diff --git a/.githooks/pre-commit b/.githooks/pre-commit index 41bac78f..799f30fe 100755 --- a/.githooks/pre-commit +++ b/.githooks/pre-commit @@ -6,14 +6,32 @@ cd "$ROOT_DIR" echo "Running pre-commit checks..." -python -m compileall -q tradingagents - +# Run black formatter (auto-fix) if python - <<'PY' import importlib.util -raise SystemExit(0 if importlib.util.find_spec("pytest") else 1) +raise SystemExit(0 if importlib.util.find_spec("black") else 1) PY then - python -m pytest -q + echo "🎨 Running black formatter..." + python -m black tradingagents/ cli/ scripts/ --quiet else - echo "pytest not installed; skipping test run." + echo "⚠️ black not installed; skipping formatting." fi + +# Run ruff linter (auto-fix, but don't fail on warnings) +if python - <<'PY' +import importlib.util +raise SystemExit(0 if importlib.util.find_spec("ruff") else 1) +PY +then + echo "🔍 Running ruff linter..." + python -m ruff check tradingagents/ cli/ scripts/ --fix --exit-zero +else + echo "⚠️ ruff not installed; skipping linting." +fi + +# CRITICAL: Check for syntax errors (this will fail the commit) +echo "🐍 Checking for syntax errors..." +python -m compileall -q tradingagents cli scripts + +echo "✅ Pre-commit checks passed!" diff --git a/cli/main.py b/cli/main.py index 4141b6a4..a586d21f 100644 --- a/cli/main.py +++ b/cli/main.py @@ -1,34 +1,31 @@ -from typing import Optional import datetime -import typer -from pathlib import Path from functools import wraps -from rich.console import Console +from pathlib import Path + +import typer from dotenv import load_dotenv +from rich.console import Console, Group # Load environment variables from .env file load_dotenv() -from rich.panel import Panel -from rich.spinner import Spinner -from rich.live import Live -from rich.columns import Columns -from rich.markdown import Markdown -from rich.layout import Layout -from rich.text import Text -from rich.live import Live -from rich.table import Table from collections import deque -import time -from rich.tree import Tree + from rich import box from rich.align import Align -from rich.rule import Rule +from rich.columns import Columns +from rich.layout import Layout +from rich.live import Live +from rich.markdown import Markdown +from rich.panel import Panel +from rich.spinner import Spinner +from rich.table import Table +from rich.text import Text -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.graph.discovery_graph import DiscoveryGraph -from tradingagents.default_config import DEFAULT_CONFIG from cli.models import AnalystType from cli.utils import * +from tradingagents.default_config import DEFAULT_CONFIG +from tradingagents.graph.discovery_graph import DiscoveryGraph +from tradingagents.graph.trading_graph import TradingAgentsGraph console = Console() @@ -54,11 +51,11 @@ def extract_text_from_content(content): elif isinstance(content, list): text_parts = [] for block in content: - if isinstance(block, dict) and 'text' in block: - text_parts.append(block['text']) + if isinstance(block, dict) and "text" in block: + text_parts.append(block["text"]) elif isinstance(block, str): text_parts.append(block) - return '\n'.join(text_parts) + return "\n".join(text_parts) else: return str(content) @@ -128,7 +125,7 @@ class MessageBuffer: if content is not None: latest_section = section latest_content = content - + if latest_section and latest_content: # Format the current section for display section_titles = { @@ -140,9 +137,7 @@ class MessageBuffer: "trader_investment_plan": "Trading Team Plan", "final_trade_decision": "Final Trade Decision", } - self.current_report = ( - f"### {section_titles[latest_section]}\n{latest_content}" - ) + self.current_report = f"### {section_titles[latest_section]}\n{latest_content}" # Update the final complete report self._update_final_report() @@ -162,17 +157,13 @@ class MessageBuffer: ): report_parts.append("## Analyst Team Reports") if self.report_sections["market_report"]: - report_parts.append( - f"### Market Analysis\n{self.report_sections['market_report']}" - ) + report_parts.append(f"### Market Analysis\n{self.report_sections['market_report']}") if self.report_sections["sentiment_report"]: report_parts.append( f"### Social Sentiment\n{self.report_sections['sentiment_report']}" ) if self.report_sections["news_report"]: - report_parts.append( - f"### News Analysis\n{self.report_sections['news_report']}" - ) + report_parts.append(f"### News Analysis\n{self.report_sections['news_report']}") if self.report_sections["fundamentals_report"]: report_parts.append( f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}" @@ -206,12 +197,8 @@ def create_layout(): Layout(name="main"), Layout(name="footer", size=3), ) - layout["main"].split_column( - Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5) - ) - layout["upper"].split_row( - Layout(name="progress", ratio=2), Layout(name="messages", ratio=3) - ) + layout["main"].split_column(Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5)) + layout["upper"].split_row(Layout(name="progress", ratio=2), Layout(name="messages", ratio=3)) return layout @@ -261,9 +248,7 @@ def update_display(layout, spinner_text=None): first_agent = agents[0] status = message_buffer.agent_status[first_agent] if status == "in_progress": - spinner = Spinner( - "dots", text="[blue]in_progress[/blue]", style="bold cyan" - ) + spinner = Spinner("dots", text="[blue]in_progress[/blue]", style="bold cyan") status_cell = spinner else: status_color = { @@ -278,9 +263,7 @@ def update_display(layout, spinner_text=None): for agent in agents[1:]: status = message_buffer.agent_status[agent] if status == "in_progress": - spinner = Spinner( - "dots", text="[blue]in_progress[/blue]", style="bold cyan" - ) + spinner = Spinner("dots", text="[blue]in_progress[/blue]", style="bold cyan") status_cell = spinner else: status_color = { @@ -333,16 +316,16 @@ def update_display(layout, spinner_text=None): text_parts = [] for item in content: if isinstance(item, dict): - if item.get('type') == 'text': - text_parts.append(item.get('text', '')) - elif item.get('type') == 'tool_use': + if item.get("type") == "text": + text_parts.append(item.get("text", "")) + elif item.get("type") == "tool_use": text_parts.append(f"[Tool: {item.get('name', 'unknown')}]") else: text_parts.append(str(item)) - content_str = ' '.join(text_parts) + content_str = " ".join(text_parts) elif not isinstance(content_str, str): content_str = str(content) - + # Truncate message content if too long if len(content_str) > 200: content_str = content_str[:197] + "..." @@ -431,9 +414,7 @@ def get_user_selections(): welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n" welcome_content += "[bold]Workflow Steps:[/bold]\n" welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Final Decision\n\n" - welcome_content += ( - "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]" - ) + welcome_content += "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]" # Create and center the welcome box welcome_box = Panel( @@ -455,13 +436,9 @@ def get_user_selections(): return Panel(box_content, border_style="blue", padding=(1, 2)) # Step 1: Select mode (Discovery or Trading) - console.print( - create_question_box( - "Step 1: Mode Selection", "Select which agent to run" - ) - ) + console.print(create_question_box("Step 1: Mode Selection", "Select which agent to run")) mode = select_mode() - + # Step 2: Ticker symbol (only for Trading mode) selected_ticker = None if mode == "trading": @@ -501,9 +478,7 @@ def get_user_selections(): # Step 5: Research depth console.print( - create_question_box( - "Step 5: Research Depth", "Select your research depth level" - ) + create_question_box("Step 5: Research Depth", "Select your research depth level") ) selected_research_depth = select_research_depth() step_offset = 5 @@ -517,7 +492,7 @@ def get_user_selections(): ) ) selected_llm_provider, backend_url = select_llm_provider() - + # Thinking agents console.print( create_question_box( @@ -548,9 +523,7 @@ def get_ticker(): def get_analysis_date(): """Get the analysis date from user input.""" while True: - date_str = typer.prompt( - "", default=datetime.datetime.now().strftime("%Y-%m-%d") - ) + date_str = typer.prompt("", default=datetime.datetime.now().strftime("%Y-%m-%d")) try: # Validate date format and ensure it's not in the future analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d") @@ -559,16 +532,14 @@ def get_analysis_date(): continue return date_str except ValueError: - console.print( - "[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]" - ) + console.print("[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]") def select_mode(): """Select between Discovery and Trading mode.""" console.print("[1] Discovery - Find investment opportunities") console.print("[2] Trading - Analyze a specific ticker") - + while True: choice = typer.prompt("Select mode", default="2") if choice in ["1", "2"]: @@ -772,9 +743,10 @@ def update_research_team_status(status): for agent in research_team: message_buffer.update_agent_status(agent, status) + def extract_text_from_content(content): """Extract text string from content that may be a string or list of dicts. - + Handles both: - Plain strings - Lists of dicts with 'type': 'text' and 'text': '...' @@ -784,12 +756,13 @@ def extract_text_from_content(content): elif isinstance(content, list): text_parts = [] for item in content: - if isinstance(item, dict) and item.get('type') == 'text': - text_parts.append(item.get('text', '')) - return '\n'.join(text_parts) if text_parts else str(content) + if isinstance(item, dict) and item.get("type") == "text": + text_parts.append(item.get("text", "")) + return "\n".join(text_parts) if text_parts else str(content) else: return str(content) + def extract_content_string(content): """Extract string content from various message formats.""" if isinstance(content, str): @@ -799,16 +772,37 @@ def extract_content_string(content): text_parts = [] for item in content: if isinstance(item, dict): - if item.get('type') == 'text': - text_parts.append(item.get('text', '')) - elif item.get('type') == 'tool_use': + if item.get("type") == "text": + text_parts.append(item.get("text", "")) + elif item.get("type") == "tool_use": text_parts.append(f"[Tool: {item.get('name', 'unknown')}]") else: text_parts.append(str(item)) - return ' '.join(text_parts) + return " ".join(text_parts) else: return str(content) + +def format_movement_stats(movement: dict) -> str: + """Format movement stats for display in discovery ranking panels.""" + if not movement: + return "" + + def fmt(value): + if value is None: + return "N/A" + return f"{value:+.2f}%" + + return ( + "**Movement:** " + f"1D {fmt(movement.get('1d'))} | " + f"7D {fmt(movement.get('7d'))} | " + f"1M {fmt(movement.get('1m'))} | " + f"6M {fmt(movement.get('6m'))} | " + f"1Y {fmt(movement.get('1y'))}" + ) + + def run_analysis(): # First get all user selections selections = get_user_selections() @@ -822,105 +816,211 @@ def run_analysis(): def run_discovery_analysis(selections): """Run discovery mode to find investment opportunities.""" - from tradingagents.dataflows.config import set_config import json - import re - + + from tradingagents.dataflows.config import set_config + # Create config config = DEFAULT_CONFIG.copy() config["quick_think_llm"] = selections["shallow_thinker"] config["deep_think_llm"] = selections["deep_thinker"] config["backend_url"] = selections["backend_url"] config["llm_provider"] = selections["llm_provider"].lower() - + # Set config globally for route_to_vendor set_config(config) - - + # Generate run timestamp import datetime + run_timestamp = datetime.datetime.now().strftime("%H_%M_%S") - + # Create results directory with run timestamp - results_dir = Path(config["results_dir"]) / "discovery" / selections["analysis_date"] / f"run_{run_timestamp}" + results_dir = ( + Path(config["results_dir"]) + / "discovery" + / selections["analysis_date"] + / f"run_{run_timestamp}" + ) results_dir.mkdir(parents=True, exist_ok=True) - + # Add results dir to config so graph can use it for logging config["discovery_run_dir"] = str(results_dir) - - console.print(f"[dim]Using {config['llm_provider'].upper()} - Shallow: {config['quick_think_llm']}, Deep: {config['deep_think_llm']}[/dim]") - + + console.print( + f"[dim]Using {config['llm_provider'].upper()} - Shallow: {config['quick_think_llm']}, Deep: {config['deep_think_llm']}[/dim]" + ) + # Initialize Discovery Graph (LLMs initialized internally like TradingAgentsGraph) discovery_graph = DiscoveryGraph(config=config) - - console.print(f"\n[bold green]Running Discovery Analysis for {selections['analysis_date']}[/bold green]\n") - - # Run discovery - result = discovery_graph.graph.invoke({ - "trade_date": selections["analysis_date"], - "tickers": [], - "filtered_tickers": [], - "opportunities": [], - "tool_logs": [], - "status": "start" - }) - - # Save discovery results - final_ranking = result.get("final_ranking", "No ranking available") - final_ranking_text = extract_text_from_content(final_ranking) - # Save as markdown - with open(results_dir / "discovery_results.md", "w") as f: - f.write(f"# Discovery Analysis - {selections['analysis_date']}\n\n") - f.write(f"**LLM Provider**: {config['llm_provider'].upper()}\n") - f.write(f"**Models**: Shallow={config['quick_think_llm']}, Deep={config['deep_think_llm']}\n\n") - f.write("## Top Investment Opportunities\n\n") - f.write(final_ranking_text) - - # Save raw result as JSON - with open(results_dir / "discovery_result.json", "w") as f: - json.dump({ - "trade_date": selections["analysis_date"], - "config": { - "llm_provider": config["llm_provider"], - "shallow_llm": config["quick_think_llm"], - "deep_llm": config["deep_think_llm"] - }, - "opportunities": result.get("opportunities", []), - "final_ranking": final_ranking_text - }, f, indent=2) - + console.print( + f"\n[bold green]Running Discovery Analysis for {selections['analysis_date']}[/bold green]\n" + ) + + # Run discovery (uses run() method which saves results) + result = discovery_graph.run(trade_date=selections["analysis_date"]) + + # Get final ranking for display (results saved by discovery_graph.run()) + final_ranking = result.get("final_ranking", "No ranking available") + + rankings_list = [] + # Format rankings for console display + try: + if isinstance(final_ranking, str): + rankings = json.loads(final_ranking) + else: + rankings = final_ranking + + # Handle dict with 'rankings' key + if isinstance(rankings, dict): + rankings = rankings.get("rankings", []) + rankings_list = rankings + + # Build nicely formatted markdown + formatted_output = [] + for rank in rankings: + ticker = rank.get("ticker", "UNKNOWN") + company_name = rank.get("company_name", ticker) + current_price = rank.get("current_price") + description = rank.get("description", "") + strategy = rank.get("strategy_match", "N/A") + final_score = rank.get("final_score", 0) + confidence = rank.get("confidence", 0) + reason = rank.get("reason", "") + rank_num = rank.get("rank", "?") + + price_str = f"${current_price:.2f}" if current_price else "N/A" + + formatted_output.append(f"### #{rank_num}: {ticker} - {company_name}") + formatted_output.append("") + formatted_output.append( + f"**Price:** {price_str} | **Strategy:** {strategy} | **Score:** {final_score} | **Confidence:** {confidence}/10" + ) + formatted_output.append("") + if description: + formatted_output.append(f"*{description}*") + formatted_output.append("") + formatted_output.append("**Investment Thesis:**") + formatted_output.append(f"{reason}") + formatted_output.append("") + formatted_output.append("---") + formatted_output.append("") + + final_ranking_text = "\n".join(formatted_output) + except Exception: + # Fallback to raw text + final_ranking_text = extract_text_from_content(final_ranking) + console.print(f"\n[dim]Results saved to: {results_dir}[/dim]\n") # Display results - console.print(Panel( - Markdown(final_ranking_text), - title="Top Investment Opportunities", - border_style="green" - )) + if getattr(discovery_graph, "console_price_charts", False) and rankings_list: + window_order = [ + str(window).strip().lower() + for window in getattr(discovery_graph, "price_chart_windows", ["1m"]) + ] + original_chart_width = getattr(discovery_graph, "price_chart_width", 60) + try: + # Fit multiple window charts side-by-side when possible. + if window_order: + target_width = max(24, (console.size.width - 12) // max(1, len(window_order))) + discovery_graph.price_chart_width = min(original_chart_width, target_width) + bundle_map = discovery_graph.build_price_chart_bundle(rankings_list) + finally: + discovery_graph.price_chart_width = original_chart_width + for rank in rankings_list: + ticker = (rank.get("ticker") or "UNKNOWN").upper() + company_name = rank.get("company_name", ticker) + current_price = rank.get("current_price") + description = rank.get("description", "") + strategy = rank.get("strategy_match", "N/A") + final_score = rank.get("final_score", 0) + confidence = rank.get("confidence", 0) + reason = rank.get("reason", "") + rank_num = rank.get("rank", "?") + + price_str = f"${current_price:.2f}" if current_price else "N/A" + ticker_bundle = bundle_map.get(ticker, {}) + movement = ticker_bundle.get("movement", {}) + movement_line = ( + format_movement_stats(movement) + if getattr(discovery_graph, "price_chart_show_movement_stats", True) + else "" + ) + + lines = [ + f"**Price:** {price_str} | **Strategy:** {strategy} | **Score:** {final_score} | **Confidence:** {confidence}/10", + ] + if movement_line: + lines.append(movement_line) + if description: + lines.append(f"*{description}*") + lines.append("**Investment Thesis:**") + lines.append(reason) + per_rank_md = "\n\n".join(lines) + + renderables = [Markdown(per_rank_md)] + charts = ticker_bundle.get("charts", {}) + if charts: + chart_columns = [] + for key in window_order: + chart = charts.get(key) + if chart: + chart_columns.append(Text.from_ansi(chart)) + if chart_columns: + renderables.append(Columns(chart_columns, equal=True, expand=True)) + else: + chart = ticker_bundle.get("chart") + if chart: + renderables.append(Text.from_ansi(chart)) + + console.print( + Panel( + Group(*renderables), + title=f"#{rank_num}: {ticker} - {company_name}", + border_style="green", + ) + ) + else: + console.print( + Panel( + ( + Markdown(final_ranking_text) + if final_ranking_text + else "[yellow]No recommendations generated[/yellow]" + ), + title="Top Investment Opportunities", + border_style="green", + ) + ) # Extract tickers from the ranking using the discovery graph's LLM - discovered_tickers = extract_tickers_from_ranking(final_ranking_text, discovery_graph.quick_thinking_llm) - + discovered_tickers = extract_tickers_from_ranking( + final_ranking_text, discovery_graph.quick_thinking_llm + ) + # Loop: Ask if they want to analyze any of the discovered tickers while True: if not discovered_tickers: console.print("\n[yellow]No tickers found in discovery results[/yellow]") break - + console.print(f"\n[bold]Discovered tickers:[/bold] {', '.join(discovered_tickers)}") - - run_trading = typer.confirm("\nWould you like to run trading analysis on one of these tickers?", default=False) - + + run_trading = typer.confirm( + "\nWould you like to run trading analysis on one of these tickers?", default=False + ) + if not run_trading: console.print("\n[green]Discovery complete! Exiting...[/green]") break - + # Let user select a ticker - console.print(f"\n[bold]Select a ticker to analyze:[/bold]") + console.print("\n[bold]Select a ticker to analyze:[/bold]") for i, ticker in enumerate(discovered_tickers, 1): console.print(f"[{i}] {ticker}") - + while True: choice = typer.prompt("Enter number", default="1") try: @@ -931,31 +1031,31 @@ def run_discovery_analysis(selections): console.print("[red]Invalid choice. Try again.[/red]") except ValueError: console.print("[red]Invalid number. Try again.[/red]") - + console.print(f"\n[green]Selected: {selected_ticker}[/green]\n") - + # Update selections with the selected ticker trading_selections = selections.copy() trading_selections["ticker"] = selected_ticker trading_selections["mode"] = "trading" - + # If analysts weren't selected (discovery mode), select default if not trading_selections.get("analysts"): trading_selections["analysts"] = [ AnalystType("market"), - AnalystType("social"), + AnalystType("social"), AnalystType("news"), - AnalystType("fundamentals") + AnalystType("fundamentals"), ] - + # If research depth wasn't selected, use default if not trading_selections.get("research_depth"): trading_selections["research_depth"] = 1 - + # Run trading analysis run_trading_analysis(trading_selections) - - console.print("\n" + "="*70 + "\n") + + console.print("\n" + "=" * 70 + "\n") def extract_tickers_from_ranking(ranking_text, llm=None): @@ -970,19 +1070,20 @@ def extract_tickers_from_ranking(ranking_text, llm=None): """ import json import re + from langchain_core.messages import HumanMessage # Try to extract from JSON first (fast path) try: # Look for JSON array in the text - json_match = re.search(r'\[[\s\S]*\]', ranking_text) + json_match = re.search(r"\[[\s\S]*\]", ranking_text) if json_match: data = json.loads(json_match.group()) if isinstance(data, list): tickers = [item.get("ticker", "").upper() for item in data if item.get("ticker")] if tickers: return tickers - except: + except Exception: pass # Use LLM to extract tickers if available @@ -1010,7 +1111,7 @@ Tickers:""" tickers = [t.strip().upper() for t in response_text.split(",") if t.strip()] # Basic validation: 1-5 uppercase letters - valid_tickers = [t for t in tickers if re.match(r'^[A-Z]{1,5}$', t)] + valid_tickers = [t for t in tickers if re.match(r"^[A-Z]{1,5}$", t)] # Remove duplicates while preserving order seen = set() @@ -1023,11 +1124,34 @@ Tickers:""" return unique_tickers[:10] # Limit to first 10 except Exception as e: - console.print(f"[yellow]Warning: LLM ticker extraction failed ({e}), using regex fallback[/yellow]") + console.print( + f"[yellow]Warning: LLM ticker extraction failed ({e}), using regex fallback[/yellow]" + ) # Regex fallback (used when no LLM provided or LLM extraction fails) - tickers = re.findall(r'\b[A-Z]{1,5}\b', ranking_text) - exclude = {'THE', 'AND', 'OR', 'FOR', 'NOT', 'BUT', 'TOP', 'USD', 'USA', 'AI', 'IT', 'IS', 'AS', 'AT', 'IN', 'ON', 'TO', 'BY', 'RMB', 'BTC'} + tickers = re.findall(r"\b[A-Z]{1,5}\b", ranking_text) + exclude = { + "THE", + "AND", + "OR", + "FOR", + "NOT", + "BUT", + "TOP", + "USD", + "USA", + "AI", + "IT", + "IS", + "AS", + "AT", + "IN", + "ON", + "TO", + "BY", + "RMB", + "BTC", + } tickers = [t for t in tickers if t not in exclude] seen = set() unique_tickers = [] @@ -1055,7 +1179,9 @@ def run_trading_analysis(selections): ) # Create result directory - results_dir = Path(config["results_dir"]) / "trading" / selections["analysis_date"] / selections["ticker"] + results_dir = ( + Path(config["results_dir"]) / "trading" / selections["analysis_date"] / selections["ticker"] + ) results_dir.mkdir(parents=True, exist_ok=True) report_dir = results_dir / "reports" report_dir.mkdir(parents=True, exist_ok=True) @@ -1067,11 +1193,16 @@ def run_trading_analysis(selections): # we must reset any previously wrapped methods; otherwise decorators stack and later runs # write logs/reports into earlier tickers' folders. message_buffer.add_message = MessageBuffer.add_message.__get__(message_buffer, MessageBuffer) - message_buffer.add_tool_call = MessageBuffer.add_tool_call.__get__(message_buffer, MessageBuffer) - message_buffer.update_report_section = MessageBuffer.update_report_section.__get__(message_buffer, MessageBuffer) + message_buffer.add_tool_call = MessageBuffer.add_tool_call.__get__( + message_buffer, MessageBuffer + ) + message_buffer.update_report_section = MessageBuffer.update_report_section.__get__( + message_buffer, MessageBuffer + ) def save_message_decorator(obj, func_name): func = getattr(obj, func_name) + @wraps(func) def wrapper(*args, **kwargs): func(*args, **kwargs) @@ -1079,10 +1210,12 @@ def run_trading_analysis(selections): content = content.replace("\n", " ") # Replace newlines with spaces with open(log_file, "a") as f: f.write(f"{timestamp} [{message_type}] {content}\n") + return wrapper - + def save_tool_call_decorator(obj, func_name): func = getattr(obj, func_name) + @wraps(func) def wrapper(*args, **kwargs): func(*args, **kwargs) @@ -1090,14 +1223,19 @@ def run_trading_analysis(selections): args_str = ", ".join(f"{k}={v}" for k, v in args.items()) with open(log_file, "a") as f: f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n") + return wrapper def save_report_section_decorator(obj, func_name): func = getattr(obj, func_name) + @wraps(func) def wrapper(section_name, content): func(section_name, content) - if section_name in obj.report_sections and obj.report_sections[section_name] is not None: + if ( + section_name in obj.report_sections + and obj.report_sections[section_name] is not None + ): content = obj.report_sections[section_name] if content: file_name = f"{section_name}.md" @@ -1105,11 +1243,14 @@ def run_trading_analysis(selections): # Extract text from LangChain content blocks content_text = extract_text_from_content(content) f.write(content_text) + return wrapper message_buffer.add_message = save_message_decorator(message_buffer, "add_message") message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, "add_tool_call") - message_buffer.update_report_section = save_report_section_decorator(message_buffer, "update_report_section") + message_buffer.update_report_section = save_report_section_decorator( + message_buffer, "update_report_section" + ) # Reset UI buffers for a clean per-ticker run message_buffer.messages.clear() @@ -1124,9 +1265,7 @@ def run_trading_analysis(selections): # Add initial messages message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}") - message_buffer.add_message( - "System", f"Analysis date: {selections['analysis_date']}" - ) + message_buffer.add_message("System", f"Analysis date: {selections['analysis_date']}") message_buffer.add_message( "System", f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}", @@ -1149,9 +1288,7 @@ def run_trading_analysis(selections): update_display(layout) # Create spinner text - spinner_text = ( - f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." - ) + spinner_text = f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." update_display(layout, spinner_text) # Initialize state and get graph args @@ -1169,38 +1306,34 @@ def run_trading_analysis(selections): # Extract message content and type if hasattr(last_message, "content"): - content = extract_content_string(last_message.content) # Use the helper function + content = extract_content_string( + last_message.content + ) # Use the helper function msg_type = "Reasoning" else: content = str(last_message) msg_type = "System" # Add message to buffer - message_buffer.add_message(msg_type, content) + message_buffer.add_message(msg_type, content) # If it's a tool call, add it to tool calls if hasattr(last_message, "tool_calls"): for tool_call in last_message.tool_calls: # Handle both dictionary and object tool calls if isinstance(tool_call, dict): - message_buffer.add_tool_call( - tool_call["name"], tool_call["args"] - ) + message_buffer.add_tool_call(tool_call["name"], tool_call["args"]) else: message_buffer.add_tool_call(tool_call.name, tool_call.args) # Update reports and agent status based on chunk content # Analyst Team Reports if "market_report" in chunk and chunk["market_report"]: - message_buffer.update_report_section( - "market_report", chunk["market_report"] - ) + message_buffer.update_report_section("market_report", chunk["market_report"]) message_buffer.update_agent_status("Market Analyst", "completed") # Set next analyst to in_progress if "social" in selections["analysts"]: - message_buffer.update_agent_status( - "Social Analyst", "in_progress" - ) + message_buffer.update_agent_status("Social Analyst", "in_progress") if "sentiment_report" in chunk and chunk["sentiment_report"]: message_buffer.update_report_section( @@ -1209,36 +1342,25 @@ def run_trading_analysis(selections): message_buffer.update_agent_status("Social Analyst", "completed") # Set next analyst to in_progress if "news" in selections["analysts"]: - message_buffer.update_agent_status( - "News Analyst", "in_progress" - ) + message_buffer.update_agent_status("News Analyst", "in_progress") if "news_report" in chunk and chunk["news_report"]: - message_buffer.update_report_section( - "news_report", chunk["news_report"] - ) + message_buffer.update_report_section("news_report", chunk["news_report"]) message_buffer.update_agent_status("News Analyst", "completed") # Set next analyst to in_progress if "fundamentals" in selections["analysts"]: - message_buffer.update_agent_status( - "Fundamentals Analyst", "in_progress" - ) + message_buffer.update_agent_status("Fundamentals Analyst", "in_progress") if "fundamentals_report" in chunk and chunk["fundamentals_report"]: message_buffer.update_report_section( "fundamentals_report", chunk["fundamentals_report"] ) - message_buffer.update_agent_status( - "Fundamentals Analyst", "completed" - ) + message_buffer.update_agent_status("Fundamentals Analyst", "completed") # Set all research team members to in_progress update_research_team_status("in_progress") # Research Team - Handle Investment Debate State - if ( - "investment_debate_state" in chunk - and chunk["investment_debate_state"] - ): + if "investment_debate_state" in chunk and chunk["investment_debate_state"]: debate_state = chunk["investment_debate_state"] # Update Bull Researcher status and report @@ -1272,10 +1394,7 @@ def run_trading_analysis(selections): ) # Update Research Manager status and final decision - if ( - "judge_decision" in debate_state - and debate_state["judge_decision"] - ): + if "judge_decision" in debate_state and debate_state["judge_decision"]: # Keep all research team members in progress until final decision update_research_team_status("in_progress") message_buffer.add_message( @@ -1290,15 +1409,10 @@ def run_trading_analysis(selections): # Mark all research team members as completed update_research_team_status("completed") # Set first risk analyst to in_progress - message_buffer.update_agent_status( - "Risky Analyst", "in_progress" - ) + message_buffer.update_agent_status("Risky Analyst", "in_progress") # Trading Team - if ( - "trader_investment_plan" in chunk - and chunk["trader_investment_plan"] - ): + if "trader_investment_plan" in chunk and chunk["trader_investment_plan"]: message_buffer.update_report_section( "trader_investment_plan", chunk["trader_investment_plan"] ) @@ -1314,9 +1428,7 @@ def run_trading_analysis(selections): "current_risky_response" in risk_state and risk_state["current_risky_response"] ): - message_buffer.update_agent_status( - "Risky Analyst", "in_progress" - ) + message_buffer.update_agent_status("Risky Analyst", "in_progress") message_buffer.add_message( "Reasoning", f"Risky Analyst: {risk_state['current_risky_response']}", @@ -1332,9 +1444,7 @@ def run_trading_analysis(selections): "current_safe_response" in risk_state and risk_state["current_safe_response"] ): - message_buffer.update_agent_status( - "Safe Analyst", "in_progress" - ) + message_buffer.update_agent_status("Safe Analyst", "in_progress") message_buffer.add_message( "Reasoning", f"Safe Analyst: {risk_state['current_safe_response']}", @@ -1350,9 +1460,7 @@ def run_trading_analysis(selections): "current_neutral_response" in risk_state and risk_state["current_neutral_response"] ): - message_buffer.update_agent_status( - "Neutral Analyst", "in_progress" - ) + message_buffer.update_agent_status("Neutral Analyst", "in_progress") message_buffer.add_message( "Reasoning", f"Neutral Analyst: {risk_state['current_neutral_response']}", @@ -1365,9 +1473,7 @@ def run_trading_analysis(selections): # Update Portfolio Manager status and final decision if "judge_decision" in risk_state and risk_state["judge_decision"]: - message_buffer.update_agent_status( - "Portfolio Manager", "in_progress" - ) + message_buffer.update_agent_status("Portfolio Manager", "in_progress") message_buffer.add_message( "Reasoning", f"Portfolio Manager: {risk_state['judge_decision']}", @@ -1380,12 +1486,8 @@ def run_trading_analysis(selections): # Mark risk analysts as completed message_buffer.update_agent_status("Risky Analyst", "completed") message_buffer.update_agent_status("Safe Analyst", "completed") - message_buffer.update_agent_status( - "Neutral Analyst", "completed" - ) - message_buffer.update_agent_status( - "Portfolio Manager", "completed" - ) + message_buffer.update_agent_status("Neutral Analyst", "completed") + message_buffer.update_agent_status("Portfolio Manager", "completed") # Update the display update_display(layout) @@ -1418,55 +1520,42 @@ def run_trading_analysis(selections): @app.command() def build_memories( start_date: str = typer.Option( - "2023-01-01", - "--start-date", - "-s", - help="Start date for scanning high movers (YYYY-MM-DD)" + "2023-01-01", "--start-date", "-s", help="Start date for scanning high movers (YYYY-MM-DD)" ), end_date: str = typer.Option( - "2024-12-01", - "--end-date", - "-e", - help="End date for scanning high movers (YYYY-MM-DD)" + "2024-12-01", "--end-date", "-e", help="End date for scanning high movers (YYYY-MM-DD)" ), tickers: str = typer.Option( None, "--tickers", "-t", - help="Comma-separated list of tickers to scan (overrides --use-alpha-vantage)" + help="Comma-separated list of tickers to scan (overrides --use-alpha-vantage)", ), use_alpha_vantage: bool = typer.Option( False, "--use-alpha-vantage", "-a", - help="Use Alpha Vantage top gainers/losers to get ticker list" + help="Use Alpha Vantage top gainers/losers to get ticker list", ), av_limit: int = typer.Option( 20, "--av-limit", - help="Number of tickers to get from each Alpha Vantage category (gainers/losers)" + help="Number of tickers to get from each Alpha Vantage category (gainers/losers)", ), min_move_pct: float = typer.Option( - 15.0, - "--min-move", - "-m", - help="Minimum percentage move to qualify as high mover" + 15.0, "--min-move", "-m", help="Minimum percentage move to qualify as high mover" ), analysis_windows: str = typer.Option( "7,30", "--windows", "-w", - help="Comma-separated list of days before move to analyze (e.g., '7,30')" + help="Comma-separated list of days before move to analyze (e.g., '7,30')", ), max_samples: int = typer.Option( - 20, - "--max-samples", - help="Maximum number of high movers to analyze (reduces runtime)" + 20, "--max-samples", help="Maximum number of high movers to analyze (reduces runtime)" ), sample_strategy: str = typer.Option( - "diverse", - "--strategy", - help="Sampling strategy: diverse, largest, recent, or random" + "diverse", "--strategy", help="Sampling strategy: diverse, largest, recent, or random" ), ): """ @@ -1488,20 +1577,27 @@ def build_memories( # Customize date range and parameters python cli/main.py build-memories --use-alpha-vantage --start-date 2023-01-01 --min-move 20.0 """ - console.print("\n[bold cyan]═══════════════════════════════════════════════════════[/bold cyan]") + console.print( + "\n[bold cyan]═══════════════════════════════════════════════════════[/bold cyan]" + ) console.print("[bold cyan] TRADINGAGENTS MEMORY BUILDER[/bold cyan]") - console.print("[bold cyan]═══════════════════════════════════════════════════════[/bold cyan]\n") + console.print( + "[bold cyan]═══════════════════════════════════════════════════════[/bold cyan]\n" + ) # Determine ticker source if use_alpha_vantage and not tickers: console.print("[bold yellow]📡 Using Alpha Vantage to fetch top movers...[/bold yellow]") try: from tradingagents.agents.utils.historical_memory_builder import HistoricalMemoryBuilder + builder_temp = HistoricalMemoryBuilder(DEFAULT_CONFIG) ticker_list = builder_temp.get_tickers_from_alpha_vantage(limit=av_limit) if not ticker_list: - console.print("\n[bold red]❌ No tickers found from Alpha Vantage. Please check your API key or try --tickers instead.[/bold red]\n") + console.print( + "\n[bold red]❌ No tickers found from Alpha Vantage. Please check your API key or try --tickers instead.[/bold red]\n" + ) raise typer.Exit(code=1) except Exception as e: console.print(f"\n[bold red]❌ Error fetching from Alpha Vantage: {e}[/bold red]") @@ -1514,12 +1610,14 @@ def build_memories( # Default tickers if neither option specified default_tickers = "AAPL,MSFT,GOOGL,NVDA,TSLA,META,AMZN,AMD,NFLX,DIS" ticker_list = [t.strip().upper() for t in default_tickers.split(",")] - console.print(f"[bold yellow]No ticker source specified. Using default list.[/bold yellow]") - console.print(f"[dim]Tip: Use --use-alpha-vantage for dynamic ticker discovery or --tickers for custom list[/dim]") + console.print("[bold yellow]No ticker source specified. Using default list.[/bold yellow]") + console.print( + "[dim]Tip: Use --use-alpha-vantage for dynamic ticker discovery or --tickers for custom list[/dim]" + ) window_list = [int(w.strip()) for w in analysis_windows.split(",")] - console.print(f"\n[bold]Configuration:[/bold]") + console.print("\n[bold]Configuration:[/bold]") console.print(f" Ticker Source: {'Alpha Vantage' if use_alpha_vantage else 'Manual/Default'}") console.print(f" Date Range: {start_date} to {end_date}") console.print(f" Tickers: {len(ticker_list)} stocks") @@ -1544,11 +1642,13 @@ def build_memories( min_move_pct=min_move_pct, analysis_windows=window_list, max_samples=max_samples, - sample_strategy=sample_strategy + sample_strategy=sample_strategy, ) if not memories: - console.print("\n[bold yellow]⚠️ No memories created. Try adjusting parameters.[/bold yellow]\n") + console.print( + "\n[bold yellow]⚠️ No memories created. Try adjusting parameters.[/bold yellow]\n" + ) return # Display summary table @@ -1564,9 +1664,9 @@ def build_memories( stats = memory.get_statistics() table.add_row( agent_type.upper(), - str(stats['total_memories']), + str(stats["total_memories"]), f"{stats['accuracy_rate']:.1f}%", - f"{stats['avg_move_pct']:.1f}%" + f"{stats['avg_move_pct']:.1f}%", ) console.print(table) @@ -1584,16 +1684,21 @@ def build_memories( for agent_type, memory in list(memories.items())[:2]: # Test first 2 agents results = memory.get_memories(test_situation, n_matches=1) if results: - console.print(f" [cyan]{agent_type.upper()}[/cyan]: Found {len(results)} relevant memory") + console.print( + f" [cyan]{agent_type.upper()}[/cyan]: Found {len(results)} relevant memory" + ) console.print(f" Similarity: {results[0]['similarity_score']:.2f}") console.print("\n[bold green]🎉 Memory bank ready for use![/bold green]") - console.print("\n[dim]Note: These memories will be used automatically in future trading analyses when memory is enabled in config.[/dim]\n") + console.print( + "\n[dim]Note: These memories will be used automatically in future trading analyses when memory is enabled in config.[/dim]\n" + ) except Exception as e: - console.print(f"\n[bold red]❌ Error building memories:[/bold red]") + console.print("\n[bold red]❌ Error building memories:[/bold red]") console.print(f"[red]{str(e)}[/red]\n") import traceback + console.print(f"[dim]{traceback.format_exc()}[/dim]") raise typer.Exit(code=1) diff --git a/cli/models.py b/cli/models.py index f68c3da1..83922d7a 100644 --- a/cli/models.py +++ b/cli/models.py @@ -1,6 +1,4 @@ from enum import Enum -from typing import List, Optional, Dict -from pydantic import BaseModel class AnalystType(str, Enum): diff --git a/cli/utils.py b/cli/utils.py index acf6f1b6..9488837e 100644 --- a/cli/utils.py +++ b/cli/utils.py @@ -1,7 +1,11 @@ +from typing import List + import questionary -from typing import List, Optional, Tuple, Dict from cli.models import AnalystType +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) ANALYST_ORDER = [ ("Market Analyst", AnalystType.MARKET), @@ -68,9 +72,7 @@ def select_analysts() -> List[AnalystType]: """Select analysts using an interactive checkbox.""" choices = questionary.checkbox( "Select Your [Analysts Team]:", - choices=[ - questionary.Choice(display, value=value) for display, value in ANALYST_ORDER - ], + choices=[questionary.Choice(display, value=value) for display, value in ANALYST_ORDER], instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done", validate=lambda x: len(x) > 0 or "You must select at least one analyst.", style=questionary.Style( @@ -102,9 +104,7 @@ def select_research_depth() -> int: choice = questionary.select( "Select Your [Research Depth]:", - choices=[ - questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS - ], + choices=[questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS], instruction="\n- Use arrow keys to navigate\n- Press Enter to select", style=questionary.Style( [ @@ -135,28 +135,44 @@ def select_shallow_thinking_agent(provider) -> str: ("GPT-4o - Standard model with solid capabilities", "gpt-4o"), ], "anthropic": [ - ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), + ( + "Claude Haiku 3.5 - Fast inference and standard capabilities", + "claude-3-5-haiku-latest", + ), ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"), - ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"), + ( + "Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", + "claude-3-7-sonnet-latest", + ), ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"), ], "google": [ ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"), - ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"), + ( + "Gemini 2.0 Flash - Next generation features, speed, and thinking", + "gemini-2.0-flash", + ), ("Gemini 2.5 Flash-Lite - Ultra-fast and cost-effective", "gemini-2.5-flash-lite"), ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash"), ("Gemini 2.5 Pro - Most capable Gemini model", "gemini-2.5-pro"), ("Gemini 3.0 Pro Preview - Next generation preview", "gemini-3-pro-preview"), + ("Gemini 3.0 Flash Preview - Latest generation preview", "gemini-3-flash-preview"), ], "openrouter": [ ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"), - ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"), - ("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"), + ( + "Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", + "meta-llama/llama-3.3-8b-instruct:free", + ), + ( + "google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", + "google/gemini-2.0-flash-exp:free", + ), ], "ollama": [ ("llama3.1 local", "llama3.1"), ("llama3.2 local", "llama3.2"), - ] + ], } choice = questionary.select( @@ -176,9 +192,7 @@ def select_shallow_thinking_agent(provider) -> str: ).ask() if choice is None: - console.print( - "\n[red]No shallow thinking llm engine selected. Exiting...[/red]" - ) + console.print("\n[red]No shallow thinking llm engine selected. Exiting...[/red]") exit(1) return choice @@ -200,30 +214,46 @@ def select_deep_thinking_agent(provider) -> str: ("o1 - Premier reasoning and problem-solving model", "o1"), ], "anthropic": [ - ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), + ( + "Claude Haiku 3.5 - Fast inference and standard capabilities", + "claude-3-5-haiku-latest", + ), ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"), - ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"), + ( + "Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", + "claude-3-7-sonnet-latest", + ), ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"), ("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"), ], "google": [ ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"), - ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"), + ( + "Gemini 2.0 Flash - Next generation features, speed, and thinking", + "gemini-2.0-flash", + ), ("Gemini 2.5 Flash-Lite - Ultra-fast and cost-effective", "gemini-2.5-flash-lite"), ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash"), ("Gemini 2.5 Pro - Most capable Gemini model", "gemini-2.5-pro"), ("Gemini 3.0 Pro Preview - Next generation preview", "gemini-3-pro-preview"), + ("Gemini 3.0 Flash Preview - Latest generation preview", "gemini-3-flash-preview"), ], "openrouter": [ - ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"), - ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"), + ( + "DeepSeek V3 - a 685B-parameter, mixture-of-experts model", + "deepseek/deepseek-chat-v3-0324:free", + ), + ( + "Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", + "deepseek/deepseek-chat-v3-0324:free", + ), ], "ollama": [ ("llama3.1 local", "llama3.1"), ("qwen3", "qwen3"), - ] + ], } - + choice = questionary.select( "Select Your [Deep-Thinking LLM Engine]:", choices=[ @@ -246,6 +276,7 @@ def select_deep_thinking_agent(provider) -> str: return choice + def select_llm_provider() -> tuple[str, str]: """Select the OpenAI api url using interactive selection.""" # Define OpenAI api options with their corresponding endpoints @@ -254,14 +285,13 @@ def select_llm_provider() -> tuple[str, str]: ("Anthropic", "https://api.anthropic.com/"), ("Google", "https://generativelanguage.googleapis.com/v1"), ("Openrouter", "https://openrouter.ai/api/v1"), - ("Ollama", "http://localhost:11434/v1"), + ("Ollama", "http://localhost:11434/v1"), ] - + choice = questionary.select( "Select your LLM Provider:", choices=[ - questionary.Choice(display, value=(display, value)) - for display, value in BASE_URLS + questionary.Choice(display, value=(display, value)) for display, value in BASE_URLS ], instruction="\n- Use arrow keys to navigate\n- Press Enter to select", style=questionary.Style( @@ -272,12 +302,12 @@ def select_llm_provider() -> tuple[str, str]: ] ), ).ask() - + if choice is None: console.print("\n[red]no OpenAI backend selected. Exiting...[/red]") exit(1) - + display_name, url = choice - print(f"You selected: {display_name}\tURL: {url}") - + logger.info(f"You selected: {display_name}\tURL: {url}") + return display_name, url diff --git a/data/tickers.txt b/data/tickers.txt index 331fcf3b..ad11032c 100644 --- a/data/tickers.txt +++ b/data/tickers.txt @@ -1,3068 +1,592 @@ -A +AA AAL -AAOI +AAP AAPL -AAXN ABBV -ABCL -ABNB ABT -ABUS ACGL -ACIW -ACLS ACN -ACRX -ADAP ADBE -ADI ADM -ADMA ADP ADSK -ADTN -ADVM AEE -AEHR -AEIS AEP AES -AEVA AFL -AFMD -AFRM -AGYS -AIG -AIMD -AIOT -AIZ -AJG +AIV AKAM -AKRO ALB ALGN -ALGT -ALHC -ALIM -ALKS +ALK ALL -ALLE -ALNY -ALTR -ALVR AMAT -AMBA -AMC -AMCR AMD AME -AMEH AMGN -AMKR -AMP -AMPH -AMRS -AMSC AMT AMZN -ANAB -ANET -ANGI -ANIP -ANSS +ANF AON AOS -AOUT APA APD APH -APLS -APLT -APOG -APP -APPF -APPN -APRN -APTV -ARAY -ARCC -ARDS ARE -ARHS -ARQQ -ARQT -ARVN -ARWR -ASML -ASND -ASTS -ATER -ATEX -ATHA -ATNF +ATKR ATO -ATOM -ATOS -ATRA -ATRC -ATVI -AUPH -AVAV AVB -AVDL AVGO -AVIR 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--git a/docs/plans/2026-02-05-modular-pipeline-architecture.md b/docs/plans/2026-02-05-modular-pipeline-architecture.md new file mode 100644 index 00000000..595e845e --- /dev/null +++ b/docs/plans/2026-02-05-modular-pipeline-architecture.md @@ -0,0 +1,1013 @@ +# Modular Multi-Pipeline Discovery Architecture - Fast Implementation + +> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. + +**Goal:** Transform discovery system into modular, multi-pipeline architecture with early signal scanners, dynamic performance tracking, and Streamlit dashboard UI. + +**Approach:** Implementation-first, skip tests/docs for fast experimentation. + +**Branch:** `feature/modular-pipeline-architecture` (no git commits during implementation) + +--- + +## Phase 1: Core Architecture (30 min) + +### Task 1: Create Scanner Registry + +**Files:** +- Create: `tradingagents/dataflows/discovery/scanner_registry.py` + +**Implementation:** + +```python +# tradingagents/dataflows/discovery/scanner_registry.py +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Type + + +class BaseScanner(ABC): + """Base class for all discovery scanners.""" + + name: str = None + pipeline: str = None + + def __init__(self, config: Dict[str, Any]): + if self.name is None: + raise ValueError(f"{self.__class__.__name__} must define 'name'") + if self.pipeline is None: + raise ValueError(f"{self.__class__.__name__} must define 'pipeline'") + + self.config = config + self.scanner_config = config.get("discovery", {}).get("scanners", {}).get(self.name, {}) + self.enabled = self.scanner_config.get("enabled", True) + self.limit = self.scanner_config.get("limit", 10) + + @abstractmethod + def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: + """Return list of candidates with: ticker, source, context, priority""" + pass + + def is_enabled(self) -> bool: + return self.enabled + + +class ScannerRegistry: + """Global scanner registry.""" + + def __init__(self): + self.scanners: Dict[str, Type[BaseScanner]] = {} + + def register(self, scanner_class: Type[BaseScanner]): + if not hasattr(scanner_class, "name") or scanner_class.name is None: + raise ValueError(f"Scanner must define 'name'") + if not hasattr(scanner_class, "pipeline") or scanner_class.pipeline is None: + raise ValueError(f"Scanner must define 'pipeline'") + self.scanners[scanner_class.name] = scanner_class + + def get_scanners_by_pipeline(self, pipeline: str) -> List[Type[BaseScanner]]: + return [sc for sc in self.scanners.values() if sc.pipeline == pipeline] + + def get_all_scanners(self) -> List[Type[BaseScanner]]: + return list(self.scanners.values()) + + +SCANNER_REGISTRY = ScannerRegistry() +``` + +--- + +### Task 2: Update Config with Modular Structure + +**Files:** +- Modify: `tradingagents/default_config.py` + +**Add to config:** + +```python +"discovery": { + # ... existing settings ... + + # PIPELINES: Define ranking behavior per pipeline + "pipelines": { + "edge": { + "enabled": True, + "priority": 1, + "ranker_prompt": "edge_signals_ranker.txt", + "deep_dive_budget": 15 + }, + "momentum": { + "enabled": True, + "priority": 2, + "ranker_prompt": "momentum_ranker.txt", + "deep_dive_budget": 10 + }, + "news": { + "enabled": True, + "priority": 3, + "ranker_prompt": "news_catalyst_ranker.txt", + "deep_dive_budget": 5 + }, + "social": { + "enabled": True, + "priority": 4, + "ranker_prompt": "social_signals_ranker.txt", + "deep_dive_budget": 5 + }, + "events": { + "enabled": False, + "priority": 5, + "deep_dive_budget": 0 + } + }, + + # SCANNERS: Each declares its pipeline + "scanners": { + # Edge signals + "insider_buying": {"enabled": True, "pipeline": "edge", "limit": 20}, + "options_flow": {"enabled": True, "pipeline": "edge", "limit": 15}, + "congress_trades": {"enabled": False, "pipeline": "edge", "limit": 10}, + + # Momentum + "volume_accumulation": {"enabled": True, "pipeline": "momentum", "limit": 15}, + "market_movers": {"enabled": True, "pipeline": "momentum", "limit": 10}, + + # News + "semantic_news": {"enabled": True, "pipeline": "news", "limit": 10}, + "analyst_upgrade": {"enabled": False, "pipeline": "news", "limit": 5}, + + # Social + "reddit_trending": {"enabled": True, "pipeline": "social", "limit": 15}, + "reddit_dd": {"enabled": True, "pipeline": "social", "limit": 10}, + + # Events + "earnings_calendar": {"enabled": False, "pipeline": "events", "limit": 10}, + "short_squeeze": {"enabled": False, "pipeline": "events", "limit": 5} + } +} +``` + +--- + +## Phase 2: New Edge Scanners (45 min) + +### Task 3: Insider Buying Scanner + +**Files:** +- Create: `tradingagents/dataflows/discovery/scanners/insider_buying.py` + +**Implementation:** + +```python +# tradingagents/dataflows/discovery/scanners/insider_buying.py +"""SEC Form 4 insider buying scanner.""" +import re +from datetime import datetime, timedelta +from typing import Any, Dict, List + +from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY + + +class InsiderBuyingScanner(BaseScanner): + """Scan SEC Form 4 for insider purchases.""" + + name = "insider_buying" + pipeline = "edge" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.lookback_days = self.scanner_config.get("lookback_days", 7) + + def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: + if not self.is_enabled(): + return [] + + print(f" 💼 Scanning insider buying (last {self.lookback_days} days)...") + + try: + # Use existing FMP API or placeholder + # For MVP: Return empty or use FMP insider trades endpoint + candidates = [] + + # TODO: Implement actual Form 4 fetching + # For now, placeholder that uses FMP API if available + + print(f" Found {len(candidates)} insider purchases") + return candidates + + except Exception as e: + print(f" Error: {e}") + return [] + + +SCANNER_REGISTRY.register(InsiderBuyingScanner) +``` + +--- + +### Task 4: Options Flow Scanner + +**Files:** +- Create: `tradingagents/dataflows/discovery/scanners/options_flow.py` + +**Implementation:** + +```python +# tradingagents/dataflows/discovery/scanners/options_flow.py +"""Unusual options activity scanner.""" +from typing import Any, Dict, List +import yfinance as yf + +from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY + + +class OptionsFlowScanner(BaseScanner): + """Scan for unusual options activity.""" + + name = "options_flow" + pipeline = "edge" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.min_volume_oi_ratio = self.scanner_config.get("min_volume_oi_ratio", 2.0) + # Focus on liquid options + self.ticker_universe = ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "AMD", "TSLA"] + + def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: + if not self.is_enabled(): + return [] + + print(f" 📊 Scanning unusual options activity...") + + candidates = [] + + for ticker in self.ticker_universe[:20]: # Limit for speed + try: + unusual = self._analyze_ticker_options(ticker) + if unusual: + candidates.append(unusual) + if len(candidates) >= self.limit: + break + except: + continue + + print(f" Found {len(candidates)} unusual options flows") + return candidates + + def _analyze_ticker_options(self, ticker: str) -> Dict[str, Any]: + try: + stock = yf.Ticker(ticker) + expirations = stock.options + if not expirations: + return None + + options = stock.option_chain(expirations[0]) + calls = options.calls + puts = options.puts + + # Find unusual strikes + unusual_strikes = [] + for _, opt in calls.iterrows(): + vol = opt.get("volume", 0) + oi = opt.get("openInterest", 0) + if oi > 0 and vol > 1000 and (vol / oi) >= self.min_volume_oi_ratio: + unusual_strikes.append({ + "type": "call", + "strike": opt["strike"], + "volume": vol, + "oi": oi + }) + + if not unusual_strikes: + return None + + # Calculate P/C ratio + total_call_vol = calls["volume"].sum() if not calls.empty else 0 + total_put_vol = puts["volume"].sum() if not puts.empty else 0 + pc_ratio = total_put_vol / total_call_vol if total_call_vol > 0 else 0 + + sentiment = "bullish" if pc_ratio < 0.7 else "bearish" if pc_ratio > 1.3 else "neutral" + + return { + "ticker": ticker, + "source": self.name, + "context": f"Unusual options: {len(unusual_strikes)} strikes, P/C={pc_ratio:.2f} ({sentiment})", + "priority": "high" if sentiment == "bullish" else "medium", + "strategy": "options_flow", + "put_call_ratio": round(pc_ratio, 2) + } + + except: + return None + + +SCANNER_REGISTRY.register(OptionsFlowScanner) +``` + +--- + +## Phase 3: Dynamic Performance Tracking (30 min) + +### Task 5: Position Tracker + +**Files:** +- Create: `tradingagents/dataflows/discovery/performance/position_tracker.py` + +**Implementation:** + +```python +# tradingagents/dataflows/discovery/performance/position_tracker.py +"""Dynamic position tracking with time-series data.""" +import json +from datetime import datetime, timedelta +from pathlib import Path +from typing import Any, Dict, List, Optional + + +class PositionTracker: + """Track positions with continuous price monitoring.""" + + def __init__(self, data_dir: str = "data"): + self.data_dir = Path(data_dir) + self.tracking_dir = self.data_dir / "recommendations" / "tracking" + self.tracking_dir.mkdir(parents=True, exist_ok=True) + + def create_position(self, recommendation: Dict[str, Any]) -> Dict[str, Any]: + """Create new position to track.""" + ticker = recommendation["ticker"] + entry_price = recommendation["entry_price"] + rec_date = recommendation.get("recommendation_date", datetime.now().isoformat()) + + return { + "ticker": ticker, + "recommendation_date": rec_date, + "entry_price": entry_price, + "pipeline": recommendation.get("pipeline", "unknown"), + "scanner": recommendation.get("scanner", "unknown"), + "strategy": recommendation.get("strategy_match", "unknown"), + "confidence": recommendation.get("confidence", 5), + "shares": recommendation.get("shares", 0), + + "price_history": [{ + "timestamp": rec_date, + "price": entry_price, + "return_pct": 0.0, + "hours_held": 0, + "days_held": 0 + }], + + "metrics": { + "peak_return": 0.0, + "current_return": 0.0, + "days_held": 0, + "status": "open" + } + } + + def update_position_price(self, position: Dict[str, Any], new_price: float, + timestamp: Optional[str] = None) -> Dict[str, Any]: + """Update position with new price.""" + if timestamp is None: + timestamp = datetime.now().isoformat() + + entry_price = position["entry_price"] + entry_time = datetime.fromisoformat(position["recommendation_date"]) + current_time = datetime.fromisoformat(timestamp) + + return_pct = ((new_price - entry_price) / entry_price) * 100.0 + time_diff = current_time - entry_time + hours_held = time_diff.total_seconds() / 3600 + days_held = time_diff.days + + position["price_history"].append({ + "timestamp": timestamp, + "price": new_price, + "return_pct": round(return_pct, 2), + "hours_held": round(hours_held, 1), + "days_held": days_held + }) + + # Update metrics + position["metrics"]["current_return"] = round(return_pct, 2) + position["metrics"]["current_price"] = new_price + position["metrics"]["days_held"] = days_held + position["metrics"]["peak_return"] = max( + position["metrics"]["peak_return"], + return_pct + ) + + return position + + def save_position(self, position: Dict[str, Any]) -> None: + """Save position to disk.""" + ticker = position["ticker"] + rec_date = position["recommendation_date"].split("T")[0] + filename = f"{ticker}_{rec_date}.json" + filepath = self.tracking_dir / filename + + with open(filepath, "w") as f: + json.dump(position, f, indent=2) + + def load_all_open_positions(self) -> List[Dict[str, Any]]: + """Load all open positions.""" + positions = [] + for filepath in self.tracking_dir.glob("*.json"): + with open(filepath, "r") as f: + position = json.load(f) + if position["metrics"]["status"] == "open": + positions.append(position) + return positions +``` + +--- + +### Task 6: Position Updater Script + +**Files:** +- Create: `scripts/update_positions.py` + +**Implementation:** + +```python +# scripts/update_positions.py +"""Update all open positions with current prices.""" +import yfinance as yf +from datetime import datetime +from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker + + +def main(): + tracker = PositionTracker() + positions = tracker.load_all_open_positions() + + if not positions: + print("No open positions") + return + + print(f"Updating {len(positions)} positions...") + + # Get unique tickers + tickers = list(set(p["ticker"] for p in positions)) + + # Fetch prices + try: + tickers_str = " ".join(tickers) + data = yf.download(tickers_str, period="1d", progress=False) + + prices = {} + if len(tickers) == 1: + prices[tickers[0]] = float(data["Close"].iloc[-1]) + else: + for ticker in tickers: + try: + prices[ticker] = float(data["Close"][ticker].iloc[-1]) + except: + pass + + # Update each position + for position in positions: + ticker = position["ticker"] + if ticker in prices: + updated = tracker.update_position_price(position, prices[ticker]) + tracker.save_position(updated) + print(f" {ticker}: ${prices[ticker]:.2f} ({updated['metrics']['current_return']:+.1f}%)") + + print(f"✅ Updated {len(positions)} positions") + + except Exception as e: + print(f"Error: {e}") + + +if __name__ == "__main__": + main() +``` + +--- + +## Phase 4: Streamlit Dashboard (60 min) + +### Task 7: Install Dependencies & Create Entry Point + +**Files:** +- Update: `requirements.txt` +- Create: `tradingagents/ui/dashboard.py` +- Create: `tradingagents/ui/utils.py` +- Create: `tradingagents/ui/pages/__init__.py` + +**Add to requirements.txt:** +``` +streamlit>=1.40.0 +plotly>=5.18.0 +``` + +**Dashboard entry point:** + +```python +# tradingagents/ui/dashboard.py +"""Trading Discovery Dashboard.""" +import streamlit as st + +st.set_page_config( + page_title="Trading Discovery", + page_icon="🎯", + layout="wide" +) + +from tradingagents.ui.pages import home, todays_picks, portfolio, performance, settings + + +def main(): + st.sidebar.title("🎯 Trading Discovery") + + page = st.sidebar.radio( + "Navigation", + ["Home", "Today's Picks", "Portfolio", "Performance", "Settings"] + ) + + # Quick stats + st.sidebar.markdown("---") + st.sidebar.markdown("### Quick Stats") + + try: + from tradingagents.ui.utils import load_quick_stats + stats = load_quick_stats() + st.sidebar.metric("Open Positions", stats.get("open_positions", 0)) + st.sidebar.metric("Win Rate", f"{stats.get('win_rate_7d', 0):.1f}%") + except: + pass + + # Render page + if page == "Home": + home.render() + elif page == "Today's Picks": + todays_picks.render() + elif page == "Portfolio": + portfolio.render() + elif page == "Performance": + performance.render() + elif page == "Settings": + settings.render() + + +if __name__ == "__main__": + main() +``` + +**Utils:** + +```python +# tradingagents/ui/utils.py +"""Dashboard utilities.""" +import json +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + + +def load_statistics() -> Dict[str, Any]: + """Load performance statistics.""" + stats_file = Path("data/recommendations/statistics.json") + if not stats_file.exists(): + return {} + with open(stats_file, "r") as f: + return json.load(f) + + +def load_recommendations(date: str = None) -> List[Dict[str, Any]]: + """Load recommendations for date.""" + if date is None: + date = datetime.now().strftime("%Y-%m-%d") + rec_file = Path(f"data/recommendations/{date}_recommendations.json") + if not rec_file.exists(): + return [] + with open(rec_file, "r") as f: + data = json.load(f) + return data.get("rankings", []) + + +def load_open_positions() -> List[Dict[str, Any]]: + """Load all open positions.""" + from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker + tracker = PositionTracker() + return tracker.load_all_open_positions() + + +def load_quick_stats() -> Dict[str, Any]: + """Load sidebar quick stats.""" + stats = load_statistics() + positions = load_open_positions() + return { + "open_positions": len(positions), + "win_rate_7d": stats.get("overall_7d", {}).get("win_rate", 0) + } +``` + +--- + +### Task 8: Home Page + +**Files:** +- Create: `tradingagents/ui/pages/home.py` + +```python +# tradingagents/ui/pages/home.py +"""Home page.""" +import streamlit as st +import plotly.express as px +import pandas as pd +from tradingagents.ui.utils import load_statistics, load_open_positions + + +def render(): + st.title("🎯 Trading Discovery Dashboard") + + stats = load_statistics() + if not stats: + st.warning("No data. Run discovery first.") + return + + # Metrics + col1, col2, col3, col4 = st.columns(4) + + overall_7d = stats.get("overall_7d", {}) + with col1: + st.metric("Win Rate (7d)", f"{overall_7d.get('win_rate', 0):.1f}%") + with col2: + st.metric("Open Positions", len(load_open_positions())) + with col3: + st.metric("Avg Return (7d)", f"{overall_7d.get('avg_return', 0):+.1f}%") + with col4: + by_pipeline = stats.get("by_pipeline", {}) + if by_pipeline: + best = max(by_pipeline.items(), key=lambda x: x[1].get("win_rate_7d", 0)) + st.metric("Best Pipeline", f"{best[0].title()} ({best[1].get('win_rate_7d', 0):.0f}%)") + + # Pipeline chart + st.subheader("📊 Pipeline Performance") + + if by_pipeline: + data = [] + for pipeline, d in by_pipeline.items(): + data.append({ + "Pipeline": pipeline.title(), + "Win Rate": d.get("win_rate_7d", 0), + "Avg Return": d.get("avg_return_7d", 0), + "Count": d.get("count", 0) + }) + + df = pd.DataFrame(data) + fig = px.scatter(df, x="Win Rate", y="Avg Return", size="Count", color="Pipeline", + title="Pipeline Performance") + fig.add_hline(y=0, line_dash="dash") + fig.add_vline(x=50, line_dash="dash") + st.plotly_chart(fig, use_container_width=True) +``` + +--- + +### Task 9: Today's Picks Page + +**Files:** +- Create: `tradingagents/ui/pages/todays_picks.py` + +```python +# tradingagents/ui/pages/todays_picks.py +"""Today's recommendations.""" +import streamlit as st +from datetime import datetime +from tradingagents.ui.utils import load_recommendations + + +def render(): + st.title("📋 Today's Recommendations") + + today = datetime.now().strftime("%Y-%m-%d") + recommendations = load_recommendations(today) + + if not recommendations: + st.warning(f"No recommendations for {today}") + return + + # Filters + col1, col2, col3 = st.columns(3) + with col1: + pipelines = list(set(r.get("pipeline", "unknown") for r in recommendations)) + pipeline_filter = st.multiselect("Pipeline", pipelines, default=pipelines) + with col2: + min_confidence = st.slider("Min Confidence", 1, 10, 7) + with col3: + min_score = st.slider("Min Score", 0, 100, 70) + + # Apply filters + filtered = [r for r in recommendations + if r.get("pipeline", "unknown") in pipeline_filter + and r.get("confidence", 0) >= min_confidence + and r.get("final_score", 0) >= min_score] + + st.write(f"**{len(filtered)}** of **{len(recommendations)}** recommendations") + + # Display recommendations + for i, rec in enumerate(filtered, 1): + ticker = rec.get("ticker", "UNKNOWN") + score = rec.get("final_score", 0) + confidence = rec.get("confidence", 0) + + with st.expander(f"#{i} {ticker} - {rec.get('company_name', '')} (Score: {score}, Conf: {confidence}/10)"): + col1, col2 = st.columns([2, 1]) + + with col1: + st.write(f"**Pipeline:** {rec.get('pipeline', 'unknown').title()}") + st.write(f"**Scanner:** {rec.get('scanner', 'unknown')}") + st.write(f"**Price:** ${rec.get('current_price', 0):.2f}") + st.write(f"**Thesis:** {rec.get('reason', 'N/A')}") + + with col2: + if st.button("✅ Enter Position", key=f"enter_{ticker}"): + st.info("Position entry modal (TODO)") + if st.button("👀 Watch", key=f"watch_{ticker}"): + st.success(f"Added {ticker} to watchlist") +``` + +--- + +### Task 10: Portfolio Page + +**Files:** +- Create: `tradingagents/ui/pages/portfolio.py` + +```python +# tradingagents/ui/pages/portfolio.py +"""Portfolio tracker.""" +import streamlit as st +import plotly.express as px +import pandas as pd +from datetime import datetime +from tradingagents.ui.utils import load_open_positions + + +def render(): + st.title("💼 Portfolio Tracker") + + # Manual add form + with st.expander("➕ Add Position"): + col1, col2, col3, col4 = st.columns(4) + with col1: + ticker = st.text_input("Ticker") + with col2: + entry_price = st.number_input("Entry Price", min_value=0.0) + with col3: + shares = st.number_input("Shares", min_value=0, step=1) + with col4: + st.write("") # Spacing + if st.button("Add"): + if ticker and entry_price > 0 and shares > 0: + from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker + tracker = PositionTracker() + pos = tracker.create_position({ + "ticker": ticker.upper(), + "entry_price": entry_price, + "shares": shares, + "recommendation_date": datetime.now().isoformat(), + "pipeline": "manual", + "scanner": "manual", + "strategy_match": "manual", + "confidence": 5 + }) + tracker.save_position(pos) + st.success(f"Added {ticker.upper()}") + st.rerun() + + # Load positions + positions = load_open_positions() + + if not positions: + st.info("No open positions") + return + + # Summary + total_invested = sum(p["entry_price"] * p.get("shares", 0) for p in positions) + total_current = sum(p["metrics"]["current_price"] * p.get("shares", 0) for p in positions) + total_pnl = total_current - total_invested + total_pnl_pct = (total_pnl / total_invested * 100) if total_invested > 0 else 0 + + col1, col2, col3, col4 = st.columns(4) + with col1: + st.metric("Invested", f"${total_invested:,.0f}") + with col2: + st.metric("Current", f"${total_current:,.0f}") + with col3: + st.metric("P/L", f"${total_pnl:,.0f}", delta=f"{total_pnl_pct:+.1f}%") + with col4: + st.metric("Positions", len(positions)) + + # Table + st.subheader("📊 Positions") + + data = [] + for p in positions: + pnl = (p["metrics"]["current_price"] - p["entry_price"]) * p.get("shares", 0) + data.append({ + "Ticker": p["ticker"], + "Entry": f"${p['entry_price']:.2f}", + "Current": f"${p['metrics']['current_price']:.2f}", + "Shares": p.get("shares", 0), + "P/L": f"${pnl:.2f}", + "P/L %": f"{p['metrics']['current_return']:+.1f}%", + "Days": p["metrics"]["days_held"] + }) + + df = pd.DataFrame(data) + st.dataframe(df, use_container_width=True) +``` + +--- + +### Task 11: Performance & Settings Pages (Simplified) + +**Files:** +- Create: `tradingagents/ui/pages/performance.py` +- Create: `tradingagents/ui/pages/settings.py` + +```python +# tradingagents/ui/pages/performance.py +"""Performance analytics.""" +import streamlit as st +import plotly.express as px +import pandas as pd +from tradingagents.ui.utils import load_statistics + + +def render(): + st.title("📊 Performance Analytics") + + stats = load_statistics() + if not stats: + st.warning("No data available") + return + + # Scanner heatmap + st.subheader("🔥 Scanner Performance") + + by_scanner = stats.get("by_scanner", {}) + if by_scanner: + data = [] + for scanner, d in by_scanner.items(): + data.append({ + "Scanner": scanner, + "Win Rate": d.get("win_rate_7d", 0), + "Avg Return": d.get("avg_return_7d", 0), + "Count": d.get("count", 0) + }) + + df = pd.DataFrame(data) + fig = px.scatter(df, x="Win Rate", y="Avg Return", size="Count", + color="Win Rate", hover_data=["Scanner"], + color_continuous_scale="RdYlGn") + fig.add_hline(y=0, line_dash="dash") + fig.add_vline(x=50, line_dash="dash") + st.plotly_chart(fig, use_container_width=True) +``` + +```python +# tradingagents/ui/pages/settings.py +"""Settings page.""" +import streamlit as st +from tradingagents.default_config import DEFAULT_CONFIG + + +def render(): + st.title("⚙️ Settings") + + st.info("Configuration UI - TODO: Implement save functionality") + + # Show current config + config = DEFAULT_CONFIG.get("discovery", {}) + + st.subheader("Pipelines") + pipelines = config.get("pipelines", {}) + for name, cfg in pipelines.items(): + with st.expander(f"{name.title()} Pipeline"): + st.write(f"Enabled: {cfg.get('enabled')}") + st.write(f"Priority: {cfg.get('priority')}") + st.write(f"Budget: {cfg.get('deep_dive_budget')}") + + st.subheader("Scanners") + scanners = config.get("scanners", {}) + for name, cfg in scanners.items(): + st.checkbox(f"{name}", value=cfg.get("enabled"), key=f"scan_{name}") +``` + +**Create __init__.py:** + +```python +# tradingagents/ui/pages/__init__.py +from . import home, todays_picks, portfolio, performance, settings +``` + +--- + +## Phase 5: Integration (15 min) + +### Task 12: Update Discovery Graph + +**Files:** +- Modify: `tradingagents/graph/discovery_graph.py` + +**Add to imports:** +```python +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY +``` + +**Replace scanner_node() method:** + +```python +def scanner_node(self, state: DiscoveryState) -> Dict[str, Any]: + """Scan using modular registry.""" + print("🔍 Scanning market for opportunities...") + + # Performance tracking + try: + self.analytics.update_performance_tracking() + except Exception as e: + print(f" Warning: {e}") + + state.setdefault("tool_logs", []) + + # Collect by pipeline + pipeline_candidates = { + "edge": [], "momentum": [], "news": [], "social": [], "events": [] + } + + pipeline_config = self.config.get("discovery", {}).get("pipelines", {}) + + # Run enabled scanners + for scanner_class in SCANNER_REGISTRY.get_all_scanners(): + pipeline = scanner_class.pipeline + + if not pipeline_config.get(pipeline, {}).get("enabled", True): + continue + + try: + scanner = scanner_class(self.config) + if not scanner.is_enabled(): + continue + + state["tool_executor"] = self._execute_tool_logged + candidates = scanner.scan(state) + pipeline_candidates[pipeline].extend(candidates) + + except Exception as e: + print(f" Error in {scanner_class.name}: {e}") + + # Merge candidates + all_candidates = [] + for candidates in pipeline_candidates.values(): + all_candidates.extend(candidates) + + unique_candidates = {} + self._merge_candidates_into_dict(all_candidates, unique_candidates) + + final = list(unique_candidates.values()) + print(f" Found {len(final)} unique candidates") + + return { + "tickers": [c["ticker"] for c in final], + "candidate_metadata": final, + "tool_logs": state.get("tool_logs", []), + "status": "scanned" + } +``` + +--- + +## Summary & Running + +**What's Implemented:** +1. ✅ Modular scanner registry +2. ✅ Config with pipelines/scanners +3. ✅ 2 edge scanners (insider, options) as templates +4. ✅ Dynamic position tracker +5. ✅ Position updater script +6. ✅ Full Streamlit dashboard (5 pages) +7. ✅ Discovery graph integration + +**To Run:** + +```bash +# Install dependencies +pip install streamlit plotly + +# Update positions (run hourly) +python scripts/update_positions.py + +# Start dashboard +streamlit run tradingagents/ui/dashboard.py + +# Run discovery +python -m cli.main analyze # Select discovery mode +``` + +**Next Steps:** +1. Test discovery with new architecture +2. Add more edge scanners (congress, 13F) +3. Add tests/docs when ready +4. Tune scanner limits based on performance diff --git a/docs/plans/2026-02-09-ml-win-probability-model.md b/docs/plans/2026-02-09-ml-win-probability-model.md new file mode 100644 index 00000000..0d2d48dc --- /dev/null +++ b/docs/plans/2026-02-09-ml-win-probability-model.md @@ -0,0 +1,44 @@ +# ML Win Probability Model — TabPFN + Triple-Barrier + +## Overview +Add an ML model that predicts win probability for each discovery candidate. +- **Training data**: Universe-wide historical simulation (~375K labeled samples) +- **Model**: TabPFN (foundation model for tabular data) with LightGBM fallback +- **Labels**: Triple-barrier method (+5% profit, -3% stop loss, 7-day timeout) +- **Integration**: Adds `ml_win_probability` field during enrichment + +## Components + +### 1. Feature Engineering (`tradingagents/ml/feature_engineering.py`) +Shared feature extraction used by both training and inference. +20 features computed locally from OHLCV via stockstats + pandas. + +### 2. Dataset Builder (`scripts/build_ml_dataset.py`) +- Fetches OHLCV for ~500 stocks × 3 years +- Computes features locally (no API calls for indicators) +- Applies triple-barrier labels +- Outputs `data/ml/training_dataset.parquet` + +### 3. Model Trainer (`scripts/train_ml_model.py`) +- Time-based train/validation split +- TabPFN or LightGBM training +- Walk-forward evaluation +- Outputs `data/ml/tabpfn_model.pkl` + `data/ml/metrics.json` + +### 4. Pipeline Integration +- `tradingagents/ml/predictor.py` — model loading + inference +- `tradingagents/dataflows/discovery/filter.py` — call predictor during enrichment +- `tradingagents/dataflows/discovery/ranker.py` — surface in LLM prompt + +## Triple-Barrier Labels +``` ++1 (WIN): Price hits +5% within 7 trading days +-1 (LOSS): Price hits -3% within 7 trading days + 0 (TIMEOUT): Neither barrier hit +``` + +## Features (20) +All computed locally from OHLCV — zero API calls for indicators. +rsi_14, macd, macd_signal, macd_hist, atr_pct, bb_width_pct, bb_position, +adx, mfi, stoch_k, volume_ratio_5d, volume_ratio_20d, return_1d, return_5d, +return_20d, sma50_distance, sma200_distance, high_low_range, gap_pct, log_market_cap diff --git a/main.py b/main.py index a85ee6ec..bed74c57 100644 --- a/main.py +++ b/main.py @@ -1,11 +1,14 @@ -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.default_config import DEFAULT_CONFIG - from dotenv import load_dotenv +from tradingagents.default_config import DEFAULT_CONFIG +from tradingagents.graph.trading_graph import TradingAgentsGraph +from tradingagents.utils.logger import get_logger + # Load environment variables from .env file load_dotenv() +logger = get_logger(__name__) + # Create a custom config config = DEFAULT_CONFIG.copy() config["deep_think_llm"] = "gpt-4o-mini" # Use a different model @@ -25,7 +28,7 @@ ta = TradingAgentsGraph(debug=True, config=config) # forward propagate _, decision = ta.propagate("NVDA", "2024-05-10") -print(decision) +logger.info(decision) # Memorize mistakes and reflect # ta.reflect_and_remember(1000) # parameter is the position returns diff --git a/pyproject.toml b/pyproject.toml index 63af4721..7f732188 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ dependencies = [ "eodhd>=1.0.32", "feedparser>=6.0.11", "finnhub-python>=2.4.23", + "google-genai>=1.60.0", "grip>=4.6.2", "langchain-anthropic>=0.3.15", "langchain-experimental>=0.3.4", @@ -23,13 +24,50 @@ dependencies = [ "praw>=7.8.1", "pytz>=2025.2", "questionary>=2.1.0", + "rapidfuzz>=3.14.3", "redis>=6.2.0", "requests>=2.32.4", "rich>=14.0.0", + "plotext>=5.2.8", + "plotille>=5.0.0", "setuptools>=80.9.0", "stockstats>=0.6.5", + "tavily>=1.1.0", "tqdm>=4.67.1", "tushare>=1.4.21", "typing-extensions>=4.14.0", "yfinance>=0.2.63", + "streamlit>=1.40.0", + "plotly>=5.18.0", + "lightgbm>=4.6.0", + "tabpfn>=2.1.3", +] + +[dependency-groups] +dev = [ + "black>=24.0.0", + "ruff>=0.8.0", + "pytest>=8.0.0", +] + +[tool.black] +line-length = 100 +target-version = ['py310'] +include = '\.pyi?$' + +[tool.ruff] +line-length = 100 +target-version = "py310" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions +] +ignore = [ + "E501", # line too long (handled by black) ] diff --git a/requirements.txt b/requirements.txt index 618b8fce..f8792d1a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,3 +25,7 @@ questionary langchain_anthropic langchain-google-genai tweepy +plotext +plotille +streamlit>=1.40.0 +plotly>=5.18.0 diff --git a/scripts/analyze_insider_transactions.py b/scripts/analyze_insider_transactions.py index 2481d174..7381f303 100644 --- a/scripts/analyze_insider_transactions.py +++ b/scripts/analyze_insider_transactions.py @@ -13,140 +13,174 @@ Usage: python scripts/analyze_insider_transactions.py AAPL --csv # Save to CSV """ -import yfinance as yf -import pandas as pd -import sys import os +import sys from datetime import datetime +from pathlib import Path + +import pandas as pd +import yfinance as yf + +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + def classify_transaction(text): """Classify transaction type based on text description.""" - if pd.isna(text) or text == '': - return 'Grant/Exercise' + if pd.isna(text) or text == "": + return "Grant/Exercise" text_lower = str(text).lower() - if 'sale' in text_lower: - return 'Sale' - elif 'purchase' in text_lower or 'buy' in text_lower: - return 'Purchase' - elif 'gift' in text_lower: - return 'Gift' + if "sale" in text_lower: + return "Sale" + elif "purchase" in text_lower or "buy" in text_lower: + return "Purchase" + elif "gift" in text_lower: + return "Gift" else: - return 'Other' + return "Other" def analyze_insider_transactions(ticker: str, save_csv: bool = False, output_dir: str = None): """Analyze and aggregate insider transactions for a given ticker. - + Args: ticker: Stock ticker symbol save_csv: Whether to save results to CSV files output_dir: Directory to save CSV files (default: current directory) - + Returns: Dictionary with DataFrames: 'by_position', 'yearly', 'sentiment' """ - print(f"\n{'='*80}") - print(f"INSIDER TRANSACTIONS ANALYSIS: {ticker.upper()}") - print(f"{'='*80}") - - result = {'by_position': None, 'by_person': None, 'yearly': None, 'sentiment': None} - + logger.info(f"\n{'='*80}") + logger.info(f"INSIDER TRANSACTIONS ANALYSIS: {ticker.upper()}") + logger.info(f"{'='*80}") + + result = {"by_position": None, "by_person": None, "yearly": None, "sentiment": None} + try: ticker_obj = yf.Ticker(ticker.upper()) data = ticker_obj.insider_transactions - + if data is None or data.empty: - print(f"No insider transaction data found for {ticker}") + logger.warning(f"No insider transaction data found for {ticker}") return result - + # Parse transaction type and year - data['Transaction'] = data['Text'].apply(classify_transaction) - data['Year'] = pd.to_datetime(data['Start Date']).dt.year - + data["Transaction"] = data["Text"].apply(classify_transaction) + data["Year"] = pd.to_datetime(data["Start Date"]).dt.year + # ============================================================ # BY POSITION, YEAR, TRANSACTION TYPE # ============================================================ - print(f"\n## BY POSITION\n") - - agg = data.groupby(['Position', 'Year', 'Transaction']).agg({ - 'Shares': 'sum', - 'Value': 'sum' - }).reset_index() - agg['Ticker'] = ticker.upper() - result['by_position'] = agg - - for position in sorted(agg['Position'].unique()): - print(f"\n### {position}") - print("-" * 50) - pos_data = agg[agg['Position'] == position].sort_values(['Year', 'Transaction'], ascending=[False, True]) + logger.info("\n## BY POSITION\n") + + agg = ( + data.groupby(["Position", "Year", "Transaction"]) + .agg({"Shares": "sum", "Value": "sum"}) + .reset_index() + ) + agg["Ticker"] = ticker.upper() + result["by_position"] = agg + + for position in sorted(agg["Position"].unique()): + logger.info(f"\n### {position}") + logger.info("-" * 50) + pos_data = agg[agg["Position"] == position].sort_values( + ["Year", "Transaction"], ascending=[False, True] + ) for _, row in pos_data.iterrows(): - value_str = f"${row['Value']:>15,.0f}" if pd.notna(row['Value']) and row['Value'] > 0 else f"{'N/A':>16}" - print(f" {row['Year']} | {row['Transaction']:15} | {row['Shares']:>12,.0f} shares | {value_str}") - + value_str = ( + f"${row['Value']:>15,.0f}" + if pd.notna(row["Value"]) and row["Value"] > 0 + else f"{'N/A':>16}" + ) + logger.info( + f" {row['Year']} | {row['Transaction']:15} | {row['Shares']:>12,.0f} shares | {value_str}" + ) + # ============================================================ # BY INSIDER # ============================================================ - print(f"\n\n{'='*80}") - print("INSIDER TRANSACTIONS BY PERSON") - print(f"{'='*80}") + logger.info(f"\n\n{'='*80}") + logger.info("INSIDER TRANSACTIONS BY PERSON") + logger.info(f"{'='*80}") + + insider_col = "Insider" + if insider_col not in data.columns and "Name" in data.columns: + insider_col = "Name" - insider_col = 'Insider' - if insider_col not in data.columns and 'Name' in data.columns: - insider_col = 'Name' - if insider_col in data.columns: - agg_person = data.groupby([insider_col, 'Position', 'Year', 'Transaction']).agg({ - 'Shares': 'sum', - 'Value': 'sum' - }).reset_index() - agg_person['Ticker'] = ticker.upper() - result['by_person'] = agg_person - + agg_person = ( + data.groupby([insider_col, "Position", "Year", "Transaction"]) + .agg({"Shares": "sum", "Value": "sum"}) + .reset_index() + ) + agg_person["Ticker"] = ticker.upper() + result["by_person"] = agg_person + for person in sorted(agg_person[insider_col].unique()): - print(f"\n### {str(person)}") - print("-" * 50) - p_data = agg_person[agg_person[insider_col] == person].sort_values(['Year', 'Transaction'], ascending=[False, True]) + logger.info(f"\n### {str(person)}") + logger.info("-" * 50) + p_data = agg_person[agg_person[insider_col] == person].sort_values( + ["Year", "Transaction"], ascending=[False, True] + ) for _, row in p_data.iterrows(): - value_str = f"${row['Value']:>15,.0f}" if pd.notna(row['Value']) and row['Value'] > 0 else f"{'N/A':>16}" - pos_str = str(row['Position'])[:25] - print(f" {row['Year']} | {pos_str:25} | {row['Transaction']:15} | {row['Shares']:>12,.0f} shares | {value_str}") + value_str = ( + f"${row['Value']:>15,.0f}" + if pd.notna(row["Value"]) and row["Value"] > 0 + else f"{'N/A':>16}" + ) + pos_str = str(row["Position"])[:25] + logger.info( + f" {row['Year']} | {pos_str:25} | {row['Transaction']:15} | {row['Shares']:>12,.0f} shares | {value_str}" + ) else: - print(f"Warning: Could not find 'Insider' or 'Name' column in data. Columns: {data.columns.tolist()}") - + logger.warning( + f"Warning: Could not find 'Insider' or 'Name' column in data. Columns: {data.columns.tolist()}" + ) + # ============================================================ # YEARLY SUMMARY # ============================================================ - print(f"\n\n{'='*80}") - print("YEARLY SUMMARY BY TRANSACTION TYPE") - print(f"{'='*80}") - - yearly = data.groupby(['Year', 'Transaction']).agg({ - 'Shares': 'sum', - 'Value': 'sum' - }).reset_index() - yearly['Ticker'] = ticker.upper() - result['yearly'] = yearly - - for year in sorted(yearly['Year'].unique(), reverse=True): - print(f"\n{year}:") - year_data = yearly[yearly['Year'] == year].sort_values('Transaction') + logger.info(f"\n\n{'='*80}") + logger.info("YEARLY SUMMARY BY TRANSACTION TYPE") + logger.info(f"{'='*80}") + + yearly = ( + data.groupby(["Year", "Transaction"]) + .agg({"Shares": "sum", "Value": "sum"}) + .reset_index() + ) + yearly["Ticker"] = ticker.upper() + result["yearly"] = yearly + + for year in sorted(yearly["Year"].unique(), reverse=True): + logger.info(f"\n{year}:") + year_data = yearly[yearly["Year"] == year].sort_values("Transaction") for _, row in year_data.iterrows(): - value_str = f"${row['Value']:>15,.0f}" if pd.notna(row['Value']) and row['Value'] > 0 else f"{'N/A':>16}" - print(f" {row['Transaction']:15} | {row['Shares']:>12,.0f} shares | {value_str}") - + value_str = ( + f"${row['Value']:>15,.0f}" + if pd.notna(row["Value"]) and row["Value"] > 0 + else f"{'N/A':>16}" + ) + logger.info(f" {row['Transaction']:15} | {row['Shares']:>12,.0f} shares | {value_str}") + # ============================================================ # OVERALL SENTIMENT # ============================================================ - print(f"\n\n{'='*80}") - print("INSIDER SENTIMENT SUMMARY") - print(f"{'='*80}\n") - - total_sales = data[data['Transaction'] == 'Sale']['Value'].sum() - total_purchases = data[data['Transaction'] == 'Purchase']['Value'].sum() - sales_count = len(data[data['Transaction'] == 'Sale']) - purchases_count = len(data[data['Transaction'] == 'Purchase']) + logger.info(f"\n\n{'='*80}") + logger.info("INSIDER SENTIMENT SUMMARY") + logger.info(f"{'='*80}\n") + + total_sales = data[data["Transaction"] == "Sale"]["Value"].sum() + total_purchases = data[data["Transaction"] == "Purchase"]["Value"].sum() + sales_count = len(data[data["Transaction"] == "Sale"]) + purchases_count = len(data[data["Transaction"] == "Purchase"]) net_value = total_purchases - total_sales - + # Determine sentiment if total_purchases > total_sales: sentiment = "BULLISH" @@ -156,134 +190,158 @@ def analyze_insider_transactions(ticker: str, save_csv: bool = False, output_dir sentiment = "SLIGHTLY_BEARISH" else: sentiment = "NEUTRAL" - - result['sentiment'] = pd.DataFrame([{ - 'Ticker': ticker.upper(), - 'Total_Sales_Count': sales_count, - 'Total_Sales_Value': total_sales, - 'Total_Purchases_Count': purchases_count, - 'Total_Purchases_Value': total_purchases, - 'Net_Value': net_value, - 'Sentiment': sentiment - }]) - - print(f"Total Sales: {sales_count:>5} transactions | ${total_sales:>15,.0f}") - print(f"Total Purchases: {purchases_count:>5} transactions | ${total_purchases:>15,.0f}") - + + result["sentiment"] = pd.DataFrame( + [ + { + "Ticker": ticker.upper(), + "Total_Sales_Count": sales_count, + "Total_Sales_Value": total_sales, + "Total_Purchases_Count": purchases_count, + "Total_Purchases_Value": total_purchases, + "Net_Value": net_value, + "Sentiment": sentiment, + } + ] + ) + + logger.info(f"Total Sales: {sales_count:>5} transactions | ${total_sales:>15,.0f}") + logger.info(f"Total Purchases: {purchases_count:>5} transactions | ${total_purchases:>15,.0f}") + if sentiment == "BULLISH": - print(f"\n⚡ BULLISH: Insiders are net BUYERS (${net_value:,.0f} net buying)") + logger.info(f"\n⚡ BULLISH: Insiders are net BUYERS (${net_value:,.0f} net buying)") elif sentiment == "BEARISH": - print(f"\n⚠️ BEARISH: Significant insider SELLING (${-net_value:,.0f} net selling)") + logger.info(f"\n⚠️ BEARISH: Significant insider SELLING (${-net_value:,.0f} net selling)") elif sentiment == "SLIGHTLY_BEARISH": - print(f"\n⚠️ SLIGHTLY BEARISH: More selling than buying (${-net_value:,.0f} net selling)") + logger.info( + f"\n⚠️ SLIGHTLY BEARISH: More selling than buying (${-net_value:,.0f} net selling)" + ) else: - print(f"\n📊 NEUTRAL: Balanced insider activity") - + logger.info("\n📊 NEUTRAL: Balanced insider activity") + # Save to CSV if requested if save_csv: if output_dir is None: output_dir = os.getcwd() os.makedirs(output_dir, exist_ok=True) - + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - + # Save by position - by_pos_file = os.path.join(output_dir, f"insider_by_position_{ticker.upper()}_{timestamp}.csv") + by_pos_file = os.path.join( + output_dir, f"insider_by_position_{ticker.upper()}_{timestamp}.csv" + ) agg.to_csv(by_pos_file, index=False) - print(f"\n📁 Saved: {by_pos_file}") + logger.info(f"\n📁 Saved: {by_pos_file}") # Save by person - if result['by_person'] is not None: - by_person_file = os.path.join(output_dir, f"insider_by_person_{ticker.upper()}_{timestamp}.csv") - result['by_person'].to_csv(by_person_file, index=False) - print(f"📁 Saved: {by_person_file}") - + if result["by_person"] is not None: + by_person_file = os.path.join( + output_dir, f"insider_by_person_{ticker.upper()}_{timestamp}.csv" + ) + result["by_person"].to_csv(by_person_file, index=False) + logger.info(f"📁 Saved: {by_person_file}") + # Save yearly summary - yearly_file = os.path.join(output_dir, f"insider_yearly_{ticker.upper()}_{timestamp}.csv") + yearly_file = os.path.join( + output_dir, f"insider_yearly_{ticker.upper()}_{timestamp}.csv" + ) yearly.to_csv(yearly_file, index=False) - print(f"📁 Saved: {yearly_file}") - + logger.info(f"📁 Saved: {yearly_file}") + # Save sentiment summary - sentiment_file = os.path.join(output_dir, f"insider_sentiment_{ticker.upper()}_{timestamp}.csv") - result['sentiment'].to_csv(sentiment_file, index=False) - print(f"📁 Saved: {sentiment_file}") - + sentiment_file = os.path.join( + output_dir, f"insider_sentiment_{ticker.upper()}_{timestamp}.csv" + ) + result["sentiment"].to_csv(sentiment_file, index=False) + logger.info(f"📁 Saved: {sentiment_file}") + except Exception as e: - print(f"Error analyzing {ticker}: {str(e)}") - + logger.error(f"Error analyzing {ticker}: {str(e)}") + return result if __name__ == "__main__": if len(sys.argv) < 2: - print("Usage: python analyze_insider_transactions.py TICKER [TICKER2 ...] [--csv] [--output-dir DIR]") - print("Example: python analyze_insider_transactions.py AAPL TSLA NVDA") - print(" python analyze_insider_transactions.py AAPL --csv") - print(" python analyze_insider_transactions.py AAPL --csv --output-dir ./output") + logger.info( + "Usage: python analyze_insider_transactions.py TICKER [TICKER2 ...] [--csv] [--output-dir DIR]" + ) + logger.info("Example: python analyze_insider_transactions.py AAPL TSLA NVDA") + logger.info(" python analyze_insider_transactions.py AAPL --csv") + logger.info(" python analyze_insider_transactions.py AAPL --csv --output-dir ./output") sys.exit(1) - + # Parse arguments args = sys.argv[1:] - save_csv = '--csv' in args + save_csv = "--csv" in args output_dir = None - - if '--output-dir' in args: - idx = args.index('--output-dir') + + if "--output-dir" in args: + idx = args.index("--output-dir") if idx + 1 < len(args): output_dir = args[idx + 1] - args = args[:idx] + args[idx+2:] + args = args[:idx] + args[idx + 2 :] else: - print("Error: --output-dir requires a directory path") + logger.error("Error: --output-dir requires a directory path") sys.exit(1) - + if save_csv: - args.remove('--csv') - - tickers = [t for t in args if not t.startswith('--')] - + args.remove("--csv") + + tickers = [t for t in args if not t.startswith("--")] + # Collect all results for combined CSV all_by_position = [] all_by_person = [] all_yearly = [] all_sentiment = [] - + for ticker in tickers: result = analyze_insider_transactions(ticker, save_csv=save_csv, output_dir=output_dir) - if result['by_position'] is not None: - all_by_position.append(result['by_position']) - if result['by_person'] is not None: - all_by_person.append(result['by_person']) - if result['yearly'] is not None: - all_yearly.append(result['yearly']) - if result['sentiment'] is not None: - all_sentiment.append(result['sentiment']) - + if result["by_position"] is not None: + all_by_position.append(result["by_position"]) + if result["by_person"] is not None: + all_by_person.append(result["by_person"]) + if result["yearly"] is not None: + all_yearly.append(result["yearly"]) + if result["sentiment"] is not None: + all_sentiment.append(result["sentiment"]) + # If multiple tickers and CSV mode, also save combined files if save_csv and len(tickers) > 1: if output_dir is None: output_dir = os.getcwd() timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - + if all_by_position: combined_pos = pd.concat(all_by_position, ignore_index=True) - combined_pos_file = os.path.join(output_dir, f"insider_by_position_combined_{timestamp}.csv") + combined_pos_file = os.path.join( + output_dir, f"insider_by_position_combined_{timestamp}.csv" + ) combined_pos.to_csv(combined_pos_file, index=False) - print(f"\n📁 Combined: {combined_pos_file}") + logger.info(f"\n📁 Combined: {combined_pos_file}") if all_by_person: combined_person = pd.concat(all_by_person, ignore_index=True) - combined_person_file = os.path.join(output_dir, f"insider_by_person_combined_{timestamp}.csv") + combined_person_file = os.path.join( + output_dir, f"insider_by_person_combined_{timestamp}.csv" + ) combined_person.to_csv(combined_person_file, index=False) - print(f"📁 Combined: {combined_person_file}") - + logger.info(f"📁 Combined: {combined_person_file}") + if all_yearly: combined_yearly = pd.concat(all_yearly, ignore_index=True) - combined_yearly_file = os.path.join(output_dir, f"insider_yearly_combined_{timestamp}.csv") + combined_yearly_file = os.path.join( + output_dir, f"insider_yearly_combined_{timestamp}.csv" + ) combined_yearly.to_csv(combined_yearly_file, index=False) - print(f"📁 Combined: {combined_yearly_file}") - + logger.info(f"📁 Combined: {combined_yearly_file}") + if all_sentiment: combined_sentiment = pd.concat(all_sentiment, ignore_index=True) - combined_sentiment_file = os.path.join(output_dir, f"insider_sentiment_combined_{timestamp}.csv") + combined_sentiment_file = os.path.join( + output_dir, f"insider_sentiment_combined_{timestamp}.csv" + ) combined_sentiment.to_csv(combined_sentiment_file, index=False) - print(f"📁 Combined: {combined_sentiment_file}") + logger.info(f"📁 Combined: {combined_sentiment_file}") diff --git a/scripts/build_historical_memories.py b/scripts/build_historical_memories.py index b4a8b749..e91b6e49 100644 --- a/scripts/build_historical_memories.py +++ b/scripts/build_historical_memories.py @@ -11,18 +11,23 @@ Usage: python scripts/build_historical_memories.py """ -import sys import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import sys + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from tradingagents.default_config import DEFAULT_CONFIG -from tradingagents.agents.utils.historical_memory_builder import HistoricalMemoryBuilder import pickle from datetime import datetime, timedelta +from tradingagents.agents.utils.historical_memory_builder import HistoricalMemoryBuilder +from tradingagents.default_config import DEFAULT_CONFIG +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + def main(): - print(""" + logger.info(""" ╔══════════════════════════════════════════════════════════════╗ ║ TradingAgents - Historical Memory Builder ║ ╚══════════════════════════════════════════════════════════════╝ @@ -30,25 +35,34 @@ def main(): # Configuration tickers = [ - "AAPL", "GOOGL", "MSFT", "NVDA", "TSLA", # Tech - "JPM", "BAC", "GS", # Finance - "XOM", "CVX", # Energy - "JNJ", "PFE", # Healthcare - "WMT", "AMZN" # Retail + "AAPL", + "GOOGL", + "MSFT", + "NVDA", + "TSLA", # Tech + "JPM", + "BAC", + "GS", # Finance + "XOM", + "CVX", # Energy + "JNJ", + "PFE", # Healthcare + "WMT", + "AMZN", # Retail ] # Date range - last 2 years end_date = datetime.now() start_date = end_date - timedelta(days=730) # 2 years - print(f"Tickers: {', '.join(tickers)}") - print(f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}") - print(f"Lookforward: 7 days (1 week returns)") - print(f"Sample interval: 30 days (monthly)\n") + logger.info(f"Tickers: {', '.join(tickers)}") + logger.info(f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}") + logger.info("Lookforward: 7 days (1 week returns)") + logger.info("Sample interval: 30 days (monthly)\n") proceed = input("Proceed with memory building? (y/n): ") - if proceed.lower() != 'y': - print("Aborted.") + if proceed.lower() != "y": + logger.info("Aborted.") return # Build memories @@ -59,7 +73,7 @@ def main(): start_date=start_date.strftime("%Y-%m-%d"), end_date=end_date.strftime("%Y-%m-%d"), lookforward_days=7, - interval_days=30 + interval_days=30, ) # Save to disk @@ -74,39 +88,36 @@ def main(): # Save the ChromaDB collection data # Note: ChromaDB doesn't serialize well, so we extract the data collection = memory.situation_collection - data = { - "documents": [], - "metadatas": [], - "embeddings": [], - "ids": [] - } # Get all items from collection results = collection.get(include=["documents", "metadatas", "embeddings"]) - with open(filename, 'wb') as f: - pickle.dump({ - "documents": results["documents"], - "metadatas": results["metadatas"], - "embeddings": results["embeddings"], - "ids": results["ids"], - "created_at": timestamp, - "tickers": tickers, - "config": { - "start_date": start_date.strftime("%Y-%m-%d"), - "end_date": end_date.strftime("%Y-%m-%d"), - "lookforward_days": 7, - "interval_days": 30 - } - }, f) + with open(filename, "wb") as f: + pickle.dump( + { + "documents": results["documents"], + "metadatas": results["metadatas"], + "embeddings": results["embeddings"], + "ids": results["ids"], + "created_at": timestamp, + "tickers": tickers, + "config": { + "start_date": start_date.strftime("%Y-%m-%d"), + "end_date": end_date.strftime("%Y-%m-%d"), + "lookforward_days": 7, + "interval_days": 30, + }, + }, + f, + ) - print(f"✅ Saved {agent_type} memory to {filename}") + logger.info(f"✅ Saved {agent_type} memory to {filename}") - print(f"\n🎉 Memory building complete!") - print(f" Memories saved to: {memory_dir}") - print(f"\n📝 To use these memories, update DEFAULT_CONFIG with:") - print(f' "memory_dir": "{memory_dir}"') - print(f' "load_historical_memories": True') + logger.info("\n🎉 Memory building complete!") + logger.info(f" Memories saved to: {memory_dir}") + logger.info("\n📝 To use these memories, update DEFAULT_CONFIG with:") + logger.info(f' "memory_dir": "{memory_dir}"') + logger.info(' "load_historical_memories": True') if __name__ == "__main__": diff --git a/scripts/build_ml_dataset.py b/scripts/build_ml_dataset.py new file mode 100644 index 00000000..7e0ba101 --- /dev/null +++ b/scripts/build_ml_dataset.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +"""Build ML training dataset from historical OHLCV data. + +Fetches price data for a universe of liquid stocks, computes features +locally via stockstats, and applies triple-barrier labels. + +Usage: + python scripts/build_ml_dataset.py + python scripts/build_ml_dataset.py --stocks 100 --years 2 + python scripts/build_ml_dataset.py --ticker-file data/tickers_top50.txt +""" + +from __future__ import annotations + +import argparse +import os +import sys +import time +from pathlib import Path + +import numpy as np +import pandas as pd + +# Add project root to path +project_root = str(Path(__file__).resolve().parent.parent) +if project_root not in sys.path: + sys.path.insert(0, project_root) + +from tradingagents.ml.feature_engineering import ( + FEATURE_COLUMNS, + MIN_HISTORY_ROWS, + apply_triple_barrier_labels, + compute_features_bulk, +) +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + +# Default universe: S&P 500 most liquid by volume (top ~200) +# Can be overridden via --ticker-file +DEFAULT_TICKERS = [ + # Mega-cap tech + "AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA", "AVGO", "ORCL", "CRM", + "AMD", "INTC", "CSCO", "ADBE", "NFLX", "QCOM", "TXN", "AMAT", "MU", "LRCX", + "KLAC", "MRVL", "SNPS", "CDNS", "PANW", "CRWD", "FTNT", "NOW", "UBER", "ABNB", + # Financials + "JPM", "BAC", "WFC", "GS", "MS", "C", "SCHW", "BLK", "AXP", "USB", + "PNC", "TFC", "COF", "BK", "STT", "FITB", "HBAN", "RF", "CFG", "KEY", + # Healthcare + "UNH", "JNJ", "LLY", "PFE", "ABBV", "MRK", "TMO", "ABT", "DHR", "BMY", + "AMGN", "GILD", "ISRG", "VRTX", "REGN", "MDT", "SYK", "BSX", "EW", "ZTS", + # Consumer + "WMT", "PG", "KO", "PEP", "COST", "MCD", "NKE", "SBUX", "TGT", "LOW", + "HD", "TJX", "ROST", "DG", "DLTR", "EL", "CL", "KMB", "GIS", "K", + # Energy + "XOM", "CVX", "COP", "EOG", "SLB", "MPC", "PSX", "VLO", "OXY", "DVN", + "HAL", "FANG", "HES", "BKR", "KMI", "WMB", "OKE", "ET", "TRGP", "LNG", + # Industrials + "CAT", "DE", "UNP", "UPS", "HON", "RTX", "BA", "LMT", "GD", "NOC", + "GE", "MMM", "EMR", "ITW", "PH", "ROK", "ETN", "SWK", "CMI", "PCAR", + # Materials & Utilities + "LIN", "APD", "ECL", "SHW", "DD", "NEM", "FCX", "VMC", "MLM", "NUE", + "NEE", "DUK", "SO", "D", "AEP", "EXC", "SRE", "XEL", "WEC", "ES", + # REITs & Telecom + "AMT", "PLD", "CCI", "EQIX", "SPG", "O", "PSA", "DLR", "WELL", "AVB", + "T", "VZ", "TMUS", "CHTR", "CMCSA", + # High-volatility / popular retail + "COIN", "MARA", "RIOT", "PLTR", "SOFI", "HOOD", "RBLX", "SNAP", "PINS", "SQ", + "SHOP", "SE", "ROKU", "DKNG", "PENN", "WYNN", "MGM", "LVS", "DASH", "TTD", + # Biotech + "MRNA", "BNTX", "BIIB", "SGEN", "ALNY", "BMRN", "EXAS", "DXCM", "HZNP", "INCY", +] + +OUTPUT_DIR = Path("data/ml") + + +def fetch_ohlcv(ticker: str, start: str, end: str) -> pd.DataFrame: + """Fetch OHLCV data for a single ticker via yfinance.""" + from tradingagents.dataflows.y_finance import download_history + + df = download_history( + ticker, + start=start, + end=end, + multi_level_index=False, + progress=False, + auto_adjust=True, + ) + + if df.empty: + return df + + df = df.reset_index() + return df + + +def get_market_cap(ticker: str) -> float | None: + """Get current market cap for a ticker (snapshot — used as static feature).""" + try: + import yfinance as yf + + info = yf.Ticker(ticker).info + return info.get("marketCap") + except Exception: + return None + + +def process_ticker( + ticker: str, + start: str, + end: str, + profit_target: float, + stop_loss: float, + max_holding_days: int, + market_cap: float | None = None, +) -> pd.DataFrame | None: + """Process a single ticker: fetch data, compute features, apply labels.""" + try: + ohlcv = fetch_ohlcv(ticker, start, end) + if ohlcv.empty or len(ohlcv) < MIN_HISTORY_ROWS + max_holding_days: + logger.debug(f"{ticker}: insufficient data ({len(ohlcv)} rows), skipping") + return None + + # Compute features + features = compute_features_bulk(ohlcv, market_cap=market_cap) + if features.empty: + logger.debug(f"{ticker}: feature computation failed, skipping") + return None + + # Compute triple-barrier labels + close = ohlcv.set_index("Date")["Close"] if "Date" in ohlcv.columns else ohlcv["Close"] + if isinstance(close.index, pd.DatetimeIndex): + pass + else: + close.index = pd.to_datetime(close.index) + + labels = apply_triple_barrier_labels( + close, + profit_target=profit_target, + stop_loss=stop_loss, + max_holding_days=max_holding_days, + ) + + # Align features and labels by date + combined = features.join(labels, how="inner") + + # Drop rows with NaN features or labels + combined = combined.dropna(subset=["label"] + FEATURE_COLUMNS) + + if combined.empty: + logger.debug(f"{ticker}: no valid rows after alignment, skipping") + return None + + # Add metadata columns + combined["ticker"] = ticker + combined["date"] = combined.index + + logger.info( + f"{ticker}: {len(combined)} samples " + f"(WIN={int((combined['label'] == 1).sum())}, " + f"LOSS={int((combined['label'] == -1).sum())}, " + f"TIMEOUT={int((combined['label'] == 0).sum())})" + ) + + return combined + + except Exception as e: + logger.warning(f"{ticker}: error processing — {e}") + return None + + +def build_dataset( + tickers: list[str], + start: str = "2022-01-01", + end: str = "2025-12-31", + profit_target: float = 0.05, + stop_loss: float = 0.03, + max_holding_days: int = 7, +) -> pd.DataFrame: + """Build the full training dataset across all tickers.""" + all_data = [] + total = len(tickers) + + logger.info(f"Building ML dataset: {total} tickers, {start} to {end}") + logger.info( + f"Triple-barrier: +{profit_target*100:.0f}% profit, " + f"-{stop_loss*100:.0f}% stop, {max_holding_days}d timeout" + ) + + # Batch-fetch market caps + logger.info("Fetching market caps...") + market_caps = {} + for ticker in tickers: + market_caps[ticker] = get_market_cap(ticker) + time.sleep(0.05) # rate limit courtesy + + for i, ticker in enumerate(tickers): + logger.info(f"[{i+1}/{total}] Processing {ticker}...") + result = process_ticker( + ticker=ticker, + start=start, + end=end, + profit_target=profit_target, + stop_loss=stop_loss, + max_holding_days=max_holding_days, + market_cap=market_caps.get(ticker), + ) + if result is not None: + all_data.append(result) + + # Brief pause between tickers to be polite to yfinance + if (i + 1) % 50 == 0: + logger.info(f"Progress: {i+1}/{total} tickers processed, pausing 2s...") + time.sleep(2) + + if not all_data: + logger.error("No data collected — check tickers and date range") + return pd.DataFrame() + + dataset = pd.concat(all_data, ignore_index=True) + + logger.info(f"\n{'='*60}") + logger.info(f"Dataset built: {len(dataset)} total samples from {len(all_data)} tickers") + logger.info(f"Label distribution:") + logger.info(f" WIN (+1): {int((dataset['label'] == 1).sum()):>7} ({(dataset['label'] == 1).mean()*100:.1f}%)") + logger.info(f" LOSS (-1): {int((dataset['label'] == -1).sum()):>7} ({(dataset['label'] == -1).mean()*100:.1f}%)") + logger.info(f" TIMEOUT: {int((dataset['label'] == 0).sum()):>7} ({(dataset['label'] == 0).mean()*100:.1f}%)") + logger.info(f"Features: {len(FEATURE_COLUMNS)}") + logger.info(f"{'='*60}") + + return dataset + + +def main(): + parser = argparse.ArgumentParser(description="Build ML training dataset") + parser.add_argument("--stocks", type=int, default=None, help="Limit to N stocks from default universe") + parser.add_argument("--ticker-file", type=str, default=None, help="File with tickers (one per line)") + parser.add_argument("--start", type=str, default="2022-01-01", help="Start date (YYYY-MM-DD)") + parser.add_argument("--end", type=str, default="2025-12-31", help="End date (YYYY-MM-DD)") + parser.add_argument("--profit-target", type=float, default=0.05, help="Profit target fraction (default: 0.05)") + parser.add_argument("--stop-loss", type=float, default=0.03, help="Stop loss fraction (default: 0.03)") + parser.add_argument("--holding-days", type=int, default=7, help="Max holding days (default: 7)") + parser.add_argument("--output", type=str, default=None, help="Output parquet path") + args = parser.parse_args() + + # Determine ticker list + if args.ticker_file: + with open(args.ticker_file) as f: + tickers = [line.strip().upper() for line in f if line.strip() and not line.startswith("#")] + logger.info(f"Loaded {len(tickers)} tickers from {args.ticker_file}") + else: + tickers = DEFAULT_TICKERS + if args.stocks: + tickers = tickers[: args.stocks] + + # Build dataset + dataset = build_dataset( + tickers=tickers, + start=args.start, + end=args.end, + profit_target=args.profit_target, + stop_loss=args.stop_loss, + max_holding_days=args.holding_days, + ) + + if dataset.empty: + logger.error("Empty dataset — aborting") + sys.exit(1) + + # Save + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + output_path = args.output or str(OUTPUT_DIR / "training_dataset.parquet") + dataset.to_parquet(output_path, index=False) + logger.info(f"Saved dataset to {output_path} ({os.path.getsize(output_path) / 1e6:.1f} MB)") + + +if __name__ == "__main__": + main() diff --git a/scripts/build_strategy_specific_memories.py b/scripts/build_strategy_specific_memories.py index bfabdee4..e8720d79 100644 --- a/scripts/build_strategy_specific_memories.py +++ b/scripts/build_strategy_specific_memories.py @@ -9,41 +9,78 @@ This script creates memory sets optimized for: - Long-term investing (90-day horizon, quarterly samples) """ -import sys import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import sys +from pathlib import Path + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from tradingagents.default_config import DEFAULT_CONFIG -from tradingagents.agents.utils.historical_memory_builder import HistoricalMemoryBuilder import pickle from datetime import datetime, timedelta +from tradingagents.agents.utils.historical_memory_builder import HistoricalMemoryBuilder +from tradingagents.default_config import DEFAULT_CONFIG +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) # Strategy configurations STRATEGIES = { "day_trading": { - "lookforward_days": 1, # Next day returns - "interval_days": 1, # Sample daily + "lookforward_days": 1, # Next day returns + "interval_days": 1, # Sample daily "description": "Day Trading - Capture intraday momentum and next-day moves", "tickers": ["SPY", "QQQ", "AAPL", "TSLA", "NVDA", "AMD", "AMZN"], # High volume }, "swing_trading": { - "lookforward_days": 7, # Weekly returns - "interval_days": 7, # Sample weekly + "lookforward_days": 7, # Weekly returns + "interval_days": 7, # Sample weekly "description": "Swing Trading - Capture week-long trends and momentum", - "tickers": ["AAPL", "GOOGL", "MSFT", "NVDA", "TSLA", "META", "AMZN", "AMD", "NFLX"], + "tickers": [ + "AAPL", + "GOOGL", + "MSFT", + "NVDA", + "TSLA", + "META", + "AMZN", + "AMD", + "NFLX", + ], }, "position_trading": { - "lookforward_days": 30, # Monthly returns - "interval_days": 30, # Sample monthly + "lookforward_days": 30, # Monthly returns + "interval_days": 30, # Sample monthly "description": "Position Trading - Capture monthly trends and fundamentals", - "tickers": ["AAPL", "GOOGL", "MSFT", "NVDA", "TSLA", "JPM", "BAC", "XOM", "JNJ", "WMT"], + "tickers": [ + "AAPL", + "GOOGL", + "MSFT", + "NVDA", + "TSLA", + "JPM", + "BAC", + "XOM", + "JNJ", + "WMT", + ], }, "long_term_investing": { - "lookforward_days": 90, # Quarterly returns - "interval_days": 90, # Sample quarterly + "lookforward_days": 90, # Quarterly returns + "interval_days": 90, # Sample quarterly "description": "Long-term Investing - Capture fundamental value and trends", - "tickers": ["AAPL", "GOOGL", "MSFT", "BRK.B", "JPM", "JNJ", "PG", "KO", "DIS", "V"], + "tickers": [ + "AAPL", + "GOOGL", + "MSFT", + "BRK.B", + "JPM", + "JNJ", + "PG", + "KO", + "DIS", + "V", + ], }, } @@ -53,7 +90,7 @@ def build_strategy_memories(strategy_name: str, config: dict): strategy = STRATEGIES[strategy_name] - print(f""" + logger.info(f""" ╔══════════════════════════════════════════════════════════════╗ ║ Building Memories: {strategy_name.upper().replace('_', ' ')} ╚══════════════════════════════════════════════════════════════╝ @@ -72,11 +109,11 @@ Tickers: {', '.join(strategy['tickers'])} builder = HistoricalMemoryBuilder(DEFAULT_CONFIG) memories = builder.populate_agent_memories( - tickers=strategy['tickers'], + tickers=strategy["tickers"], start_date=start_date.strftime("%Y-%m-%d"), end_date=end_date.strftime("%Y-%m-%d"), - lookforward_days=strategy['lookforward_days'], - interval_days=strategy['interval_days'] + lookforward_days=strategy["lookforward_days"], + interval_days=strategy["interval_days"], ) # Save to disk @@ -92,33 +129,36 @@ Tickers: {', '.join(strategy['tickers'])} collection = memory.situation_collection results = collection.get(include=["documents", "metadatas", "embeddings"]) - with open(filename, 'wb') as f: - pickle.dump({ - "documents": results["documents"], - "metadatas": results["metadatas"], - "embeddings": results["embeddings"], - "ids": results["ids"], - "created_at": timestamp, - "strategy": strategy_name, - "tickers": strategy['tickers'], - "config": { - "start_date": start_date.strftime("%Y-%m-%d"), - "end_date": end_date.strftime("%Y-%m-%d"), - "lookforward_days": strategy['lookforward_days'], - "interval_days": strategy['interval_days'] - } - }, f) + with open(filename, "wb") as f: + pickle.dump( + { + "documents": results["documents"], + "metadatas": results["metadatas"], + "embeddings": results["embeddings"], + "ids": results["ids"], + "created_at": timestamp, + "strategy": strategy_name, + "tickers": strategy["tickers"], + "config": { + "start_date": start_date.strftime("%Y-%m-%d"), + "end_date": end_date.strftime("%Y-%m-%d"), + "lookforward_days": strategy["lookforward_days"], + "interval_days": strategy["interval_days"], + }, + }, + f, + ) - print(f"✅ Saved {agent_type} memory to {filename}") + logger.info(f"✅ Saved {agent_type} memory to {filename}") - print(f"\n🎉 {strategy_name.replace('_', ' ').title()} memories complete!") - print(f" Saved to: {memory_dir}\n") + logger.info(f"\n🎉 {strategy_name.replace('_', ' ').title()} memories complete!") + logger.info(f" Saved to: {memory_dir}\n") return memory_dir def main(): - print(""" + logger.info(""" ╔══════════════════════════════════════════════════════════════╗ ║ TradingAgents - Strategy-Specific Memory Builder ║ ╚══════════════════════════════════════════════════════════════╝ @@ -131,29 +171,31 @@ This script builds optimized memories for different trading styles: 4. Long-term - 90-day returns, quarterly samples """) - print("Available strategies:") + logger.info("Available strategies:") for i, (name, config) in enumerate(STRATEGIES.items(), 1): - print(f" {i}. {name.replace('_', ' ').title()}") - print(f" {config['description']}") - print(f" Horizon: {config['lookforward_days']} days, Interval: {config['interval_days']} days\n") + logger.info(f" {i}. {name.replace('_', ' ').title()}") + logger.info(f" {config['description']}") + logger.info( + f" Horizon: {config['lookforward_days']} days, Interval: {config['interval_days']} days\n" + ) choice = input("Choose strategy (1-4, or 'all' for all strategies): ").strip() - if choice.lower() == 'all': + if choice.lower() == "all": strategies_to_build = list(STRATEGIES.keys()) else: try: idx = int(choice) - 1 strategies_to_build = [list(STRATEGIES.keys())[idx]] except (ValueError, IndexError): - print("Invalid choice. Exiting.") + logger.error("Invalid choice. Exiting.") return - print(f"\nWill build memories for: {', '.join(strategies_to_build)}") + logger.info(f"\nWill build memories for: {', '.join(strategies_to_build)}") proceed = input("Proceed? (y/n): ") - if proceed.lower() != 'y': - print("Aborted.") + if proceed.lower() != "y": + logger.info("Aborted.") return # Build memories for each selected strategy @@ -163,19 +205,19 @@ This script builds optimized memories for different trading styles: results[strategy_name] = memory_dir # Print summary - print("\n" + "="*70) - print("📊 MEMORY BUILDING COMPLETE") - print("="*70) + logger.info("\n" + "=" * 70) + logger.info("📊 MEMORY BUILDING COMPLETE") + logger.info("=" * 70) for strategy_name, memory_dir in results.items(): - print(f"\n{strategy_name.replace('_', ' ').title()}:") - print(f" Location: {memory_dir}") - print(f" Config to use:") - print(f' "memory_dir": "{memory_dir}"') - print(f' "load_historical_memories": True') + logger.info(f"\n{strategy_name.replace('_', ' ').title()}:") + logger.info(f" Location: {memory_dir}") + logger.info(" Config to use:") + logger.info(f' "memory_dir": "{memory_dir}"') + logger.info(' "load_historical_memories": True') - print("\n" + "="*70) - print("\n💡 TIP: To use a specific strategy's memories, update your config:") - print(""" + logger.info("\n" + "=" * 70) + logger.info("\n💡 TIP: To use a specific strategy's memories, update your config:") + logger.info(""" config = DEFAULT_CONFIG.copy() config["memory_dir"] = "data/memories/swing_trading" # or your strategy config["load_historical_memories"] = True diff --git a/scripts/scan_reddit_dd.py b/scripts/scan_reddit_dd.py index 251cde03..992482bb 100755 --- a/scripts/scan_reddit_dd.py +++ b/scripts/scan_reddit_dd.py @@ -12,31 +12,58 @@ Examples: python scripts/scan_reddit_dd.py --output reports/reddit_dd_2024_01_15.md """ +import argparse import os import sys -import argparse from datetime import datetime from pathlib import Path + from dotenv import load_dotenv + +from tradingagents.utils.logger import get_logger + load_dotenv() # Add parent directory to path sys.path.insert(0, str(Path(__file__).parent.parent)) -from tradingagents.dataflows.reddit_api import get_reddit_undiscovered_dd +logger = get_logger(__name__) + from langchain_openai import ChatOpenAI +from tradingagents.dataflows.reddit_api import get_reddit_undiscovered_dd + def main(): - parser = argparse.ArgumentParser(description='Scan Reddit for high-quality DD posts') - parser.add_argument('--hours', type=int, default=72, help='Hours to look back (default: 72)') - parser.add_argument('--limit', type=int, default=100, help='Number of posts to scan (default: 100)') - parser.add_argument('--top', type=int, default=15, help='Number of top DD to include (default: 15)') - parser.add_argument('--output', type=str, help='Output markdown file (default: reports/reddit_dd_YYYY_MM_DD.md)') - parser.add_argument('--min-score', type=int, default=55, help='Minimum quality score (default: 55)') - parser.add_argument('--model', type=str, default='gpt-4o-mini', help='LLM model to use (default: gpt-4o-mini)') - parser.add_argument('--temperature', type=float, default=0, help='LLM temperature (default: 0)') - parser.add_argument('--comments', type=int, default=10, help='Number of top comments to include (default: 10)') + parser = argparse.ArgumentParser(description="Scan Reddit for high-quality DD posts") + parser.add_argument("--hours", type=int, default=72, help="Hours to look back (default: 72)") + parser.add_argument( + "--limit", type=int, default=100, help="Number of posts to scan (default: 100)" + ) + parser.add_argument( + "--top", type=int, default=15, help="Number of top DD to include (default: 15)" + ) + parser.add_argument( + "--output", + type=str, + help="Output markdown file (default: reports/reddit_dd_YYYY_MM_DD.md)", + ) + parser.add_argument( + "--min-score", type=int, default=55, help="Minimum quality score (default: 55)" + ) + parser.add_argument( + "--model", + type=str, + default="gpt-4o-mini", + help="LLM model to use (default: gpt-4o-mini)", + ) + parser.add_argument("--temperature", type=float, default=0, help="LLM temperature (default: 0)") + parser.add_argument( + "--comments", + type=int, + default=10, + help="Number of top comments to include (default: 10)", + ) args = parser.parse_args() @@ -51,36 +78,36 @@ def main(): timestamp = datetime.now().strftime("%Y_%m_%d_%H%M") output_file = reports_dir / f"reddit_dd_{timestamp}.md" - print("=" * 70) - print("📊 REDDIT DD SCANNER") - print("=" * 70) - print(f"Lookback: {args.hours} hours") - print(f"Scan limit: {args.limit} posts") - print(f"Top results: {args.top}") - print(f"Min quality score: {args.min_score}") - print(f"LLM model: {args.model}") - print(f"Temperature: {args.temperature}") - print(f"Output: {output_file}") - print("=" * 70) - print() + logger.info("=" * 70) + logger.info("📊 REDDIT DD SCANNER") + logger.info("=" * 70) + logger.info(f"Lookback: {args.hours} hours") + logger.info(f"Scan limit: {args.limit} posts") + logger.info(f"Top results: {args.top}") + logger.info(f"Min quality score: {args.min_score}") + logger.info(f"LLM model: {args.model}") + logger.info(f"Temperature: {args.temperature}") + logger.info(f"Output: {output_file}") + logger.info("=" * 70) + logger.info("") # Initialize LLM - print("Initializing LLM...") + logger.info("Initializing LLM...") llm = ChatOpenAI( model=args.model, temperature=args.temperature, - api_key=os.getenv("OPENAI_API_KEY") + api_key=os.getenv("OPENAI_API_KEY"), ) # Scan Reddit - print(f"\n🔍 Scanning Reddit (last {args.hours} hours)...\n") + logger.info(f"\n🔍 Scanning Reddit (last {args.hours} hours)...\n") dd_report = get_reddit_undiscovered_dd( lookback_hours=args.hours, scan_limit=args.limit, top_n=args.top, num_comments=args.comments, - llm_evaluator=llm + llm_evaluator=llm, ) # Add header with metadata @@ -98,47 +125,49 @@ def main(): full_report = header + dd_report # Save to file - with open(output_file, 'w') as f: + with open(output_file, "w") as f: f.write(full_report) - print("\n" + "=" * 70) - print(f"✅ Report saved to: {output_file}") - print("=" * 70) + logger.info("\n" + "=" * 70) + logger.info(f"✅ Report saved to: {output_file}") + logger.info("=" * 70) # Print summary - print("\n📈 SUMMARY:") + logger.info("\n📈 SUMMARY:") # Count quality posts by parsing the report import re - quality_match = re.search(r'\*\*High Quality:\*\* (\d+) DD posts', dd_report) - scanned_match = re.search(r'\*\*Scanned:\*\* (\d+) posts', dd_report) + + quality_match = re.search(r"\*\*High Quality:\*\* (\d+) DD posts", dd_report) + scanned_match = re.search(r"\*\*Scanned:\*\* (\d+) posts", dd_report) if scanned_match and quality_match: scanned = int(scanned_match.group(1)) quality = int(quality_match.group(1)) - print(f" • Posts scanned: {scanned}") - print(f" • Quality DD found: {quality}") + logger.info(f" • Posts scanned: {scanned}") + logger.info(f" • Quality DD found: {quality}") if scanned > 0: - print(f" • Quality rate: {(quality/scanned)*100:.1f}%") + logger.info(f" • Quality rate: {(quality/scanned)*100:.1f}%") # Extract tickers - ticker_matches = re.findall(r'\*\*Ticker:\*\* \$([A-Z]+)', dd_report) + ticker_matches = re.findall(r"\*\*Ticker:\*\* \$([A-Z]+)", dd_report) if ticker_matches: unique_tickers = list(set(ticker_matches)) - print(f" • Tickers mentioned: {', '.join(['$' + t for t in unique_tickers])}") + logger.info(f" • Tickers mentioned: {', '.join(['$' + t for t in unique_tickers])}") - print() - print("💡 TIP: Review the report and investigate promising opportunities!") + logger.info("") + logger.info("💡 TIP: Review the report and investigate promising opportunities!") if __name__ == "__main__": try: main() except KeyboardInterrupt: - print("\n\n⚠️ Scan interrupted by user") + logger.warning("\n\n⚠️ Scan interrupted by user") sys.exit(1) except Exception as e: - print(f"\n❌ Error: {str(e)}") + logger.error(f"\n❌ Error: {str(e)}") import traceback + traceback.print_exc() sys.exit(1) diff --git a/scripts/track_recommendation_performance.py b/scripts/track_recommendation_performance.py new file mode 100644 index 00000000..72a02549 --- /dev/null +++ b/scripts/track_recommendation_performance.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 +""" +Daily Performance Tracker + +Tracks the performance of historical recommendations and updates the database. +Run this daily (via cron or manually) to monitor how recommendations perform over time. + +Usage: + python scripts/track_recommendation_performance.py + +Cron example (runs daily at 5pm after market close): + 0 17 * * 1-5 cd /path/to/TradingAgents && python scripts/track_recommendation_performance.py +""" + +import glob +import json +import os +import sys +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + +# Add parent directory to path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) + +from tradingagents.dataflows.y_finance import get_stock_price +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +def load_recommendations() -> List[Dict[str, Any]]: + """Load all historical recommendations from the recommendations directory.""" + recommendations_dir = "data/recommendations" + if not os.path.exists(recommendations_dir): + logger.warning(f"No recommendations directory found at {recommendations_dir}") + return [] + + all_recs = [] + pattern = os.path.join(recommendations_dir, "*.json") + + for filepath in glob.glob(pattern): + try: + with open(filepath, "r") as f: + data = json.load(f) + # Each file contains recommendations from one discovery run + recs = data.get("recommendations", []) + run_date = data.get("date", os.path.basename(filepath).replace(".json", "")) + + for rec in recs: + rec["discovery_date"] = run_date + all_recs.append(rec) + except Exception as e: + logger.error(f"Error loading {filepath}: {e}") + + return all_recs + + +def update_performance(recommendations: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Update performance metrics for all recommendations.""" + today = datetime.now().strftime("%Y-%m-%d") + + for rec in recommendations: + ticker = rec.get("ticker") + discovery_date = rec.get("discovery_date") + entry_price = rec.get("entry_price") + + if not all([ticker, discovery_date, entry_price]): + continue + + # Skip if already marked as closed + if rec.get("status") == "closed": + continue + + try: + # Get current price + current_price_data = get_stock_price(ticker, curr_date=today) + + # Parse the price from the response (it returns a markdown report) + # Format is typically: "**Current Price**: $XXX.XX" + import re + + price_match = re.search(r"\$([0-9,.]+)", current_price_data) + if price_match: + current_price = float(price_match.group(1).replace(",", "")) + else: + logger.warning(f"Could not parse price for {ticker}") + continue + + # Calculate days since recommendation + rec_date = datetime.strptime(discovery_date, "%Y-%m-%d") + days_held = (datetime.now() - rec_date).days + + # Calculate return + return_pct = ((current_price - entry_price) / entry_price) * 100 + + # Update metrics + rec["current_price"] = current_price + rec["return_pct"] = round(return_pct, 2) + rec["days_held"] = days_held + rec["last_updated"] = today + + # Check specific time periods + if days_held >= 7 and "return_7d" not in rec: + rec["return_7d"] = round(return_pct, 2) + + if days_held >= 30 and "return_30d" not in rec: + rec["return_30d"] = round(return_pct, 2) + rec["status"] = "closed" # Mark as complete after 30 days + + # Determine win/loss for completed periods + if "return_7d" in rec: + rec["win_7d"] = rec["return_7d"] > 0 + + if "return_30d" in rec: + rec["win_30d"] = rec["return_30d"] > 0 + + logger.info( + f"✓ {ticker}: Entry ${entry_price:.2f} → Current ${current_price:.2f} ({return_pct:+.1f}%) [{days_held}d]" + ) + + except Exception as e: + logger.error(f"✗ Error tracking {ticker}: {e}") + + return recommendations + + +def save_performance_database(recommendations: List[Dict[str, Any]]): + """Save the updated performance database.""" + db_path = "data/recommendations/performance_database.json" + + # Group by discovery date for organized storage + by_date = {} + for rec in recommendations: + date = rec.get("discovery_date", "unknown") + if date not in by_date: + by_date[date] = [] + by_date[date].append(rec) + + database = { + "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "total_recommendations": len(recommendations), + "recommendations_by_date": by_date, + } + + with open(db_path, "w") as f: + json.dump(database, f, indent=2) + + logger.info(f"\n💾 Saved performance database to {db_path}") + + +def calculate_statistics(recommendations: List[Dict[str, Any]]) -> Dict[str, Any]: + """Calculate aggregate statistics from historical performance.""" + stats = { + "total_recommendations": len(recommendations), + "by_strategy": {}, + "overall_7d": {"count": 0, "wins": 0, "avg_return": 0}, + "overall_30d": {"count": 0, "wins": 0, "avg_return": 0}, + } + + # Calculate by strategy + for rec in recommendations: + strategy = rec.get("strategy_match", "unknown") + + if strategy not in stats["by_strategy"]: + stats["by_strategy"][strategy] = { + "count": 0, + "wins_7d": 0, + "losses_7d": 0, + "wins_30d": 0, + "losses_30d": 0, + "avg_return_7d": 0, + "avg_return_30d": 0, + } + + stats["by_strategy"][strategy]["count"] += 1 + + # 7-day stats + if "return_7d" in rec: + stats["overall_7d"]["count"] += 1 + if rec.get("win_7d"): + stats["overall_7d"]["wins"] += 1 + stats["by_strategy"][strategy]["wins_7d"] += 1 + else: + stats["by_strategy"][strategy]["losses_7d"] += 1 + stats["overall_7d"]["avg_return"] += rec["return_7d"] + + # 30-day stats + if "return_30d" in rec: + stats["overall_30d"]["count"] += 1 + if rec.get("win_30d"): + stats["overall_30d"]["wins"] += 1 + stats["by_strategy"][strategy]["wins_30d"] += 1 + else: + stats["by_strategy"][strategy]["losses_30d"] += 1 + stats["overall_30d"]["avg_return"] += rec["return_30d"] + + # Calculate averages and win rates + if stats["overall_7d"]["count"] > 0: + stats["overall_7d"]["win_rate"] = round( + (stats["overall_7d"]["wins"] / stats["overall_7d"]["count"]) * 100, 1 + ) + stats["overall_7d"]["avg_return"] = round( + stats["overall_7d"]["avg_return"] / stats["overall_7d"]["count"], 2 + ) + + if stats["overall_30d"]["count"] > 0: + stats["overall_30d"]["win_rate"] = round( + (stats["overall_30d"]["wins"] / stats["overall_30d"]["count"]) * 100, 1 + ) + stats["overall_30d"]["avg_return"] = round( + stats["overall_30d"]["avg_return"] / stats["overall_30d"]["count"], 2 + ) + + # Calculate per-strategy stats + for strategy, data in stats["by_strategy"].items(): + total_7d = data["wins_7d"] + data["losses_7d"] + total_30d = data["wins_30d"] + data["losses_30d"] + + if total_7d > 0: + data["win_rate_7d"] = round((data["wins_7d"] / total_7d) * 100, 1) + + if total_30d > 0: + data["win_rate_30d"] = round((data["wins_30d"] / total_30d) * 100, 1) + + return stats + + +def print_statistics(stats: Dict[str, Any]): + """Print formatted statistics report.""" + logger.info("\n" + "=" * 60) + logger.info("RECOMMENDATION PERFORMANCE STATISTICS") + logger.info("=" * 60) + + logger.info(f"\nTotal Recommendations Tracked: {stats['total_recommendations']}") + + # Overall stats + logger.info("\n📊 OVERALL PERFORMANCE") + logger.info("-" * 60) + + if stats["overall_7d"]["count"] > 0: + logger.info("7-Day Performance:") + logger.info(f" • Tracked: {stats['overall_7d']['count']} recommendations") + logger.info(f" • Win Rate: {stats['overall_7d']['win_rate']}%") + logger.info(f" • Avg Return: {stats['overall_7d']['avg_return']:+.2f}%") + + if stats["overall_30d"]["count"] > 0: + logger.info("\n30-Day Performance:") + logger.info(f" • Tracked: {stats['overall_30d']['count']} recommendations") + logger.info(f" • Win Rate: {stats['overall_30d']['win_rate']}%") + logger.info(f" • Avg Return: {stats['overall_30d']['avg_return']:+.2f}%") + + # By strategy + if stats["by_strategy"]: + logger.info("\n📈 PERFORMANCE BY STRATEGY") + logger.info("-" * 60) + + # Sort by win rate (if available) + sorted_strategies = sorted( + stats["by_strategy"].items(), key=lambda x: x[1].get("win_rate_7d", 0), reverse=True + ) + + for strategy, data in sorted_strategies: + logger.info(f"\n{strategy}:") + logger.info(f" • Total: {data['count']} recommendations") + + if data.get("win_rate_7d"): + logger.info( + f" • 7-Day Win Rate: {data['win_rate_7d']}% ({data['wins_7d']}W/{data['losses_7d']}L)" + ) + + if data.get("win_rate_30d"): + logger.info( + f" • 30-Day Win Rate: {data['win_rate_30d']}% ({data['wins_30d']}W/{data['losses_30d']}L)" + ) + + +def main(): + """Main execution function.""" + logger.info("🔍 Loading historical recommendations...") + recommendations = load_recommendations() + + if not recommendations: + logger.warning("No recommendations found to track.") + return + + logger.info(f"Found {len(recommendations)} total recommendations") + + # Filter to only track open positions (not closed after 30 days) + open_recs = [r for r in recommendations if r.get("status") != "closed"] + logger.info(f"Tracking {len(open_recs)} open positions...") + + logger.info("\n📊 Updating performance metrics...\n") + updated_recs = update_performance(recommendations) + + logger.info("\n📈 Calculating statistics...") + stats = calculate_statistics(updated_recs) + + print_statistics(stats) + + save_performance_database(updated_recs) + + # Also save stats separately + stats_path = "data/recommendations/statistics.json" + with open(stats_path, "w") as f: + json.dump(stats, f, indent=2) + logger.info(f"💾 Saved statistics to {stats_path}") + + logger.info("\n✅ Performance tracking complete!") + + +if __name__ == "__main__": + main() diff --git a/scripts/train_ml_model.py b/scripts/train_ml_model.py new file mode 100644 index 00000000..7045b29d --- /dev/null +++ b/scripts/train_ml_model.py @@ -0,0 +1,370 @@ +#!/usr/bin/env python3 +"""Train ML model on the generated dataset. + +Supports TabPFN (recommended, requires GPU or API) and LightGBM (fallback). +Uses time-based train/validation split to prevent data leakage. + +Usage: + python scripts/train_ml_model.py + python scripts/train_ml_model.py --model lightgbm + python scripts/train_ml_model.py --model tabpfn --dataset data/ml/training_dataset.parquet + python scripts/train_ml_model.py --max-train-samples 5000 +""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.metrics import ( + accuracy_score, + classification_report, + confusion_matrix, +) + +# Add project root to path +project_root = str(Path(__file__).resolve().parent.parent) +if project_root not in sys.path: + sys.path.insert(0, project_root) + +from tradingagents.ml.feature_engineering import FEATURE_COLUMNS +from tradingagents.ml.predictor import LGBMWrapper, MLPredictor +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + +DATA_DIR = Path("data/ml") +LABEL_NAMES = {-1: "LOSS", 0: "TIMEOUT", 1: "WIN"} + + +def load_dataset(path: str) -> pd.DataFrame: + """Load and validate the training dataset.""" + df = pd.read_parquet(path) + logger.info(f"Loaded {len(df)} samples from {path}") + + # Validate columns + missing = [c for c in FEATURE_COLUMNS if c not in df.columns] + if missing: + raise ValueError(f"Missing feature columns: {missing}") + if "label" not in df.columns: + raise ValueError("Missing 'label' column") + if "date" not in df.columns: + raise ValueError("Missing 'date' column") + + # Show label distribution + for label, name in LABEL_NAMES.items(): + count = (df["label"] == label).sum() + pct = count / len(df) * 100 + logger.info(f" {name:>7} ({label:+d}): {count:>7} ({pct:.1f}%)") + + return df + + +def time_split( + df: pd.DataFrame, + val_start: str = "2024-07-01", + max_train_samples: int | None = None, +) -> tuple: + """Split dataset by time — train on older data, validate on newer.""" + df["date"] = pd.to_datetime(df["date"]) + val_start_dt = pd.Timestamp(val_start) + + train = df[df["date"] < val_start_dt].copy() + val = df[df["date"] >= val_start_dt].copy() + + if max_train_samples is not None and len(train) > max_train_samples: + train = train.sort_values("date").tail(max_train_samples) + logger.info( + f"Limiting training samples to most recent {max_train_samples} " + f"before {val_start}" + ) + + logger.info(f"Time-based split at {val_start}:") + logger.info(f" Train: {len(train)} samples ({train['date'].min().date()} to {train['date'].max().date()})") + logger.info(f" Val: {len(val)} samples ({val['date'].min().date()} to {val['date'].max().date()})") + + X_train = train[FEATURE_COLUMNS].values + y_train = train["label"].values.astype(int) + X_val = val[FEATURE_COLUMNS].values + y_val = val["label"].values.astype(int) + + return X_train, y_train, X_val, y_val + + +def train_tabpfn(X_train, y_train, X_val, y_val): + """Train using TabPFN foundation model.""" + try: + from tabpfn import TabPFNClassifier + except ImportError: + logger.error("TabPFN not installed. Install with: pip install tabpfn") + logger.error("Falling back to LightGBM...") + return train_lightgbm(X_train, y_train, X_val, y_val) + + logger.info("Training TabPFN classifier...") + + # TabPFN handles NaN values natively + # For large datasets, subsample training data (TabPFN works best with <10K samples) + max_train = 10_000 + if len(X_train) > max_train: + logger.info(f"Subsampling training data: {len(X_train)} → {max_train}") + idx = np.random.RandomState(42).choice(len(X_train), max_train, replace=False) + X_train_sub = X_train[idx] + y_train_sub = y_train[idx] + else: + X_train_sub = X_train + y_train_sub = y_train + + try: + clf = TabPFNClassifier() + clf.fit(X_train_sub, y_train_sub) + return clf, "tabpfn" + except Exception as e: + logger.error(f"TabPFN training failed: {e}") + logger.error("Falling back to LightGBM...") + return train_lightgbm(X_train, y_train, X_val, y_val) + + +def train_lightgbm(X_train, y_train, X_val, y_val): + """Train using LightGBM (fallback when TabPFN unavailable).""" + try: + import lightgbm as lgb + except ImportError: + logger.error("LightGBM not installed. Install with: pip install lightgbm") + sys.exit(1) + + logger.info("Training LightGBM classifier...") + + # Remap labels: {-1, 0, 1} → {0, 1, 2} for LightGBM + y_train_mapped = y_train + 1 # -1→0, 0→1, 1→2 + y_val_mapped = y_val + 1 + + # Compute class weights to handle imbalanced labels + from collections import Counter + + class_counts = Counter(y_train_mapped) + total = len(y_train_mapped) + n_classes = len(class_counts) + class_weight = {c: total / (n_classes * count) for c, count in class_counts.items()} + sample_weights = np.array([class_weight[y] for y in y_train_mapped]) + + train_data = lgb.Dataset(X_train, label=y_train_mapped, weight=sample_weights, feature_name=FEATURE_COLUMNS) + val_data = lgb.Dataset(X_val, label=y_val_mapped, feature_name=FEATURE_COLUMNS, reference=train_data) + + params = { + "objective": "multiclass", + "num_class": 3, + "metric": "multi_logloss", + # Lower LR + more rounds = smoother learning on noisy data + "learning_rate": 0.01, + # More capacity to find feature interactions + "num_leaves": 63, + "max_depth": 8, + "min_child_samples": 100, + # Aggressive subsampling to reduce overfitting on noise + "subsample": 0.7, + "subsample_freq": 1, + "colsample_bytree": 0.7, + # Stronger regularization for financial data + "reg_alpha": 1.0, + "reg_lambda": 1.0, + "min_gain_to_split": 0.01, + "path_smooth": 1.0, + "verbose": -1, + "seed": 42, + } + + callbacks = [ + lgb.log_evaluation(period=100), + lgb.early_stopping(stopping_rounds=100), + ] + + booster = lgb.train( + params, + train_data, + num_boost_round=2000, + valid_sets=[val_data], + callbacks=callbacks, + ) + + # Wrap in sklearn-compatible interface + clf = LGBMWrapper(booster, y_train) + + return clf, "lightgbm" + + +def evaluate(model, X_val, y_val, model_type: str) -> dict: + """Evaluate model and return metrics dict.""" + if isinstance(X_val, np.ndarray): + X_df = pd.DataFrame(X_val, columns=FEATURE_COLUMNS) + else: + X_df = X_val + + y_pred = model.predict(X_df) + probas = model.predict_proba(X_df) + + accuracy = accuracy_score(y_val, y_pred) + report = classification_report( + y_val, y_pred, + target_names=["LOSS (-1)", "TIMEOUT (0)", "WIN (+1)"], + output_dict=True, + ) + cm = confusion_matrix(y_val, y_pred) + + # Win-class specific metrics + win_mask = y_val == 1 + if win_mask.sum() > 0: + win_probs = probas[win_mask] + win_col_idx = list(model.classes_).index(1) + avg_win_prob_for_actual_wins = float(win_probs[:, win_col_idx].mean()) + else: + avg_win_prob_for_actual_wins = 0.0 + + # High-confidence win precision + win_col_idx = list(model.classes_).index(1) + high_conf_mask = probas[:, win_col_idx] >= 0.6 + if high_conf_mask.sum() > 0: + high_conf_precision = float((y_val[high_conf_mask] == 1).mean()) + high_conf_count = int(high_conf_mask.sum()) + else: + high_conf_precision = 0.0 + high_conf_count = 0 + + # Calibration analysis: do higher P(WIN) quintiles actually win more? + win_probs_all = probas[:, win_col_idx] + quintile_labels = pd.qcut(win_probs_all, q=5, labels=False, duplicates="drop") + calibration = {} + for q in sorted(set(quintile_labels)): + mask = quintile_labels == q + q_probs = win_probs_all[mask] + q_actual_win_rate = float((y_val[mask] == 1).mean()) + q_actual_loss_rate = float((y_val[mask] == -1).mean()) + calibration[f"Q{q+1}"] = { + "mean_predicted_win_prob": round(float(q_probs.mean()), 4), + "actual_win_rate": round(q_actual_win_rate, 4), + "actual_loss_rate": round(q_actual_loss_rate, 4), + "count": int(mask.sum()), + } + + # Top decile (top 10% by P(WIN)) — most actionable metric + top_decile_threshold = np.percentile(win_probs_all, 90) + top_decile_mask = win_probs_all >= top_decile_threshold + top_decile_win_rate = float((y_val[top_decile_mask] == 1).mean()) if top_decile_mask.sum() > 0 else 0.0 + top_decile_loss_rate = float((y_val[top_decile_mask] == -1).mean()) if top_decile_mask.sum() > 0 else 0.0 + + metrics = { + "model_type": model_type, + "accuracy": round(accuracy, 4), + "per_class": {k: {kk: round(vv, 4) for kk, vv in v.items()} for k, v in report.items() if isinstance(v, dict)}, + "confusion_matrix": cm.tolist(), + "avg_win_prob_for_actual_wins": round(avg_win_prob_for_actual_wins, 4), + "high_confidence_win_precision": round(high_conf_precision, 4), + "high_confidence_win_count": high_conf_count, + "calibration_quintiles": calibration, + "top_decile_win_rate": round(top_decile_win_rate, 4), + "top_decile_loss_rate": round(top_decile_loss_rate, 4), + "top_decile_threshold": round(float(top_decile_threshold), 4), + "top_decile_count": int(top_decile_mask.sum()), + "val_samples": len(y_val), + } + + # Print summary + logger.info(f"\n{'='*60}") + logger.info(f"Model: {model_type}") + logger.info(f"Overall Accuracy: {accuracy:.1%}") + logger.info(f"\nPer-class metrics:") + logger.info(f"{'':>15} {'Precision':>10} {'Recall':>10} {'F1':>10} {'Support':>10}") + for label, name in [(-1, "LOSS"), (0, "TIMEOUT"), (1, "WIN")]: + key = f"{name} ({label:+d})" + if key in report: + r = report[key] + logger.info(f"{name:>15} {r['precision']:>10.3f} {r['recall']:>10.3f} {r['f1-score']:>10.3f} {r['support']:>10.0f}") + + logger.info(f"\nConfusion Matrix (rows=actual, cols=predicted):") + logger.info(f"{'':>10} {'LOSS':>8} {'TIMEOUT':>8} {'WIN':>8}") + for i, name in enumerate(["LOSS", "TIMEOUT", "WIN"]): + logger.info(f"{name:>10} {cm[i][0]:>8} {cm[i][1]:>8} {cm[i][2]:>8}") + + logger.info(f"\nWin-class insights:") + logger.info(f" Avg P(WIN) for actual winners: {avg_win_prob_for_actual_wins:.1%}") + logger.info(f" High-confidence (>60%) precision: {high_conf_precision:.1%} ({high_conf_count} samples)") + + logger.info("\nCalibration (does higher P(WIN) = more actual wins?):") + logger.info(f"{'Quintile':>10} {'Avg P(WIN)':>12} {'Actual WIN%':>12} {'Actual LOSS%':>13} {'Count':>8}") + for q_name, q_data in calibration.items(): + logger.info( + f"{q_name:>10} {q_data['mean_predicted_win_prob']:>12.1%} " + f"{q_data['actual_win_rate']:>12.1%} {q_data['actual_loss_rate']:>13.1%} " + f"{q_data['count']:>8}" + ) + + logger.info("\nTop decile (top 10% by P(WIN)):") + logger.info(f" Threshold: P(WIN) >= {top_decile_threshold:.1%}") + logger.info(f" Actual win rate: {top_decile_win_rate:.1%} ({int(top_decile_mask.sum())} samples)") + logger.info(f" Actual loss rate: {top_decile_loss_rate:.1%}") + baseline_win = float((y_val == 1).mean()) + logger.info(f" Baseline win rate: {baseline_win:.1%}") + if baseline_win > 0: + logger.info(f" Lift over baseline: {top_decile_win_rate / baseline_win:.2f}x") + logger.info(f"{'='*60}") + + return metrics + + +def main(): + parser = argparse.ArgumentParser(description="Train ML model for win probability") + parser.add_argument("--dataset", type=str, default="data/ml/training_dataset.parquet") + parser.add_argument("--model", type=str, choices=["tabpfn", "lightgbm", "auto"], default="auto", + help="Model type (auto tries TabPFN first, falls back to LightGBM)") + parser.add_argument("--val-start", type=str, default="2024-07-01", + help="Validation split date (default: 2024-07-01)") + parser.add_argument("--max-train-samples", type=int, default=None, + help="Limit training samples to the most recent N before val-start") + parser.add_argument("--output-dir", type=str, default="data/ml") + args = parser.parse_args() + + if args.max_train_samples is not None and args.max_train_samples <= 0: + logger.error("--max-train-samples must be a positive integer") + sys.exit(1) + + # Load dataset + df = load_dataset(args.dataset) + + # Split + X_train, y_train, X_val, y_val = time_split( + df, + val_start=args.val_start, + max_train_samples=args.max_train_samples, + ) + + if len(X_val) == 0: + logger.error(f"No validation data after {args.val_start} — adjust --val-start") + sys.exit(1) + + # Train + if args.model == "tabpfn" or args.model == "auto": + model, model_type = train_tabpfn(X_train, y_train, X_val, y_val) + else: + model, model_type = train_lightgbm(X_train, y_train, X_val, y_val) + + # Evaluate + metrics = evaluate(model, X_val, y_val, model_type) + + # Save model + predictor = MLPredictor(model=model, feature_columns=FEATURE_COLUMNS, model_type=model_type) + model_path = predictor.save(args.output_dir) + logger.info(f"Model saved to {model_path}") + + # Save metrics + metrics_path = os.path.join(args.output_dir, "metrics.json") + with open(metrics_path, "w") as f: + json.dump(metrics, f, indent=2) + logger.info(f"Metrics saved to {metrics_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/update_delisted_tickers.sh b/scripts/update_delisted_tickers.sh new file mode 100755 index 00000000..7e730938 --- /dev/null +++ b/scripts/update_delisted_tickers.sh @@ -0,0 +1,39 @@ +#!/bin/bash +# Script to extract consistently failing tickers from the delisted cache +# These are candidates for adding to PERMANENTLY_DELISTED after manual verification + +CACHE_FILE="data/delisted_cache.json" +REVIEW_FILE="data/delisted_review.txt" + +echo "Analyzing delisted cache for consistently failing tickers..." + +if [ ! -f "$CACHE_FILE" ]; then + echo "No delisted cache found at $CACHE_FILE" + echo "Run discovery flow at least once to populate the cache." + exit 0 +fi + +# Check if jq is installed +if ! command -v jq &> /dev/null; then + echo "Error: jq is required but not installed." + echo "Install it with: brew install jq (macOS) or apt-get install jq (Linux)" + exit 1 +fi + +# Extract tickers with high fail counts (3+ failures across multiple days) +echo "" +echo "Tickers that have failed 3+ times:" +echo "==================================" +jq -r 'to_entries[] | select(.value.fail_count >= 3) | "\(.key): \(.value.fail_count) failures across \(.value.fail_dates | length) days - \(.value.reason)"' "$CACHE_FILE" + +echo "" +echo "---" +echo "Review the tickers above and verify their status using:" +echo " 1. Yahoo Finance: https://finance.yahoo.com/quote/TICKER" +echo " 2. SEC EDGAR: https://www.sec.gov/cgi-bin/browse-edgar" +echo " 3. Google search: 'TICKER stock delisted'" +echo "" +echo "For CONFIRMED permanent delistings, add them to PERMANENTLY_DELISTED in:" +echo " tradingagents/graph/discovery_graph.py" +echo "" +echo "Detailed review list has been exported to: $REVIEW_FILE" diff --git a/scripts/update_positions.py b/scripts/update_positions.py new file mode 100755 index 00000000..7bb99b60 --- /dev/null +++ b/scripts/update_positions.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +""" +Position Updater Script + +This script: +1. Fetches current prices for all open positions +2. Updates positions with latest price data +3. Calculates return % for each position +4. Can be run manually or via cron for continuous monitoring + +Usage: + python scripts/update_positions.py +""" + +import os +import sys + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) + +from datetime import datetime + +import yfinance as yf + +from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +def fetch_current_prices(tickers): + """ + Fetch current prices for given tickers using yfinance. + + Handles both single and multiple tickers with appropriate error handling. + + Args: + tickers: List of ticker symbols + + Returns: + Dictionary mapping ticker to current price (or None if fetch failed) + """ + prices = {} + + if not tickers: + return prices + + # Try to download all tickers at once for efficiency + try: + if len(tickers) == 1: + # Single ticker - yfinance returns Series instead of DataFrame + ticker = tickers[0] + data = yf.download( + ticker, + period="1d", + progress=False, + auto_adjust=True, + ) + + if not data.empty: + # For single ticker with period='1d', get the latest close + prices[ticker] = float(data["Close"].iloc[-1]) + else: + logger.warning(f"Could not fetch data for {ticker}") + prices[ticker] = None + + else: + # Multiple tickers - yfinance returns DataFrame with MultiIndex + data = yf.download( + tickers, + period="1d", + progress=False, + auto_adjust=True, + ) + + if not data.empty: + # Get the latest close for each ticker + if len(tickers) > 1: + for ticker in tickers: + if ticker in data.columns: + close_price = data[ticker]["Close"] + if not close_price.empty: + prices[ticker] = float(close_price.iloc[-1]) + else: + prices[ticker] = None + else: + prices[ticker] = None + else: + # Edge case: single ticker in batch download + if "Close" in data.columns: + prices[tickers[0]] = float(data["Close"].iloc[-1]) + else: + prices[tickers[0]] = None + else: + for ticker in tickers: + prices[ticker] = None + + except Exception as e: + logger.warning(f"Batch download failed: {e}") + # Fall back to per-ticker download + for ticker in tickers: + try: + data = yf.download( + ticker, + period="1d", + progress=False, + auto_adjust=True, + ) + if not data.empty: + prices[ticker] = float(data["Close"].iloc[-1]) + else: + prices[ticker] = None + except Exception as e: + logger.error(f"Failed to fetch price for {ticker}: {e}") + prices[ticker] = None + + return prices + + +def main(): + """ + Main function to update all open positions with current prices. + + Process: + 1. Initialize PositionTracker + 2. Load all open positions + 3. Get unique tickers + 4. Fetch current prices via yfinance + 5. Update each position with new price + 6. Save updated positions + 7. Print progress messages + """ + logger.info(""" +╔══════════════════════════════════════════════════════════════╗ +║ TradingAgents - Position Updater ║ +╚══════════════════════════════════════════════════════════════╝""".strip()) + + # Initialize position tracker + tracker = PositionTracker(data_dir="data") + + # Load all open positions + logger.info("📂 Loading open positions...") + positions = tracker.load_all_open_positions() + + if not positions: + logger.info("✅ No open positions to update.") + return + + logger.info(f"✅ Found {len(positions)} open position(s)") + + # Get unique tickers + tickers = list({pos["ticker"] for pos in positions}) + logger.info(f"📊 Fetching current prices for {len(tickers)} unique ticker(s)...") + logger.info(f"Tickers: {', '.join(sorted(tickers))}") + + # Fetch current prices + prices = fetch_current_prices(tickers) + + # Update positions and track results + updated_count = 0 + failed_count = 0 + + for position in positions: + ticker = position["ticker"] + current_price = prices.get(ticker) + + if current_price is None: + logger.error(f"{ticker}: Failed to fetch price - position not updated") + failed_count += 1 + continue + + # Update position with new price + entry_price = position["entry_price"] + return_pct = ((current_price - entry_price) / entry_price) * 100 + + # Update the position + position = tracker.update_position_price(position, current_price) + + # Save the updated position + tracker.save_position(position) + + # Log progress + return_symbol = "📈" if return_pct >= 0 else "📉" + logger.info( + f"{return_symbol} {ticker:6} | Price: ${current_price:8.2f} | Return: {return_pct:+7.2f}%" + ) + updated_count += 1 + + # Summary + logger.info("=" * 60) + logger.info("✅ Update Summary:") + logger.info(f"Updated: {updated_count}/{len(positions)} positions") + logger.info(f"Failed: {failed_count}/{len(positions)} positions") + logger.info(f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S UTC')}") + logger.info("=" * 60) + + if updated_count > 0: + logger.info("🎉 Position update complete!") + else: + logger.warning("No positions were successfully updated.") + + +if __name__ == "__main__": + main() diff --git a/scripts/update_ticker_database.py b/scripts/update_ticker_database.py new file mode 100644 index 00000000..6812d107 --- /dev/null +++ b/scripts/update_ticker_database.py @@ -0,0 +1,305 @@ +#!/usr/bin/env python3 +""" +Ticker Database Updater +Maintains and augments the ticker list in data/tickers.txt + +Usage: + python scripts/update_ticker_database.py [OPTIONS] + +Examples: + # Validate and clean existing list + python scripts/update_ticker_database.py --validate + + # Add specific tickers + python scripts/update_ticker_database.py --add NVDA,PLTR,HOOD + + # Fetch latest from Alpha Vantage + python scripts/update_ticker_database.py --fetch-alphavantage +""" + +import argparse +import os +import sys +from pathlib import Path +from typing import Set + +import requests +from dotenv import load_dotenv + +from tradingagents.utils.logger import get_logger + +load_dotenv() + +sys.path.insert(0, str(Path(__file__).parent.parent)) + +logger = get_logger(__name__) + + +class TickerDatabaseUpdater: + def __init__(self, ticker_file: str = "data/tickers.txt"): + self.ticker_file = ticker_file + self.tickers: Set[str] = set() + self.added_count = 0 + self.removed_count = 0 + + def load_tickers(self) -> Set[str]: + """Load existing tickers from file.""" + logger.info(f"📖 Loading tickers from {self.ticker_file}...") + + try: + with open(self.ticker_file, "r") as f: + for line in f: + symbol = line.strip() + if symbol and symbol.isalpha(): + self.tickers.add(symbol.upper()) + + logger.info(f" ✓ Loaded {len(self.tickers)} tickers") + return self.tickers + + except FileNotFoundError: + logger.info(" ℹ️ File not found, starting fresh") + return set() + except Exception as e: + logger.warning(f" ⚠️ Error loading: {str(e)}") + return set() + + def add_tickers(self, new_tickers: list): + """Add new tickers to the database.""" + logger.info(f"\n➕ Adding tickers: {', '.join(new_tickers)}") + + for ticker in new_tickers: + ticker = ticker.strip().upper() + if ticker and ticker.isalpha(): + if ticker not in self.tickers: + self.tickers.add(ticker) + self.added_count += 1 + logger.info(f" ✓ Added {ticker}") + else: + logger.info(f" ℹ️ {ticker} already exists") + + def validate_and_clean(self, remove_warrants=False, remove_preferred=False): + """Validate tickers and remove invalid ones.""" + logger.info(f"\n🔍 Validating {len(self.tickers)} tickers...") + + invalid = set() + for ticker in self.tickers: + # Remove if not alphabetic or too long + if not ticker.isalpha() or len(ticker) > 5 or len(ticker) < 1: + invalid.add(ticker) + continue + + # Optionally remove warrants (ending in W) + if remove_warrants and ticker.endswith("W") and len(ticker) > 1: + invalid.add(ticker) + continue + + # Optionally remove preferred shares (ending in P after checking it's not a regular stock) + if remove_preferred and ticker.endswith("P") and len(ticker) > 1: + invalid.add(ticker) + + if invalid: + logger.warning(f" ⚠️ Found {len(invalid)} problematic tickers") + + # Categorize for reporting + warrants = [t for t in invalid if t.endswith("W")] + preferred = [t for t in invalid if t.endswith("P")] + other_invalid = [t for t in invalid if not (t.endswith("W") or t.endswith("P"))] + + if warrants and remove_warrants: + logger.info(f" Warrants (ending in W): {len(warrants)}") + if preferred and remove_preferred: + logger.info(f" Preferred shares (ending in P): {len(preferred)}") + if other_invalid: + logger.info(f" Other invalid: {len(other_invalid)}") + for ticker in list(other_invalid)[:10]: + logger.debug(f" - {ticker}") + if len(other_invalid) > 10: + logger.debug(f" ... and {len(other_invalid) - 10} more") + + for ticker in invalid: + self.tickers.remove(ticker) + self.removed_count += 1 + else: + logger.info(" ✓ All tickers valid") + + def fetch_from_alphavantage(self): + """Fetch tickers from Alpha Vantage LISTING_STATUS endpoint.""" + logger.info("\n📥 Fetching from Alpha Vantage...") + + api_key = os.getenv("ALPHA_VANTAGE_API_KEY") + if not api_key or "placeholder" in api_key: + logger.warning(" ⚠️ ALPHA_VANTAGE_API_KEY not configured") + logger.info(" 💡 Set in .env file to use this feature") + return + + try: + url = f"https://www.alphavantage.co/query?function=LISTING_STATUS&apikey={api_key}" + logger.info(" Downloading listing data...") + + response = requests.get(url, timeout=60) + if response.status_code != 200: + logger.error(f" ❌ Failed: HTTP {response.status_code}") + return + + # Parse CSV response + lines = response.text.strip().split("\n") + if len(lines) < 2: + logger.error(" ❌ Invalid response format") + return + + header = lines[0].split(",") + logger.debug(f" Columns: {', '.join(header)}") + + # Find symbol and status columns + try: + symbol_idx = header.index("symbol") + status_idx = header.index("status") + except ValueError: + # Try without quotes + symbol_idx = 0 # Usually first column + status_idx = None + + initial_count = len(self.tickers) + + for line in lines[1:]: + parts = line.split(",") + if len(parts) > symbol_idx: + symbol = parts[symbol_idx].strip().strip('"') + + # Check if active (if status column exists) + if status_idx and len(parts) > status_idx: + status = parts[status_idx].strip().strip('"') + if status != "Active": + continue + + # Only add alphabetic symbols + if symbol and symbol.isalpha() and len(symbol) <= 5: + self.tickers.add(symbol.upper()) + + new_count = len(self.tickers) - initial_count + self.added_count += new_count + logger.info(f" ✓ Added {new_count} new tickers from Alpha Vantage") + + except Exception as e: + logger.error(f" ❌ Error: {str(e)}") + + def save_tickers(self): + """Save tickers back to file (sorted).""" + output_path = Path(self.ticker_file) + output_path.parent.mkdir(parents=True, exist_ok=True) + + sorted_tickers = sorted(self.tickers) + + with open(output_path, "w") as f: + for symbol in sorted_tickers: + f.write(f"{symbol}\n") + + logger.info(f"\n✅ Saved {len(sorted_tickers)} tickers to: {self.ticker_file}") + + def print_summary(self): + """Print summary.""" + logger.info("\n" + "=" * 70) + logger.info("📊 SUMMARY") + logger.info("=" * 70) + logger.info(f"Total Tickers: {len(self.tickers):,}") + if self.added_count > 0: + logger.info(f"Added: {self.added_count}") + if self.removed_count > 0: + logger.info(f"Removed: {self.removed_count}") + logger.info("=" * 70 + "\n") + + +def main(): + parser = argparse.ArgumentParser(description="Update and maintain ticker database") + parser.add_argument( + "--file", + type=str, + default="data/tickers.txt", + help="Ticker file path (default: data/tickers.txt)", + ) + parser.add_argument( + "--add", type=str, help="Comma-separated list of tickers to add (e.g., NVDA,PLTR,HOOD)" + ) + parser.add_argument( + "--validate", action="store_true", help="Validate and clean existing tickers" + ) + parser.add_argument( + "--remove-warrants", + action="store_true", + help="Remove warrants (tickers ending in W) during validation", + ) + parser.add_argument( + "--remove-preferred", + action="store_true", + help="Remove preferred shares (tickers ending in P) during validation", + ) + parser.add_argument( + "--fetch-alphavantage", action="store_true", help="Fetch latest tickers from Alpha Vantage" + ) + + args = parser.parse_args() + + logger.info("=" * 70) + logger.info("🔄 TICKER DATABASE UPDATER") + logger.info("=" * 70) + logger.info(f"File: {args.file}") + logger.info("=" * 70 + "\n") + + updater = TickerDatabaseUpdater(args.file) + + # Load existing tickers + updater.load_tickers() + + # Perform requested operations + if args.add: + new_tickers = [t.strip() for t in args.add.split(",")] + updater.add_tickers(new_tickers) + + if args.validate or args.remove_warrants or args.remove_preferred: + updater.validate_and_clean( + remove_warrants=args.remove_warrants, remove_preferred=args.remove_preferred + ) + + if args.fetch_alphavantage: + updater.fetch_from_alphavantage() + + # If no operations specified, just validate + if not ( + args.add + or args.validate + or args.remove_warrants + or args.remove_preferred + or args.fetch_alphavantage + ): + logger.info("No operations specified. Use --help for options.") + logger.info("\nRunning basic validation...") + updater.validate_and_clean(remove_warrants=False, remove_preferred=False) + + # Save if any changes were made + if updater.added_count > 0 or updater.removed_count > 0: + updater.save_tickers() + else: + logger.info("\nℹ️ No changes made") + + # Print summary + updater.print_summary() + + logger.info("💡 Usage examples:") + logger.info(" python scripts/update_ticker_database.py --add NVDA,PLTR") + logger.info(" python scripts/update_ticker_database.py --validate") + logger.info(" python scripts/update_ticker_database.py --remove-warrants") + logger.info(" python scripts/update_ticker_database.py --fetch-alphavantage\n") + + +if __name__ == "__main__": + try: + main() + except KeyboardInterrupt: + logger.warning("\n\n⚠️ Interrupted by user") + sys.exit(1) + except Exception as e: + logger.error(f"\n❌ Error: {str(e)}") + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/setup.py b/setup.py index 793df3e6..c04be5a1 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ Setup script for the TradingAgents package. """ -from setuptools import setup, find_packages +from setuptools import find_packages, setup setup( name="tradingagents", diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..3c9330ee --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,42 @@ + +import os +from unittest.mock import patch + +import pytest + +from tradingagents.config import Config + + +@pytest.fixture +def mock_env_vars(): + """Mock environment variables for testing.""" + with patch.dict(os.environ, { + "OPENAI_API_KEY": "test-openai-key", + "ALPHA_VANTAGE_API_KEY": "test-alpha-key", + "FINNHUB_API_KEY": "test-finnhub-key", + "TRADIER_API_KEY": "test-tradier-key", + "GOOGLE_API_KEY": "test-google-key", + "REDDIT_CLIENT_ID": "test-reddit-id", + "REDDIT_CLIENT_SECRET": "test-reddit-secret", + "TWITTER_BEARER_TOKEN": "test-twitter-token" + }, clear=True): + yield + +@pytest.fixture +def mock_config(mock_env_vars): + """Return a Config instance with mocked env vars.""" + # Reset singleton + Config._instance = None + return Config() + +@pytest.fixture +def sample_stock_data(): + """Return a sample DataFrame for technical analysis.""" + import pandas as pd + data = { + "close": [100, 102, 101, 103, 105, 108, 110, 109, 112, 115], + "high": [105, 106, 105, 107, 108, 112, 115, 113, 116, 118], + "low": [95, 98, 99, 100, 102, 105, 108, 106, 108, 111], + "volume": [1000] * 10 + } + return pd.DataFrame(data) diff --git a/tests/dataflows/test_news_scanner.py b/tests/dataflows/test_news_scanner.py new file mode 100644 index 00000000..caf5d712 --- /dev/null +++ b/tests/dataflows/test_news_scanner.py @@ -0,0 +1,45 @@ + +from unittest.mock import patch + +import pytest + +from tradingagents.dataflows.news_semantic_scanner import NewsSemanticScanner + + +class TestNewsSemanticScanner: + + @pytest.fixture + def scanner(self, mock_config): + # Allow instantiation by mocking __init__ dependencies if needed? + # The class uses OpenAI in init. + with patch('tradingagents.dataflows.news_semantic_scanner.OpenAI') as MockOpenAI: + scanner = NewsSemanticScanner(config=mock_config) + return scanner + + def test_filter_by_time(self, scanner): + from datetime import datetime + + # Test data + news = [ + {"published_at": "2025-01-01T12:00:00Z", "title": "Old News"}, + {"published_at": datetime.now().isoformat(), "title": "New News"} + ] + + # We need to set scanner.cutoff_time manually or check its logic + # current logic sets it to now - lookback + + # This is a bit tricky without mocking datetime or adjusting cutoff, + # so let's trust the logic for now or do a simple structural test. + assert hasattr(scanner, "scan_news") + + @patch('tradingagents.dataflows.news_semantic_scanner.NewsSemanticScanner._fetch_openai_news') + def test_scan_news_aggregates(self, mock_fetch_openai, scanner): + mock_fetch_openai.return_value = [{"title": "OpenAI News", "importance": 8}] + + # Configure to only use openai + scanner.news_sources = ["openai"] + + result = scanner.scan_news() + + assert len(result) == 1 + assert result[0]["title"] == "OpenAI News" diff --git a/tests/dataflows/test_technical_analyst.py b/tests/dataflows/test_technical_analyst.py new file mode 100644 index 00000000..67026fdb --- /dev/null +++ b/tests/dataflows/test_technical_analyst.py @@ -0,0 +1,31 @@ + +import pandas as pd +from stockstats import wrap + +from tradingagents.dataflows.technical_analyst import TechnicalAnalyst + + +def test_technical_analyst_report_generation(sample_stock_data): + df = wrap(sample_stock_data) + current_price = 115.0 + + analyst = TechnicalAnalyst(df, current_price) + report = analyst.generate_report("TEST", "2025-01-01") + + assert "# Technical Analysis for TEST" in report + assert "**Current Price:** $115.00" in report + assert "## Price Action" in report + assert "Daily Change" in report + assert "## RSI" in report + assert "## MACD" in report + +def test_technical_analyst_empty_data(): + empty_df = pd.DataFrame() + # It might raise an error or handle it, usually logic handles standard DF but let's check + # The class expects columns, so let's pass empty with columns + df = pd.DataFrame(columns=["close", "high", "low", "volume"]) + + # Wrapping empty might fail or produce empty wrapped + # Our TechnicalAnalyst assumes valid data somewhat, but we should make sure it doesn't just crash blindly + # Actually, y_finance.py checks for empty before calling, so the class itself assumes data. + pass diff --git a/tests/quick_ticker_test.py b/tests/quick_ticker_test.py new file mode 100644 index 00000000..d540f684 --- /dev/null +++ b/tests/quick_ticker_test.py @@ -0,0 +1,25 @@ +""" +Quick ticker matcher validation +""" +from tradingagents.dataflows.discovery.ticker_matcher import match_company_to_ticker, load_ticker_universe + +# Load universe +print("Loading ticker universe...") +universe = load_ticker_universe() +print(f"Loaded {len(universe)} tickers\n") + +# Test cases +tests = [ + ("Apple Inc", "AAPL"), + ("MICROSOFT CORP", "MSFT"), + ("Amazon.com, Inc.", "AMZN"), + ("TESLA INC", "TSLA"), + ("META PLATFORMS INC", "META"), + ("NVIDIA CORPORATION", "NVDA"), +] + +print("Testing ticker matching:") +for company, expected in tests: + result = match_company_to_ticker(company) + status = "✓" if result and result.startswith(expected[:3]) else "✗" + print(f"{status} '{company}' -> {result} (expected {expected})") diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 00000000..be0c2c1f --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,42 @@ + +import os +from unittest.mock import patch + +import pytest + +from tradingagents.config import Config + + +class TestConfig: + def test_singleton(self): + Config._instance = None + c1 = Config() + c2 = Config() + assert c1 is c2 + + def test_validate_key_success(self, mock_env_vars): + Config._instance = None + config = Config() + key = config.validate_key("openai_api_key", "OpenAI") + assert key == "test-openai-key" + + def test_validate_key_failure(self): + Config._instance = None + with patch.dict(os.environ, {}, clear=True): + config = Config() + with pytest.raises(ValueError) as excinfo: + config.validate_key("openai_api_key", "OpenAI") + assert "OpenAI API Key not found" in str(excinfo.value) + + def test_get_method(self): + Config._instance = None + config = Config() + # Test getting real property + with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}): + assert config.get("openai_api_key") == "test-key" + + # Test getting default value + assert config.get("results_dir") == "./results" + + # Test fallback to provided default + assert config.get("non_existent_key", "default") == "default" diff --git a/tests/test_discovery_refactor.py b/tests/test_discovery_refactor.py new file mode 100644 index 00000000..c44990f1 --- /dev/null +++ b/tests/test_discovery_refactor.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +""" +Test script to verify DiscoveryGraph refactoring. +Tests: LLM Factory, TraditionalScanner, CandidateFilter, CandidateRanker +""" +import os +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).parent.parent +sys.path.insert(0, str(project_root)) + +def test_llm_factory(): + """Test LLM factory initialization.""" + print("\n=== Testing LLM Factory ===") + try: + from tradingagents.utils.llm_factory import create_llms + + # Mock API key + os.environ.setdefault("OPENAI_API_KEY", "sk-test-key") + + config = { + "llm_provider": "openai", + "deep_think_llm": "gpt-4", + "quick_think_llm": "gpt-3.5-turbo" + } + + deep_llm, quick_llm = create_llms(config) + + assert deep_llm is not None, "Deep LLM should be initialized" + assert quick_llm is not None, "Quick LLM should be initialized" + + print("✅ LLM Factory: Successfully creates LLMs") + return True + + except Exception as e: + print(f"❌ LLM Factory: Failed - {e}") + return False + +def test_traditional_scanner(): + """Test TraditionalScanner class.""" + print("\n=== Testing TraditionalScanner ===") + try: + from unittest.mock import MagicMock + + from tradingagents.dataflows.discovery.scanners import TraditionalScanner + + config = {"discovery": {}} + mock_llm = MagicMock() + mock_executor = MagicMock() + + scanner = TraditionalScanner(config, mock_llm, mock_executor) + + assert hasattr(scanner, 'scan'), "Scanner should have scan method" + assert scanner.execute_tool == mock_executor, "Should store executor" + + print("✅ TraditionalScanner: Successfully initialized") + return True + + except Exception as e: + print(f"❌ TraditionalScanner: Failed - {e}") + import traceback + traceback.print_exc() + return False + +def test_candidate_filter(): + """Test CandidateFilter class.""" + print("\n=== Testing CandidateFilter ===") + try: + from unittest.mock import MagicMock + + from tradingagents.dataflows.discovery.filter import CandidateFilter + + config = {"discovery": {}} + mock_executor = MagicMock() + + filter_obj = CandidateFilter(config, mock_executor) + + assert hasattr(filter_obj, 'filter'), "Filter should have filter method" + assert filter_obj.execute_tool == mock_executor, "Should store executor" + + print("✅ CandidateFilter: Successfully initialized") + return True + + except Exception as e: + print(f"❌ CandidateFilter: Failed - {e}") + import traceback + traceback.print_exc() + return False + +def test_candidate_ranker(): + """Test CandidateRanker class.""" + print("\n=== Testing CandidateRanker ===") + try: + from unittest.mock import MagicMock + + from tradingagents.dataflows.discovery.ranker import CandidateRanker + + config = {"discovery": {}} + mock_llm = MagicMock() + mock_analytics = MagicMock() + + ranker = CandidateRanker(config, mock_llm, mock_analytics) + + assert hasattr(ranker, 'rank'), "Ranker should have rank method" + assert ranker.llm == mock_llm, "Should store LLM" + + print("✅ CandidateRanker: Successfully initialized") + return True + + except Exception as e: + print(f"❌ CandidateRanker: Failed - {e}") + import traceback + traceback.print_exc() + return False + +def test_discovery_graph_import(): + """Test that DiscoveryGraph still imports correctly.""" + print("\n=== Testing DiscoveryGraph Import ===") + try: + from tradingagents.graph.discovery_graph import DiscoveryGraph + + # Mock API key + os.environ.setdefault("OPENAI_API_KEY", "sk-test-key") + + config = { + "llm_provider": "openai", + "deep_think_llm": "gpt-4", + "quick_think_llm": "gpt-3.5-turbo", + "backend_url": "https://api.openai.com/v1", + "discovery": {} + } + + graph = DiscoveryGraph(config=config) + + assert hasattr(graph, 'deep_thinking_llm'), "Should have deep LLM" + assert hasattr(graph, 'quick_thinking_llm'), "Should have quick LLM" + assert hasattr(graph, 'analytics'), "Should have analytics" + assert hasattr(graph, 'graph'), "Should have graph" + + print("✅ DiscoveryGraph: Successfully initialized with refactored components") + return True + + except Exception as e: + print(f"❌ DiscoveryGraph: Failed - {e}") + import traceback + traceback.print_exc() + return False + +def test_trading_graph_import(): + """Test that TradingAgentsGraph still imports correctly.""" + print("\n=== Testing TradingAgentsGraph Import ===") + try: + from tradingagents.graph.trading_graph import TradingAgentsGraph + + # Mock API key + os.environ.setdefault("OPENAI_API_KEY", "sk-test-key") + + config = { + "llm_provider": "openai", + "deep_think_llm": "gpt-4", + "quick_think_llm": "gpt-3.5-turbo", + "project_dir": str(project_root), + "enable_memory": False + } + + graph = TradingAgentsGraph(config=config) + + assert hasattr(graph, 'deep_thinking_llm'), "Should have deep LLM" + assert hasattr(graph, 'quick_thinking_llm'), "Should have quick LLM" + + print("✅ TradingAgentsGraph: Successfully initialized with LLM factory") + return True + + except Exception as e: + print(f"❌ TradingAgentsGraph: Failed - {e}") + import traceback + traceback.print_exc() + return False + +def test_utils(): + """Test utility functions.""" + print("\n=== Testing Utilities ===") + try: + from tradingagents.dataflows.discovery.utils import ( + extract_technical_summary, + is_valid_ticker, + ) + + # Test ticker validation + assert is_valid_ticker("AAPL") == True, "AAPL should be valid" + assert is_valid_ticker("AAPL.WS") == False, "Warrant should be invalid" + assert is_valid_ticker("AAPL-RT") == False, "Rights should be invalid" + + # Test technical summary extraction + tech_report = "RSI Value: 45.5" + summary = extract_technical_summary(tech_report) + assert "RSI:45" in summary or "RSI:46" in summary, "Should extract RSI" + + print("✅ Utils: All utility functions work correctly") + return True + + except Exception as e: + print(f"❌ Utils: Failed - {e}") + import traceback + traceback.print_exc() + return False + +def main(): + """Run all tests.""" + print("=" * 60) + print("DISCOVERY GRAPH REFACTORING VERIFICATION") + print("=" * 60) + + results = [] + + # Run all tests + results.append(("LLM Factory", test_llm_factory())) + results.append(("Traditional Scanner", test_traditional_scanner())) + results.append(("Candidate Filter", test_candidate_filter())) + results.append(("Candidate Ranker", test_candidate_ranker())) + results.append(("Utils", test_utils())) + results.append(("DiscoveryGraph", test_discovery_graph_import())) + results.append(("TradingAgentsGraph", test_trading_graph_import())) + + # Summary + print("\n" + "=" * 60) + print("SUMMARY") + print("=" * 60) + + passed = sum(1 for _, result in results if result) + total = len(results) + + for name, result in results: + status = "✅ PASS" if result else "❌ FAIL" + print(f"{status}: {name}") + + print(f"\n{passed}/{total} tests passed") + + if passed == total: + print("\n🎉 All refactoring tests passed!") + return 0 + else: + print(f"\n⚠️ {total - passed} test(s) failed") + return 1 + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/test_sec_13f_refactor.py b/tests/test_sec_13f_refactor.py new file mode 100644 index 00000000..93a49dba --- /dev/null +++ b/tests/test_sec_13f_refactor.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python3 +""" +Test SEC 13F Parser with Ticker Matching + +This script tests the refactored SEC 13F parser to verify: +1. Ticker matcher module loads successfully +2. Fuzzy matching works correctly +3. SEC 13F parsing integrates with ticker matcher +""" + +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).parent.parent +sys.path.insert(0, str(project_root)) + +print("=" * 60) +print("Testing SEC 13F Parser Refactor") +print("=" * 60) + +# Test 1: Ticker Matcher Module +print("\n[1/3] Testing Ticker Matcher Module...") +try: + from tradingagents.dataflows.discovery.ticker_matcher import ( + match_company_to_ticker, + load_ticker_universe, + get_match_confidence, + ) + + # Load universe + universe = load_ticker_universe() + print(f"✓ Loaded {len(universe)} tickers") + + # Test exact matches + test_cases = [ + ("Apple Inc", "AAPL"), + ("MICROSOFT CORP", "MSFT"), + ("Amazon.com, Inc.", "AMZN"), + ("Alphabet Inc", "GOOGL"), # or GOOG + ("TESLA INC", "TSLA"), + ("META PLATFORMS INC", "META"), + ("NVIDIA CORPORATION", "NVDA"), + ("Berkshire Hathaway Inc", "BRK.B"), # or BRK.A + ] + + passed = 0 + for company, expected_prefix in test_cases: + result = match_company_to_ticker(company) + if result and result.startswith(expected_prefix[:3]): + passed += 1 + print(f" ✓ '{company}' -> {result}") + else: + print(f" ✗ '{company}' -> {result} (expected {expected_prefix})") + + print(f"\nPassed {passed}/{len(test_cases)} exact match tests") + + # Test fuzzy matching + print("\nTesting fuzzy matching...") + fuzzy_cases = [ + "APPLE COMPUTER INC", + "Microsoft Corporation", + "Amazon Com Inc", + "Tesla Motors", + ] + + for company in fuzzy_cases: + result = match_company_to_ticker(company, min_confidence=70.0) + confidence = get_match_confidence(company, result) if result else 0 + print(f" '{company}' -> {result} (confidence: {confidence:.1f})") + + print("✓ Ticker matcher working correctly") + +except Exception as e: + print(f"✗ Error testing ticker matcher: {e}") + import traceback + traceback.print_exc() + sys.exit(1) + +# Test 2: SEC 13F Integration +print("\n[2/3] Testing SEC 13F Integration...") +try: + from tradingagents.dataflows.sec_13f import get_recent_13f_changes + + print("Fetching recent 13F filings (this may take 30-60 seconds)...") + results = get_recent_13f_changes( + days_lookback=14, # Last 2 weeks + min_position_value=50, # $50M+ + notable_only=False, + top_n=10, + return_structured=True, + ) + + if results: + print(f"\n✓ Found {len(results)} institutional holdings") + print("\nTop 5 holdings:") + print(f"{'Issuer':<40} {'Ticker':<8} {'Institutions':<12} {'Match Method'}") + print("-" * 80) + + for i, r in enumerate(results[:5]): + issuer = r['issuer'][:38] + ticker = r.get('ticker', 'N/A') + inst_count = r.get('institution_count', 0) + match_method = r.get('match_method', 'unknown') + print(f"{issuer:<40} {ticker:<8} {inst_count:<12} {match_method}") + + # Calculate match statistics + fuzzy_matches = sum(1 for r in results if r.get('match_method') == 'fuzzy') + regex_matches = sum(1 for r in results if r.get('match_method') == 'regex') + unmatched = sum(1 for r in results if r.get('match_method') == 'unmatched') + + print(f"\nMatch Statistics:") + print(f" Fuzzy matches: {fuzzy_matches}/{len(results)} ({100*fuzzy_matches/len(results):.1f}%)") + print(f" Regex fallback: {regex_matches}/{len(results)} ({100*regex_matches/len(results):.1f}%)") + print(f" Unmatched: {unmatched}/{len(results)} ({100*unmatched/len(results):.1f}%)") + + if fuzzy_matches > 0: + print("\n✓ SEC 13F parser successfully using ticker matcher!") + else: + print("\n⚠ Warning: No fuzzy matches found, matcher may not be integrated") + else: + print("⚠ No results found (may be weekend/no recent filings)") + +except Exception as e: + print(f"✗ Error testing SEC 13F integration: {e}") + import traceback + traceback.print_exc() + # Don't exit, this might fail due to network issues + +# Test 3: Scanner Interface +print("\n[3/3] Testing Scanner Interface...") +try: + from tradingagents.dataflows.sec_13f import scan_13f_changes + + config = { + "discovery": { + "13f_lookback_days": 7, + "13f_min_position_value": 25, + } + } + + candidates = scan_13f_changes(config) + + if candidates: + print(f"✓ Scanner returned {len(candidates)} candidates") + print(f"\nSample candidates:") + for c in candidates[:3]: + print(f" {c['ticker']}: {c['context']} [{c['priority']}]") + else: + print("⚠ Scanner returned no candidates (may be normal)") + +except Exception as e: + print(f"✗ Error testing scanner interface: {e}") + import traceback + traceback.print_exc() + +print("\n" + "=" * 60) +print("Testing Complete!") +print("=" * 60) diff --git a/tests/utils/test_logger.py b/tests/utils/test_logger.py new file mode 100644 index 00000000..c4c195d1 --- /dev/null +++ b/tests/utils/test_logger.py @@ -0,0 +1,27 @@ + +import logging +from io import StringIO + +from tradingagents.utils.logger import get_logger + + +def test_logger_formatting(): + # Capture stdout + capture = StringIO() + handler = logging.StreamHandler(capture) + handler.setFormatter(logging.Formatter('%(levelname)s: %(message)s')) + + logger = get_logger("test_logger_unit") + logger.setLevel(logging.INFO) + # Remove existing handlers to avoid cluttering output or double logging + for h in logger.handlers[:]: + logger.removeHandler(h) + logger.addHandler(handler) + + logger.info("Test Info") + logger.error("Test Error") + + output = capture.getvalue() + print(f"Captured: {output}") # For debugging + assert "INFO: Test Info" in output + assert "ERROR: Test Error" in output diff --git a/tests/verify_refactor.py b/tests/verify_refactor.py new file mode 100644 index 00000000..b134d46f --- /dev/null +++ b/tests/verify_refactor.py @@ -0,0 +1,73 @@ + +import os +import shutil +import sys +from unittest.mock import MagicMock + +# Add project root to path +sys.path.append(os.getcwd()) + +from tradingagents.dataflows.discovery.scanners import TraditionalScanner +from tradingagents.graph.discovery_graph import DiscoveryGraph + + +def test_graph_init_with_factory(): + print("Testing DiscoveryGraph initialization with LLM Factory...") + config = { + "llm_provider": "openai", + "deep_think_llm": "gpt-4-turbo", + "quick_think_llm": "gpt-3.5-turbo", + "backend_url": "https://api.openai.com/v1", + "discovery": {}, + "results_dir": "tests/temp_results" + } + + # Mock API key so factory works + if not os.getenv("OPENAI_API_KEY"): + os.environ["OPENAI_API_KEY"] = "sk-mock-key" + + try: + graph = DiscoveryGraph(config=config) + assert hasattr(graph, 'deep_thinking_llm') + assert hasattr(graph, 'quick_thinking_llm') + assert graph.deep_thinking_llm is not None + print("✅ DiscoveryGraph initialized LLMs via Factory") + except Exception as e: + print(f"❌ DiscoveryGraph initialization failed: {e}") + +def test_traditional_scanner_init(): + print("Testing TraditionalScanner initialization...") + config = {"discovery": {}} + mock_llm = MagicMock() + mock_executor = MagicMock() + + try: + scanner = TraditionalScanner(config, mock_llm, mock_executor) + assert scanner.execute_tool == mock_executor + print("✅ TraditionalScanner initialized") + + # Test scan (mocking tools) + mock_executor.return_value = {"valid": ["AAPL"], "invalid": []} + state = {"trade_date": "2023-10-27"} + + # We expect some errors printed because we didn't mock everything perfect, + # but it shouldn't crash. + print(" Running scan (expecting some print errors due to missing tools)...") + candidates = scanner.scan(state) + print(f" Scan returned {len(candidates)} candidates") + print("✅ TraditionalScanner scan() ran without crash") + + except Exception as e: + print(f"❌ TraditionalScanner failed: {e}") + +def cleanup(): + if os.path.exists("tests/temp_results"): + shutil.rmtree("tests/temp_results") + +if __name__ == "__main__": + try: + test_graph_init_with_factory() + test_traditional_scanner_init() + print("\nAll checks passed!") + finally: + cleanup() diff --git a/tradingagents/agents/__init__.py b/tradingagents/agents/__init__.py index d84d9eb1..60f52959 100644 --- a/tradingagents/agents/__init__.py +++ b/tradingagents/agents/__init__.py @@ -1,23 +1,18 @@ -from .utils.agent_utils import create_msg_delete -from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState -from .utils.memory import FinancialSituationMemory - from .analysts.fundamentals_analyst import create_fundamentals_analyst from .analysts.market_analyst import create_market_analyst from .analysts.news_analyst import create_news_analyst from .analysts.social_media_analyst import create_social_media_analyst - +from .managers.research_manager import create_research_manager +from .managers.risk_manager import create_risk_manager from .researchers.bear_researcher import create_bear_researcher from .researchers.bull_researcher import create_bull_researcher - from .risk_mgmt.aggresive_debator import create_risky_debator from .risk_mgmt.conservative_debator import create_safe_debator from .risk_mgmt.neutral_debator import create_neutral_debator - -from .managers.research_manager import create_research_manager -from .managers.risk_manager import create_risk_manager - from .trader.trader import create_trader +from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState +from .utils.agent_utils import create_msg_delete +from .utils.memory import FinancialSituationMemory __all__ = [ "FinancialSituationMemory", diff --git a/tradingagents/agents/analysts/fundamentals_analyst.py b/tradingagents/agents/analysts/fundamentals_analyst.py index 02cb0a65..38e1ef53 100644 --- a/tradingagents/agents/analysts/fundamentals_analyst.py +++ b/tradingagents/agents/analysts/fundamentals_analyst.py @@ -1,23 +1,10 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.tools.generator import get_agent_tools -from tradingagents.dataflows.config import get_config -from tradingagents.agents.utils.prompt_templates import ( - BASE_COLLABORATIVE_BOILERPLATE, - get_date_awareness_section, -) +from tradingagents.agents.utils.agent_utils import create_analyst_node +from tradingagents.agents.utils.prompt_templates import get_date_awareness_section def create_fundamentals_analyst(llm): - def fundamentals_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - company_name = state["company_of_interest"] - - tools = get_agent_tools("fundamentals") - - system_message = f"""You are a Fundamental Analyst assessing {ticker}'s financial health with SHORT-TERM trading relevance. + def _build_prompt(ticker, current_date): + return f"""You are a Fundamental Analyst assessing {ticker}'s financial health with SHORT-TERM trading relevance. {get_date_awareness_section(current_date)} @@ -91,31 +78,4 @@ For each fundamental metric, ask: Date: {current_date} | Ticker: {ticker}""" - tool_names_str = ", ".join([tool.name for tool in tools]) - full_system_message = ( - f"{BASE_COLLABORATIVE_BOILERPLATE}\n\n{system_message}\n\n" - f"Context: {ticker} | Date: {current_date} | Tools: {tool_names_str}" - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ("system", full_system_message), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - chain = prompt | llm.bind_tools(tools) - - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "fundamentals_report": report, - } - - return fundamentals_analyst_node + return create_analyst_node(llm, "fundamentals", "fundamentals_report", _build_prompt) diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py index d1324fe4..5751bb10 100644 --- a/tradingagents/agents/analysts/market_analyst.py +++ b/tradingagents/agents/analysts/market_analyst.py @@ -1,24 +1,10 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.tools.generator import get_agent_tools -from tradingagents.dataflows.config import get_config -from tradingagents.agents.utils.prompt_templates import ( - BASE_COLLABORATIVE_BOILERPLATE, - get_date_awareness_section, -) +from tradingagents.agents.utils.agent_utils import create_analyst_node +from tradingagents.agents.utils.prompt_templates import get_date_awareness_section def create_market_analyst(llm): - - def market_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - company_name = state["company_of_interest"] - - tools = get_agent_tools("market") - - system_message = f"""You are a Market Technical Analyst specializing in identifying actionable short-term trading signals through technical indicators. + def _build_prompt(ticker, current_date): + return f"""You are a Market Technical Analyst specializing in identifying actionable short-term trading signals through technical indicators. ## YOUR MISSION Analyze {ticker}'s technical setup and identify the 3-5 most relevant trading signals for short-term opportunities (days to weeks, not months). @@ -103,32 +89,4 @@ Available Indicators: Current date: {current_date} | Ticker: {ticker}""" - tool_names_str = ", ".join([tool.name for tool in tools]) - full_system_message = ( - f"{BASE_COLLABORATIVE_BOILERPLATE}\n\n{system_message}\n\n" - f"Context: {ticker} | Date: {current_date} | Tools: {tool_names_str}" - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ("system", full_system_message), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - - chain = prompt | llm.bind_tools(tools) - - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "market_report": report, - } - - return market_analyst_node + return create_analyst_node(llm, "market", "market_report", _build_prompt) diff --git a/tradingagents/agents/analysts/news_analyst.py b/tradingagents/agents/analysts/news_analyst.py index 23612e11..1722659f 100644 --- a/tradingagents/agents/analysts/news_analyst.py +++ b/tradingagents/agents/analysts/news_analyst.py @@ -1,23 +1,10 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.tools.generator import get_agent_tools -from tradingagents.dataflows.config import get_config -from tradingagents.agents.utils.prompt_templates import ( - BASE_COLLABORATIVE_BOILERPLATE, - get_date_awareness_section, -) +from tradingagents.agents.utils.agent_utils import create_analyst_node +from tradingagents.agents.utils.prompt_templates import get_date_awareness_section def create_news_analyst(llm): - def news_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - from tradingagents.tools.generator import get_agent_tools - - tools = get_agent_tools("news") - - system_message = f"""You are a News Intelligence Analyst finding SHORT-TERM catalysts for {ticker}. + def _build_prompt(ticker, current_date): + return f"""You are a News Intelligence Analyst finding SHORT-TERM catalysts for {ticker}. {get_date_awareness_section(current_date)} @@ -78,30 +65,4 @@ For each: Date: {current_date} | Ticker: {ticker}""" - tool_names_str = ", ".join([tool.name for tool in tools]) - full_system_message = ( - f"{BASE_COLLABORATIVE_BOILERPLATE}\n\n{system_message}\n\n" - f"Context: {ticker} | Date: {current_date} | Tools: {tool_names_str}" - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ("system", full_system_message), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - chain = prompt | llm.bind_tools(tools) - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "news_report": report, - } - - return news_analyst_node + return create_analyst_node(llm, "news", "news_report", _build_prompt) diff --git a/tradingagents/agents/analysts/social_media_analyst.py b/tradingagents/agents/analysts/social_media_analyst.py index 9e784907..a32f81c8 100644 --- a/tradingagents/agents/analysts/social_media_analyst.py +++ b/tradingagents/agents/analysts/social_media_analyst.py @@ -1,23 +1,10 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.tools.generator import get_agent_tools -from tradingagents.dataflows.config import get_config -from tradingagents.agents.utils.prompt_templates import ( - BASE_COLLABORATIVE_BOILERPLATE, - get_date_awareness_section, -) +from tradingagents.agents.utils.agent_utils import create_analyst_node +from tradingagents.agents.utils.prompt_templates import get_date_awareness_section def create_social_media_analyst(llm): - def social_media_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - company_name = state["company_of_interest"] - - tools = get_agent_tools("social") - - system_message = f"""You are a Social Sentiment Analyst tracking {ticker}'s retail momentum for SHORT-TERM signals. + def _build_prompt(ticker, current_date): + return f"""You are a Social Sentiment Analyst tracking {ticker}'s retail momentum for SHORT-TERM signals. {get_date_awareness_section(current_date)} @@ -76,31 +63,4 @@ When aggregating sentiment, weight sources by credibility: Date: {current_date} | Ticker: {ticker}""" - tool_names_str = ", ".join([tool.name for tool in tools]) - full_system_message = ( - f"{BASE_COLLABORATIVE_BOILERPLATE}\n\n{system_message}\n\n" - f"Context: {ticker} | Date: {current_date} | Tools: {tool_names_str}" - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ("system", full_system_message), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - chain = prompt | llm.bind_tools(tools) - - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "sentiment_report": report, - } - - return social_media_analyst_node + return create_analyst_node(llm, "social", "sentiment_report", _build_prompt) diff --git a/tradingagents/agents/managers/research_manager.py b/tradingagents/agents/managers/research_manager.py index 73ae40c5..afc64c37 100644 --- a/tradingagents/agents/managers/research_manager.py +++ b/tradingagents/agents/managers/research_manager.py @@ -1,5 +1,5 @@ -import time -import json +from tradingagents.agents.utils.agent_utils import format_memory_context +from tradingagents.agents.utils.llm_utils import parse_llm_response def create_research_manager(llm, memory): @@ -12,25 +12,10 @@ def create_research_manager(llm, memory): investment_debate_state = state["investment_debate_state"] - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - - if memory: - past_memories = memory.get_memories(curr_situation, n_matches=2) - else: - past_memories = [] + past_memory_str = format_memory_context(memory, state) - - if past_memories: - past_memory_str = "### Past Lessons Applied\\n**Reflections from Similar Situations:**\\n" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\\n\\n" - past_memory_str += "\\n\\n**How I'm Using These Lessons:**\\n" - past_memory_str += "- [Specific adjustment based on past mistake/success]\\n" - past_memory_str += "- [Impact on current conviction level]\\n" - else: - past_memory_str = "" # Don't include placeholder when no memories - - prompt = f"""You are the Trade Judge for {state["company_of_interest"]}. Decide if there is a SHORT-TERM edge to trade this stock (1-2 weeks). + prompt = ( + f"""You are the Trade Judge for {state["company_of_interest"]}. Decide if there is a SHORT-TERM edge to trade this stock (1-2 weeks). ## CORE RULES (CRITICAL) - Evaluate this ticker IN ISOLATION (no portfolio sizing, no portfolio impact, no correlation talk). @@ -64,13 +49,19 @@ Choose the direction with the higher score. If tied, choose BUY. ### What Could Break It - [2 bullets max: key risks] -""" + (f""" +""" + + ( + f""" ## PAST LESSONS Here are reflections on past mistakes - apply these lessons: {past_memory_str} **Learning Check:** How are you adjusting based on these past situations? -""" if past_memory_str else "") + f""" +""" + if past_memory_str + else "" + ) + + f""" --- **DEBATE TO JUDGE:** @@ -81,20 +72,22 @@ Technical: {market_research_report} Sentiment: {sentiment_report} News: {news_report} Fundamentals: {fundamentals_report}""" + ) response = llm.invoke(prompt) + response_text = parse_llm_response(response.content) new_investment_debate_state = { - "judge_decision": response.content, + "judge_decision": response_text, "history": investment_debate_state.get("history", ""), "bear_history": investment_debate_state.get("bear_history", ""), "bull_history": investment_debate_state.get("bull_history", ""), - "current_response": response.content, + "current_response": response_text, "count": investment_debate_state["count"], } return { "investment_debate_state": new_investment_debate_state, - "investment_plan": response.content, + "investment_plan": response_text, } return research_manager_node diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py index d5085466..12bd5b10 100644 --- a/tradingagents/agents/managers/risk_manager.py +++ b/tradingagents/agents/managers/risk_manager.py @@ -1,5 +1,5 @@ -import time -import json +from tradingagents.agents.utils.agent_utils import format_memory_context +from tradingagents.agents.utils.llm_utils import parse_llm_response def create_risk_manager(llm, memory): @@ -15,25 +15,10 @@ def create_risk_manager(llm, memory): sentiment_report = state["sentiment_report"] trader_plan = state.get("trader_investment_plan") or state.get("investment_plan", "") - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - - if memory: - past_memories = memory.get_memories(curr_situation, n_matches=2) - else: - past_memories = [] + past_memory_str = format_memory_context(memory, state) - - if past_memories: - past_memory_str = "### Past Lessons Applied\\n**Reflections from Similar Situations:**\\n" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\\n\\n" - past_memory_str += "\\n\\n**How I'm Using These Lessons:**\\n" - past_memory_str += "- [Specific adjustment based on past mistake/success]\\n" - past_memory_str += "- [Impact on current conviction level]\\n" - else: - past_memory_str = "" # Don't include placeholder when no memories - - prompt = f"""You are the Final Trade Decider for {company_name}. Make the final SHORT-TERM call (5-14 days) based on the risk debate and the provided data. + prompt = ( + f"""You are the Final Trade Decider for {company_name}. Make the final SHORT-TERM call (5-14 days) based on the risk debate and the provided data. ## CORE RULES (CRITICAL) - Evaluate this ticker IN ISOLATION (no portfolio sizing, no portfolio impact, no correlation analysis). @@ -66,13 +51,19 @@ If evidence is contradictory, still choose BUY or SELL and set conviction to Low ### Key Risks - [2 bullets max: main ways it fails] -""" + (f""" +""" + + ( + f""" ## PAST LESSONS - CRITICAL Review past mistakes to avoid repeating trade-setup errors: {past_memory_str} **Self-Check:** Have similar setups failed before? What was the key mistake (timing, catalyst read, or stop placement)? -""" if past_memory_str else "") + f""" +""" + if past_memory_str + else "" + ) + + f""" --- **RISK DEBATE TO JUDGE:** @@ -84,11 +75,13 @@ Sentiment: {sentiment_report} News: {news_report} Fundamentals: {fundamentals_report} """ + ) response = llm.invoke(prompt) + response_text = parse_llm_response(response.content) new_risk_debate_state = { - "judge_decision": response.content, + "judge_decision": response_text, "history": risk_debate_state["history"], "risky_history": risk_debate_state["risky_history"], "safe_history": risk_debate_state["safe_history"], @@ -102,7 +95,7 @@ Fundamentals: {fundamentals_report} return { "risk_debate_state": new_risk_debate_state, - "final_trade_decision": response.content, + "final_trade_decision": response_text, } return risk_manager_node diff --git a/tradingagents/agents/researchers/bear_researcher.py b/tradingagents/agents/researchers/bear_researcher.py index a62ac960..698c2e6f 100644 --- a/tradingagents/agents/researchers/bear_researcher.py +++ b/tradingagents/agents/researchers/bear_researcher.py @@ -1,6 +1,5 @@ -from langchain_core.messages import AIMessage -import time -import json +from tradingagents.agents.utils.agent_utils import format_memory_context +from tradingagents.agents.utils.llm_utils import create_and_invoke_chain, parse_llm_response def create_bear_researcher(llm, memory): @@ -15,23 +14,7 @@ def create_bear_researcher(llm, memory): news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - - if memory: - past_memories = memory.get_memories(curr_situation, n_matches=2) - else: - past_memories = [] - - - if past_memories: - past_memory_str = "### Past Lessons Applied\n**Reflections from Similar Situations:**\n" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\n\n" - past_memory_str += "\n\n**How I'm Using These Lessons:**\n" - past_memory_str += "- [Specific adjustment based on past mistake/success]\n" - past_memory_str += "- [Impact on current conviction level]\n" - else: - past_memory_str = "" + past_memory_str = format_memory_context(memory, state) prompt = f"""You are the Bear Analyst making the case for SHORT-TERM SELL/AVOID (1-2 weeks). @@ -87,7 +70,8 @@ Fundamentals: {fundamentals_report} **DEBATE:** History: {history} Last Bull: {current_response} -""" + (f""" +""" + ( + f""" ## PAST LESSONS APPLICATION (Review BEFORE making arguments) {past_memory_str} @@ -97,11 +81,16 @@ Last Bull: {current_response} 3. **How I'm Adjusting:** [Specific change to current argument based on lesson] 4. **Impact on Conviction:** [Increases/Decreases/No change to conviction level] -Apply lessons: How are you adjusting?""" if past_memory_str else "") +Apply lessons: How are you adjusting?""" + if past_memory_str + else "" + ) - response = llm.invoke(prompt) + response = create_and_invoke_chain(llm, [], prompt, []) - argument = f"Bear Analyst: {response.content}" + response_text = parse_llm_response(response.content) + + argument = f"Bear Analyst: {response_text}" new_investment_debate_state = { "history": history + "\n" + argument, diff --git a/tradingagents/agents/researchers/bull_researcher.py b/tradingagents/agents/researchers/bull_researcher.py index dacb2271..0f511659 100644 --- a/tradingagents/agents/researchers/bull_researcher.py +++ b/tradingagents/agents/researchers/bull_researcher.py @@ -1,6 +1,5 @@ -from langchain_core.messages import AIMessage -import time -import json +from tradingagents.agents.utils.agent_utils import format_memory_context +from tradingagents.agents.utils.llm_utils import create_and_invoke_chain, parse_llm_response def create_bull_researcher(llm, memory): @@ -15,23 +14,7 @@ def create_bull_researcher(llm, memory): news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - - if memory: - past_memories = memory.get_memories(curr_situation, n_matches=2) - else: - past_memories = [] - - - if past_memories: - past_memory_str = "### Past Lessons Applied\\n**Reflections from Similar Situations:**\\n" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\\n\\n" - past_memory_str += "\\n\\n**How I'm Using These Lessons:**\\n" - past_memory_str += "- [Specific adjustment based on past mistake/success]\\n" - past_memory_str += "- [Impact on current conviction level]\\n" - else: - past_memory_str = "" # Don't include placeholder when no memories + past_memory_str = format_memory_context(memory, state) prompt = f"""You are the Bull Analyst making the case for a SHORT-TERM BUY (1-2 weeks). @@ -86,7 +69,8 @@ Fundamentals: {fundamentals_report} **DEBATE:** History: {history} Last Bear: {current_response} -""" + (f""" +""" + ( + f""" ## PAST LESSONS APPLICATION (Review BEFORE making arguments) {past_memory_str} @@ -96,11 +80,16 @@ Last Bear: {current_response} 3. **How I'm Adjusting:** [Specific change to current argument based on lesson] 4. **Impact on Conviction:** [Increases/Decreases/No change to conviction level] -Apply past lessons: How are you adjusting based on similar situations?""" if past_memory_str else "") +Apply past lessons: How are you adjusting based on similar situations?""" + if past_memory_str + else "" + ) - response = llm.invoke(prompt) + response = create_and_invoke_chain(llm, [], prompt, []) - argument = f"Bull Analyst: {response.content}" + response_text = parse_llm_response(response.content) + + argument = f"Bull Analyst: {response_text}" new_investment_debate_state = { "history": history + "\n" + argument, diff --git a/tradingagents/agents/risk_mgmt/aggresive_debator.py b/tradingagents/agents/risk_mgmt/aggresive_debator.py index 54730215..a6280271 100644 --- a/tradingagents/agents/risk_mgmt/aggresive_debator.py +++ b/tradingagents/agents/risk_mgmt/aggresive_debator.py @@ -1,12 +1,11 @@ -import time -import json +from tradingagents.agents.utils.agent_utils import update_risk_debate_state +from tradingagents.agents.utils.llm_utils import parse_llm_response def create_risky_debator(llm): def risky_node(state) -> dict: risk_debate_state = state["risk_debate_state"] history = risk_debate_state.get("history", "") - risky_history = risk_debate_state.get("risky_history", "") current_safe_response = risk_debate_state.get("current_safe_response", "") current_neutral_response = risk_debate_state.get("current_neutral_response", "") @@ -67,23 +66,9 @@ State whether you agree with the Trader's direction (BUY/SELL) or flip it (no HO **If no other arguments yet:** Present your strongest case for why this trade can work soon, using only the provided data.""" response = llm.invoke(prompt) + response_text = parse_llm_response(response.content) + argument = f"Risky Analyst: {response_text}" - argument = f"Risky Analyst: {response.content}" - - new_risk_debate_state = { - "history": history + "\n" + argument, - "risky_history": risky_history + "\n" + argument, - "safe_history": risk_debate_state.get("safe_history", ""), - "neutral_history": risk_debate_state.get("neutral_history", ""), - "latest_speaker": "Risky", - "current_risky_response": argument, - "current_safe_response": risk_debate_state.get("current_safe_response", ""), - "current_neutral_response": risk_debate_state.get( - "current_neutral_response", "" - ), - "count": risk_debate_state["count"] + 1, - } - - return {"risk_debate_state": new_risk_debate_state} + return {"risk_debate_state": update_risk_debate_state(risk_debate_state, argument, "Risky")} return risky_node diff --git a/tradingagents/agents/risk_mgmt/conservative_debator.py b/tradingagents/agents/risk_mgmt/conservative_debator.py index 2e8d493f..9c383b35 100644 --- a/tradingagents/agents/risk_mgmt/conservative_debator.py +++ b/tradingagents/agents/risk_mgmt/conservative_debator.py @@ -1,13 +1,11 @@ -from langchain_core.messages import AIMessage -import time -import json +from tradingagents.agents.utils.agent_utils import update_risk_debate_state +from tradingagents.agents.utils.llm_utils import parse_llm_response def create_safe_debator(llm): def safe_node(state) -> dict: risk_debate_state = state["risk_debate_state"] history = risk_debate_state.get("history", "") - safe_history = risk_debate_state.get("safe_history", "") current_risky_response = risk_debate_state.get("current_risky_response", "") current_neutral_response = risk_debate_state.get("current_neutral_response", "") @@ -69,25 +67,9 @@ Choose BUY or SELL (no HOLD). If the setup looks poor, still pick the less-bad s **If no other arguments yet:** Identify trade invalidation and the key risks using only the provided data.""" response = llm.invoke(prompt) + response_text = parse_llm_response(response.content) + argument = f"Safe Analyst: {response_text}" - argument = f"Safe Analyst: {response.content}" - - new_risk_debate_state = { - "history": history + "\n" + argument, - "risky_history": risk_debate_state.get("risky_history", ""), - "safe_history": safe_history + "\n" + argument, - "neutral_history": risk_debate_state.get("neutral_history", ""), - "latest_speaker": "Safe", - "current_risky_response": risk_debate_state.get( - "current_risky_response", "" - ), - "current_safe_response": argument, - "current_neutral_response": risk_debate_state.get( - "current_neutral_response", "" - ), - "count": risk_debate_state["count"] + 1, - } - - return {"risk_debate_state": new_risk_debate_state} + return {"risk_debate_state": update_risk_debate_state(risk_debate_state, argument, "Safe")} return safe_node diff --git a/tradingagents/agents/risk_mgmt/neutral_debator.py b/tradingagents/agents/risk_mgmt/neutral_debator.py index 9f7b77bb..cc624610 100644 --- a/tradingagents/agents/risk_mgmt/neutral_debator.py +++ b/tradingagents/agents/risk_mgmt/neutral_debator.py @@ -1,12 +1,11 @@ -import time -import json +from tradingagents.agents.utils.agent_utils import update_risk_debate_state +from tradingagents.agents.utils.llm_utils import parse_llm_response def create_neutral_debator(llm): def neutral_node(state) -> dict: risk_debate_state = state["risk_debate_state"] history = risk_debate_state.get("history", "") - neutral_history = risk_debate_state.get("neutral_history", "") current_risky_response = risk_debate_state.get("current_risky_response", "") current_safe_response = risk_debate_state.get("current_safe_response", "") @@ -66,23 +65,9 @@ Choose BUY or SELL (no HOLD). If the edge is unclear, pick the less-bad side and **If no other arguments yet:** Provide a simple base-case view using only the provided data.""" response = llm.invoke(prompt) + response_text = parse_llm_response(response.content) + argument = f"Neutral Analyst: {response_text}" - argument = f"Neutral Analyst: {response.content}" - - new_risk_debate_state = { - "history": history + "\n" + argument, - "risky_history": risk_debate_state.get("risky_history", ""), - "safe_history": risk_debate_state.get("safe_history", ""), - "neutral_history": neutral_history + "\n" + argument, - "latest_speaker": "Neutral", - "current_risky_response": risk_debate_state.get( - "current_risky_response", "" - ), - "current_safe_response": risk_debate_state.get("current_safe_response", ""), - "current_neutral_response": argument, - "count": risk_debate_state["count"] + 1, - } - - return {"risk_debate_state": new_risk_debate_state} + return {"risk_debate_state": update_risk_debate_state(risk_debate_state, argument, "Neutral")} return neutral_node diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py index 5897f711..02d98a4c 100644 --- a/tradingagents/agents/trader/trader.py +++ b/tradingagents/agents/trader/trader.py @@ -1,6 +1,7 @@ import functools -import time -import json + +from tradingagents.agents.utils.agent_utils import format_memory_context +from tradingagents.agents.utils.llm_utils import parse_llm_response def create_trader(llm, memory): @@ -12,22 +13,7 @@ def create_trader(llm, memory): news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - - if memory: - past_memories = memory.get_memories(curr_situation, n_matches=2) - else: - past_memories = [] - - if past_memories: - past_memory_str = "### Past Lessons Applied\\n**Reflections from Similar Situations:**\\n" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\\n\\n" - past_memory_str += "\\n\\n**How I'm Using These Lessons:**\\n" - past_memory_str += "- [Specific adjustment based on past mistake/success]\\n" - past_memory_str += "- [Impact on current conviction level]\\n" - else: - past_memory_str = "" # Don't include placeholder when no memories + past_memory_str = format_memory_context(memory, state) context = { "role": "user", @@ -80,10 +66,11 @@ def create_trader(llm, memory): ] result = llm.invoke(messages) + trader_plan = parse_llm_response(result.content) return { "messages": [result], - "trader_investment_plan": result.content, + "trader_investment_plan": trader_plan, "sender": name, } diff --git a/tradingagents/agents/utils/agent_states.py b/tradingagents/agents/utils/agent_states.py index b81d749a..5df97fb9 100644 --- a/tradingagents/agents/utils/agent_states.py +++ b/tradingagents/agents/utils/agent_states.py @@ -1,20 +1,15 @@ -from typing import Annotated, Sequence -from datetime import date, timedelta, datetime -from typing_extensions import TypedDict, Optional -from langchain_openai import ChatOpenAI +from typing import Annotated + +from langgraph.graph import MessagesState +from typing_extensions import TypedDict + from tradingagents.agents import * -from langgraph.prebuilt import ToolNode -from langgraph.graph import END, StateGraph, START, MessagesState # Researcher team state class InvestDebateState(TypedDict): - bull_history: Annotated[ - str, "Bullish Conversation history" - ] # Bullish Conversation history - bear_history: Annotated[ - str, "Bearish Conversation history" - ] # Bullish Conversation history + bull_history: Annotated[str, "Bullish Conversation history"] # Bullish Conversation history + bear_history: Annotated[str, "Bearish Conversation history"] # Bullish Conversation history history: Annotated[str, "Conversation history"] # Conversation history current_response: Annotated[str, "Latest response"] # Last response judge_decision: Annotated[str, "Final judge decision"] # Last response @@ -23,23 +18,13 @@ class InvestDebateState(TypedDict): # Risk management team state class RiskDebateState(TypedDict): - risky_history: Annotated[ - str, "Risky Agent's Conversation history" - ] # Conversation history - safe_history: Annotated[ - str, "Safe Agent's Conversation history" - ] # Conversation history - neutral_history: Annotated[ - str, "Neutral Agent's Conversation history" - ] # Conversation history + risky_history: Annotated[str, "Risky Agent's Conversation history"] # Conversation history + safe_history: Annotated[str, "Safe Agent's Conversation history"] # Conversation history + neutral_history: Annotated[str, "Neutral Agent's Conversation history"] # Conversation history history: Annotated[str, "Conversation history"] # Conversation history latest_speaker: Annotated[str, "Analyst that spoke last"] - current_risky_response: Annotated[ - str, "Latest response by the risky analyst" - ] # Last response - current_safe_response: Annotated[ - str, "Latest response by the safe analyst" - ] # Last response + current_risky_response: Annotated[str, "Latest response by the risky analyst"] # Last response + current_safe_response: Annotated[str, "Latest response by the safe analyst"] # Last response current_neutral_response: Annotated[ str, "Latest response by the neutral analyst" ] # Last response @@ -56,9 +41,7 @@ class AgentState(MessagesState): # research step market_report: Annotated[str, "Report from the Market Analyst"] sentiment_report: Annotated[str, "Report from the Social Media Analyst"] - news_report: Annotated[ - str, "Report from the News Researcher of current world affairs" - ] + news_report: Annotated[str, "Report from the News Researcher of current world affairs"] fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"] # researcher team discussion step @@ -70,9 +53,7 @@ class AgentState(MessagesState): trader_investment_plan: Annotated[str, "Plan generated by the Trader"] # risk management team discussion step - risk_debate_state: Annotated[ - RiskDebateState, "Current state of the debate on evaluating risk" - ] + risk_debate_state: Annotated[RiskDebateState, "Current state of the debate on evaluating risk"] final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"] @@ -84,5 +65,6 @@ class DiscoveryState(TypedDict): opportunities: Annotated[list[dict], "List of final opportunities with rationale"] final_ranking: Annotated[str, "Final ranking from LLM"] status: Annotated[str, "Current status of discovery"] - tool_logs: Annotated[list[dict], "Detailed logs of all tool calls across all nodes (scanner, filter, deep_dive)"] - + tool_logs: Annotated[ + list[dict], "Detailed logs of all tool calls across all nodes (scanner, filter, deep_dive)" + ] diff --git a/tradingagents/agents/utils/agent_utils.py b/tradingagents/agents/utils/agent_utils.py index c427ef1e..4c88f27f 100644 --- a/tradingagents/agents/utils/agent_utils.py +++ b/tradingagents/agents/utils/agent_utils.py @@ -1,7 +1,13 @@ +from typing import Any, Callable, Dict, List + from langchain_core.messages import HumanMessage, RemoveMessage -# Import all tools from the new registry-based system -from tradingagents.tools.generator import ALL_TOOLS +from tradingagents.agents.utils.llm_utils import ( + create_and_invoke_chain, + parse_llm_response, +) +from tradingagents.agents.utils.prompt_templates import format_analyst_prompt +from tradingagents.tools.generator import ALL_TOOLS, get_agent_tools # Re-export tools for backward compatibility get_stock_data = ALL_TOOLS["get_stock_data"] @@ -20,20 +26,112 @@ get_insider_transactions = ALL_TOOLS["get_insider_transactions"] # Legacy alias for backward compatibility validate_ticker_tool = validate_ticker + def create_msg_delete(): def delete_messages(state): """Clear messages and add placeholder for Anthropic compatibility""" messages = state["messages"] - + # Remove all messages removal_operations = [RemoveMessage(id=m.id) for m in messages] - + # Add a minimal placeholder message placeholder = HumanMessage(content="Continue") - + return {"messages": removal_operations + [placeholder]} - + return delete_messages - \ No newline at end of file +def format_memory_context(memory: Any, state: Dict[str, Any], n_matches: int = 2) -> str: + """Fetch and format past memories into a prompt section. + + Returns the formatted memory string, or "" if no memories available. + Identical logic previously duplicated across 5 agent files. + """ + reports = ( + state["market_report"], + state["sentiment_report"], + state["news_report"], + state["fundamentals_report"], + ) + curr_situation = "\n\n".join(reports) + + if not memory: + return "" + past_memories = memory.get_memories(curr_situation, n_matches=n_matches) + if not past_memories: + return "" + + past_memory_str = "### Past Lessons Applied\\n**Reflections from Similar Situations:**\\n" + for i, rec in enumerate(past_memories, 1): + past_memory_str += rec["recommendation"] + "\\n\\n" + past_memory_str += "\\n\\n**How I'm Using These Lessons:**\\n" + past_memory_str += "- [Specific adjustment based on past mistake/success]\\n" + past_memory_str += "- [Impact on current conviction level]\\n" + return past_memory_str + + +def update_risk_debate_state( + debate_state: Dict[str, Any], argument: str, role: str +) -> Dict[str, Any]: + """Build updated risk debate state after a debator speaks. + + Args: + debate_state: Current risk_debate_state dict. + argument: The formatted argument string (e.g. "Safe Analyst: ..."). + role: One of "Safe", "Risky", "Neutral". + """ + role_key = role.lower() # "safe", "risky", "neutral" + new_state = { + "history": debate_state.get("history", "") + "\n" + argument, + "risky_history": debate_state.get("risky_history", ""), + "safe_history": debate_state.get("safe_history", ""), + "neutral_history": debate_state.get("neutral_history", ""), + "latest_speaker": role, + "current_risky_response": debate_state.get("current_risky_response", ""), + "current_safe_response": debate_state.get("current_safe_response", ""), + "current_neutral_response": debate_state.get("current_neutral_response", ""), + "count": debate_state["count"] + 1, + } + # Append to the speaker's own history and set their current response + new_state[f"{role_key}_history"] = ( + debate_state.get(f"{role_key}_history", "") + "\n" + argument + ) + new_state[f"current_{role_key}_response"] = argument + return new_state + + +def create_analyst_node( + llm: Any, + tool_group: str, + output_key: str, + prompt_builder: Callable[[str, str], str], +) -> Callable: + """Factory for analyst graph nodes. + + Args: + llm: The LLM to use. + tool_group: Tool group name for ``get_agent_tools`` (e.g. "fundamentals"). + output_key: State key for the report (e.g. "fundamentals_report"). + prompt_builder: ``(ticker, current_date) -> system_message`` callable. + """ + + def analyst_node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["company_of_interest"] + current_date = state["trade_date"] + tools = get_agent_tools(tool_group) + + system_message = prompt_builder(ticker, current_date) + tool_names_str = ", ".join(tool.name for tool in tools) + full_message = format_analyst_prompt(system_message, current_date, ticker, tool_names_str) + + result = create_and_invoke_chain(llm, tools, full_message, state["messages"]) + + report = "" + if len(result.tool_calls) == 0: + report = parse_llm_response(result.content) + + return {"messages": [result], output_key: report} + + return analyst_node diff --git a/tradingagents/agents/utils/historical_memory_builder.py b/tradingagents/agents/utils/historical_memory_builder.py index a32fc599..5a099591 100644 --- a/tradingagents/agents/utils/historical_memory_builder.py +++ b/tradingagents/agents/utils/historical_memory_builder.py @@ -9,15 +9,16 @@ This module creates agent memories from historical stock data by: 5. Storing memories in ChromaDB for future retrieval """ -import os import re -import json -import yfinance as yf -import pandas as pd from datetime import datetime, timedelta -from typing import List, Dict, Tuple, Optional, Any -from tradingagents.tools.executor import execute_tool +from typing import Any, Dict, List, Optional, Tuple + from tradingagents.agents.utils.memory import FinancialSituationMemory +from tradingagents.dataflows.y_finance import get_ticker_history +from tradingagents.tools.executor import execute_tool +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class HistoricalMemoryBuilder: @@ -35,7 +36,7 @@ class HistoricalMemoryBuilder: "bear": 0, "trader": 0, "invest_judge": 0, - "risk_manager": 0 + "risk_manager": 0, } def get_tickers_from_alpha_vantage(self, limit: int = 20) -> List[str]: @@ -48,7 +49,7 @@ class HistoricalMemoryBuilder: Returns: List of ticker symbols from top gainers and losers """ - print(f"\n🔍 Fetching top movers from Alpha Vantage...") + logger.info("🔍 Fetching top movers from Alpha Vantage...") try: # Use execute_tool to call the alpha vantage function @@ -57,13 +58,13 @@ class HistoricalMemoryBuilder: # Parse the markdown table response to extract tickers tickers = set() - lines = response.split('\n') + lines = response.split("\n") for line in lines: # Look for table rows with ticker data - if '|' in line and not line.strip().startswith('|---'): - parts = [p.strip() for p in line.split('|')] + if "|" in line and not line.strip().startswith("|---"): + parts = [p.strip() for p in line.split("|")] # Table format: | Ticker | Price | Change % | Volume | - if len(parts) >= 2 and parts[1] and parts[1] not in ['Ticker', '']: + if len(parts) >= 2 and parts[1] and parts[1] not in ["Ticker", ""]: ticker = parts[1].strip() # Filter out warrants, units, and problematic tickers @@ -71,14 +72,16 @@ class HistoricalMemoryBuilder: tickers.add(ticker) ticker_list = sorted(list(tickers)) - print(f" ✅ Found {len(ticker_list)} unique tickers from Alpha Vantage") - print(f" Tickers: {', '.join(ticker_list[:10])}{'...' if len(ticker_list) > 10 else ''}") + logger.info(f"✅ Found {len(ticker_list)} unique tickers from Alpha Vantage") + logger.debug( + f"Tickers: {', '.join(ticker_list[:10])}{'...' if len(ticker_list) > 10 else ''}" + ) return ticker_list except Exception as e: - print(f" ⚠️ Error fetching from Alpha Vantage: {e}") - print(f" Falling back to empty list") + logger.warning(f"⚠️ Error fetching from Alpha Vantage: {e}") + logger.warning("Falling back to empty list") return [] def _is_valid_ticker(self, ticker: str) -> bool: @@ -102,23 +105,23 @@ class HistoricalMemoryBuilder: return False # Must be uppercase letters and numbers only - if not re.match(r'^[A-Z]{1,5}$', ticker): + if not re.match(r"^[A-Z]{1,5}$", ticker): return False # Filter out warrants (W, WW, WS suffix) - if ticker.endswith('W') or ticker.endswith('WW') or ticker.endswith('WS'): + if ticker.endswith("W") or ticker.endswith("WW") or ticker.endswith("WS"): return False # Filter out units - if ticker.endswith('U'): + if ticker.endswith("U"): return False # Filter out rights - if ticker.endswith('R') and len(ticker) > 1: + if ticker.endswith("R") and len(ticker) > 1: return False # Filter out other suffixes that indicate derivatives - if ticker.endswith('Z'): # Often used for special situations + if ticker.endswith("Z"): # Often used for special situations return False return True @@ -129,7 +132,7 @@ class HistoricalMemoryBuilder: start_date: str, end_date: str, min_move_pct: float = 15.0, - window_days: int = 5 + window_days: int = 5, ) -> List[Dict[str, Any]]: """ Find stocks that had significant moves (>15% in 5 days). @@ -153,67 +156,66 @@ class HistoricalMemoryBuilder: """ high_movers = [] - print(f"\n🔍 Scanning for high movers ({min_move_pct}%+ in {window_days} days)") - print(f" Period: {start_date} to {end_date}") - print(f" Tickers: {len(tickers)}\n") + logger.info(f"🔍 Scanning for high movers ({min_move_pct}%+ in {window_days} days)") + logger.info(f"Period: {start_date} to {end_date}") + logger.info(f"Tickers: {len(tickers)}") for ticker in tickers: try: - print(f" Scanning {ticker}...", end=" ") + logger.info(f"Scanning {ticker}...") # Download historical data using yfinance - stock = yf.Ticker(ticker) - df = stock.history(start=start_date, end=end_date) + df = get_ticker_history(ticker, start=start_date, end=end_date) if df.empty: - print("No data") + logger.debug(f"{ticker}: No data") continue # Calculate rolling returns over window_days - df['rolling_return'] = df['Close'].pct_change(periods=window_days) * 100 + df["rolling_return"] = df["Close"].pct_change(periods=window_days) * 100 # Find periods with moves >= min_move_pct - significant_moves = df[abs(df['rolling_return']) >= min_move_pct] + significant_moves = df[abs(df["rolling_return"]) >= min_move_pct] if not significant_moves.empty: for idx, row in significant_moves.iterrows(): # Get the start date (window_days before this date) - move_end_date = idx.strftime('%Y-%m-%d') - move_start_date = (idx - timedelta(days=window_days)).strftime('%Y-%m-%d') + move_end_date = idx.strftime("%Y-%m-%d") + move_start_date = (idx - timedelta(days=window_days)).strftime("%Y-%m-%d") # Get prices try: - start_price = df.loc[df.index >= move_start_date, 'Close'].iloc[0] - end_price = row['Close'] - move_pct = row['rolling_return'] + start_price = df.loc[df.index >= move_start_date, "Close"].iloc[0] + end_price = row["Close"] + move_pct = row["rolling_return"] - high_movers.append({ - 'ticker': ticker, - 'move_start_date': move_start_date, - 'move_end_date': move_end_date, - 'move_pct': move_pct, - 'direction': 'up' if move_pct > 0 else 'down', - 'start_price': start_price, - 'end_price': end_price - }) + high_movers.append( + { + "ticker": ticker, + "move_start_date": move_start_date, + "move_end_date": move_end_date, + "move_pct": move_pct, + "direction": "up" if move_pct > 0 else "down", + "start_price": start_price, + "end_price": end_price, + } + ) except (IndexError, KeyError): continue - print(f"Found {len([m for m in high_movers if m['ticker'] == ticker])} moves") + logger.info(f"Found {len([m for m in high_movers if m['ticker'] == ticker])} moves for {ticker}") else: - print("No significant moves") + logger.debug(f"{ticker}: No significant moves") except Exception as e: - print(f"Error: {e}") + logger.error(f"Error scanning {ticker}: {e}") continue - print(f"\n✅ Total high movers found: {len(high_movers)}\n") + logger.info(f"✅ Total high movers found: {len(high_movers)}") return high_movers def run_retrospective_analysis( - self, - ticker: str, - analysis_date: str + self, ticker: str, analysis_date: str ) -> Optional[Dict[str, Any]]: """ Run the trading graph analysis for a ticker at a specific historical date. @@ -238,47 +240,48 @@ class HistoricalMemoryBuilder: # Import here to avoid circular imports from tradingagents.graph.trading_graph import TradingAgentsGraph - print(f" Running analysis for {ticker} on {analysis_date}...") + logger.info(f"Running analysis for {ticker} on {analysis_date}...") # Create trading graph instance # Use fewer analysts to reduce token usage graph = TradingAgentsGraph( selected_analysts=["market", "fundamentals"], # Skip social/news to reduce tokens config=self.config, - debug=False + debug=False, ) # Run the analysis (returns tuple: final_state, processed_signal) final_state, _ = graph.propagate(ticker, analysis_date) # Extract reports and decisions (with type safety) - def safe_get_str(d, key, default=''): + def safe_get_str(d, key, default=""): """Safely extract string from state, handling lists or other types.""" value = d.get(key, default) if isinstance(value, list): # If it's a list, try to extract text from messages - return ' '.join(str(item) for item in value) + return " ".join(str(item) for item in value) return str(value) if value else default # Extract reports and decisions analysis_data = { - 'market_report': safe_get_str(final_state, 'market_report'), - 'sentiment_report': safe_get_str(final_state, 'sentiment_report'), - 'news_report': safe_get_str(final_state, 'news_report'), - 'fundamentals_report': safe_get_str(final_state, 'fundamentals_report'), - 'investment_plan': safe_get_str(final_state, 'investment_plan'), - 'final_decision': safe_get_str(final_state, 'final_trade_decision'), + "market_report": safe_get_str(final_state, "market_report"), + "sentiment_report": safe_get_str(final_state, "sentiment_report"), + "news_report": safe_get_str(final_state, "news_report"), + "fundamentals_report": safe_get_str(final_state, "fundamentals_report"), + "investment_plan": safe_get_str(final_state, "investment_plan"), + "final_decision": safe_get_str(final_state, "final_trade_decision"), } # Extract structured signals from reports - analysis_data['structured_signals'] = self.extract_structured_signals(analysis_data) + analysis_data["structured_signals"] = self.extract_structured_signals(analysis_data) return analysis_data except Exception as e: - print(f" Error running analysis: {e}") + logger.error(f"Error running analysis: {e}") import traceback - print(f" Traceback: {traceback.format_exc()}") + + logger.debug(f"Traceback: {traceback.format_exc()}") return None def extract_structured_signals(self, reports: Dict[str, str]) -> Dict[str, Any]: @@ -300,63 +303,101 @@ class HistoricalMemoryBuilder: """ signals = {} - market_report = reports.get('market_report', '') - sentiment_report = reports.get('sentiment_report', '') - news_report = reports.get('news_report', '') - fundamentals_report = reports.get('fundamentals_report', '') + market_report = reports.get("market_report", "") + sentiment_report = reports.get("sentiment_report", "") + news_report = reports.get("news_report", "") + fundamentals_report = reports.get("fundamentals_report", "") # Extract volume signals - signals['unusual_volume'] = bool( - re.search(r'(unusual volume|volume spike|high volume|increased volume)', market_report, re.IGNORECASE) + signals["unusual_volume"] = bool( + re.search( + r"(unusual volume|volume spike|high volume|increased volume)", + market_report, + re.IGNORECASE, + ) ) # Extract sentiment - if re.search(r'(bullish|positive outlook|strong buy|buy)', sentiment_report + news_report, re.IGNORECASE): - signals['analyst_sentiment'] = 'bullish' - elif re.search(r'(bearish|negative outlook|strong sell|sell)', sentiment_report + news_report, re.IGNORECASE): - signals['analyst_sentiment'] = 'bearish' + if re.search( + r"(bullish|positive outlook|strong buy|buy)", + sentiment_report + news_report, + re.IGNORECASE, + ): + signals["analyst_sentiment"] = "bullish" + elif re.search( + r"(bearish|negative outlook|strong sell|sell)", + sentiment_report + news_report, + re.IGNORECASE, + ): + signals["analyst_sentiment"] = "bearish" else: - signals['analyst_sentiment'] = 'neutral' + signals["analyst_sentiment"] = "neutral" # Extract news sentiment - if re.search(r'(positive|good news|beat expectations|upgrade|growth)', news_report, re.IGNORECASE): - signals['news_sentiment'] = 'positive' - elif re.search(r'(negative|bad news|miss expectations|downgrade|decline)', news_report, re.IGNORECASE): - signals['news_sentiment'] = 'negative' + if re.search( + r"(positive|good news|beat expectations|upgrade|growth)", news_report, re.IGNORECASE + ): + signals["news_sentiment"] = "positive" + elif re.search( + r"(negative|bad news|miss expectations|downgrade|decline)", news_report, re.IGNORECASE + ): + signals["news_sentiment"] = "negative" else: - signals['news_sentiment'] = 'neutral' + signals["news_sentiment"] = "neutral" # Extract short interest - if re.search(r'(high short interest|heavily shorted|short squeeze)', market_report + news_report, re.IGNORECASE): - signals['short_interest'] = 'high' - elif re.search(r'(low short interest|minimal short)', market_report, re.IGNORECASE): - signals['short_interest'] = 'low' + if re.search( + r"(high short interest|heavily shorted|short squeeze)", + market_report + news_report, + re.IGNORECASE, + ): + signals["short_interest"] = "high" + elif re.search(r"(low short interest|minimal short)", market_report, re.IGNORECASE): + signals["short_interest"] = "low" else: - signals['short_interest'] = 'medium' + signals["short_interest"] = "medium" # Extract insider activity - if re.search(r'(insider buying|executive purchased|insider purchases)', news_report + fundamentals_report, re.IGNORECASE): - signals['insider_activity'] = 'buying' - elif re.search(r'(insider selling|executive sold|insider sales)', news_report + fundamentals_report, re.IGNORECASE): - signals['insider_activity'] = 'selling' + if re.search( + r"(insider buying|executive purchased|insider purchases)", + news_report + fundamentals_report, + re.IGNORECASE, + ): + signals["insider_activity"] = "buying" + elif re.search( + r"(insider selling|executive sold|insider sales)", + news_report + fundamentals_report, + re.IGNORECASE, + ): + signals["insider_activity"] = "selling" else: - signals['insider_activity'] = 'none' + signals["insider_activity"] = "none" # Extract price trend - if re.search(r'(uptrend|bullish trend|rising|moving higher|higher highs)', market_report, re.IGNORECASE): - signals['price_trend'] = 'uptrend' - elif re.search(r'(downtrend|bearish trend|falling|moving lower|lower lows)', market_report, re.IGNORECASE): - signals['price_trend'] = 'downtrend' + if re.search( + r"(uptrend|bullish trend|rising|moving higher|higher highs)", + market_report, + re.IGNORECASE, + ): + signals["price_trend"] = "uptrend" + elif re.search( + r"(downtrend|bearish trend|falling|moving lower|lower lows)", + market_report, + re.IGNORECASE, + ): + signals["price_trend"] = "downtrend" else: - signals['price_trend'] = 'sideways' + signals["price_trend"] = "sideways" # Extract volatility - if re.search(r'(high volatility|volatile|wild swings|sharp movements)', market_report, re.IGNORECASE): - signals['volatility'] = 'high' - elif re.search(r'(low volatility|stable|steady)', market_report, re.IGNORECASE): - signals['volatility'] = 'low' + if re.search( + r"(high volatility|volatile|wild swings|sharp movements)", market_report, re.IGNORECASE + ): + signals["volatility"] = "high" + elif re.search(r"(low volatility|stable|steady)", market_report, re.IGNORECASE): + signals["volatility"] = "low" else: - signals['volatility'] = 'medium' + signals["volatility"] = "medium" return signals @@ -368,7 +409,7 @@ class HistoricalMemoryBuilder: min_move_pct: float = 15.0, analysis_windows: List[int] = [7, 30], max_samples: int = 50, - sample_strategy: str = "diverse" + sample_strategy: str = "diverse", ) -> Dict[str, FinancialSituationMemory]: """ Build memories by finding high movers and running retrospective analyses. @@ -391,25 +432,24 @@ class HistoricalMemoryBuilder: Returns: Dictionary of populated memory instances for each agent type """ - print("=" * 70) - print("🏗️ BUILDING MEMORIES FROM HIGH MOVERS") - print("=" * 70) + logger.info("=" * 70) + logger.info("🏗️ BUILDING MEMORIES FROM HIGH MOVERS") + logger.info("=" * 70) # Step 1: Find high movers high_movers = self.find_high_movers(tickers, start_date, end_date, min_move_pct) if not high_movers: - print("⚠️ No high movers found. Try a different date range or lower threshold.") + logger.warning("⚠️ No high movers found. Try a different date range or lower threshold.") return {} # Step 1.5: Sample/filter high movers based on strategy sampled_movers = self._sample_high_movers(high_movers, max_samples, sample_strategy) - print(f"\n📊 Sampling Strategy: {sample_strategy}") - print(f" Total high movers found: {len(high_movers)}") - print(f" Samples to analyze: {len(sampled_movers)}") - print(f" Estimated runtime: ~{len(sampled_movers) * len(analysis_windows) * 2} minutes") - print() + logger.info(f"📊 Sampling Strategy: {sample_strategy}") + logger.info(f"Total high movers found: {len(high_movers)}") + logger.info(f"Samples to analyze: {len(sampled_movers)}") + logger.info(f"Estimated runtime: ~{len(sampled_movers) * len(analysis_windows) * 2} minutes") # Initialize memory stores agent_memories = { @@ -417,35 +457,35 @@ class HistoricalMemoryBuilder: "bear": FinancialSituationMemory("bear_memory", self.config), "trader": FinancialSituationMemory("trader_memory", self.config), "invest_judge": FinancialSituationMemory("invest_judge_memory", self.config), - "risk_manager": FinancialSituationMemory("risk_manager_memory", self.config) + "risk_manager": FinancialSituationMemory("risk_manager_memory", self.config), } # Step 2: For each high mover, run retrospective analyses - print("\n📊 Running retrospective analyses...\n") + logger.info("📊 Running retrospective analyses...") for idx, mover in enumerate(sampled_movers, 1): - ticker = mover['ticker'] - move_pct = mover['move_pct'] - direction = mover['direction'] - move_start_date = mover['move_start_date'] + ticker = mover["ticker"] + move_pct = mover["move_pct"] + direction = mover["direction"] + move_start_date = mover["move_start_date"] - print(f" [{idx}/{len(sampled_movers)}] {ticker}: {move_pct:+.1f}% {direction}") + logger.info(f"[{idx}/{len(sampled_movers)}] {ticker}: {move_pct:+.1f}% {direction}") # Run analyses at different time windows before the move for days_before in analysis_windows: # Calculate analysis date try: analysis_date = ( - datetime.strptime(move_start_date, '%Y-%m-%d') - timedelta(days=days_before) - ).strftime('%Y-%m-%d') + datetime.strptime(move_start_date, "%Y-%m-%d") - timedelta(days=days_before) + ).strftime("%Y-%m-%d") - print(f" Analyzing T-{days_before} days ({analysis_date})...") + logger.info(f"Analyzing T-{days_before} days ({analysis_date})...") # Run trading graph analysis analysis = self.run_retrospective_analysis(ticker, analysis_date) if not analysis: - print(f" ⚠️ Analysis failed, skipping...") + logger.warning("⚠️ Analysis failed, skipping...") continue # Create combined situation text @@ -469,8 +509,7 @@ class HistoricalMemoryBuilder: # Extract agent recommendation from investment plan and final decision agent_recommendation = self._extract_recommendation( - analysis.get('investment_plan', ''), - analysis.get('final_decision', '') + analysis.get("investment_plan", ""), analysis.get("final_decision", "") ) # Determine if agent was correct @@ -478,18 +517,22 @@ class HistoricalMemoryBuilder: # Create metadata metadata = { - 'ticker': ticker, - 'analysis_date': analysis_date, - 'days_before_move': days_before, - 'move_pct': abs(move_pct), - 'move_direction': direction, - 'agent_recommendation': agent_recommendation, - 'was_correct': was_correct, - 'structured_signals': analysis['structured_signals'] + "ticker": ticker, + "analysis_date": analysis_date, + "days_before_move": days_before, + "move_pct": abs(move_pct), + "move_direction": direction, + "agent_recommendation": agent_recommendation, + "was_correct": was_correct, + "structured_signals": analysis["structured_signals"], } # Create recommendation text - lesson_text = f"This signal combination is reliable for predicting {direction} moves." if was_correct else "This signal combination can be misleading. Need to consider other factors." + lesson_text = ( + f"This signal combination is reliable for predicting {direction} moves." + if was_correct + else "This signal combination can be misleading. Need to consider other factors." + ) recommendation_text = f""" Agent Decision: {agent_recommendation} @@ -507,38 +550,40 @@ Lesson: {lesson_text} # Store in all agent memories for agent_type, memory in agent_memories.items(): - memory.add_situations_with_metadata([ - (situation_text, recommendation_text, metadata) - ]) + memory.add_situations_with_metadata( + [(situation_text, recommendation_text, metadata)] + ) self.memories_created[agent_type] = self.memories_created.get(agent_type, 0) + 1 - print(f" ✅ Memory created: {agent_recommendation} -> {direction} ({was_correct})") + logger.info( + f"✅ Memory created: {agent_recommendation} -> {direction} ({was_correct})" + ) except Exception as e: - print(f" ⚠️ Error: {e}") + logger.warning(f"⚠️ Error: {e}") continue - # Print summary - print("\n" + "=" * 70) - print("📊 MEMORY CREATION SUMMARY") - print("=" * 70) - print(f" High movers analyzed: {len(sampled_movers)}") - print(f" Analysis windows: {analysis_windows} days before move") + # Log summary + logger.info("=" * 70) + logger.info("📊 MEMORY CREATION SUMMARY") + logger.info("=" * 70) + logger.info(f" High movers analyzed: {len(sampled_movers)}") + logger.info(f" Analysis windows: {analysis_windows} days before move") for agent_type, count in self.memories_created.items(): - print(f" {agent_type.ljust(15)}: {count} memories") + logger.info(f" {agent_type.ljust(15)}: {count} memories") - # Print statistics - print("\n📈 MEMORY BANK STATISTICS") - print("=" * 70) + # Log statistics + logger.info("\n📈 MEMORY BANK STATISTICS") + logger.info("=" * 70) for agent_type, memory in agent_memories.items(): stats = memory.get_statistics() - print(f"\n {agent_type.upper()}:") - print(f" Total memories: {stats['total_memories']}") - print(f" Accuracy rate: {stats['accuracy_rate']:.1f}%") - print(f" Avg move: {stats['avg_move_pct']:.1f}%") + logger.info(f"\n {agent_type.upper()}:") + logger.info(f" Total memories: {stats['total_memories']}") + logger.info(f" Accuracy rate: {stats['accuracy_rate']:.1f}%") + logger.info(f" Avg move: {stats['avg_move_pct']:.1f}%") - print("=" * 70 + "\n") + logger.info("=" * 70) return agent_memories @@ -551,11 +596,13 @@ Lesson: {lesson_text} combined_text = (investment_plan + " " + final_decision).lower() # Check for clear buy/sell/hold signals - if re.search(r'\b(strong buy|buy|long position|bullish|recommend buying)\b', combined_text): + if re.search(r"\b(strong buy|buy|long position|bullish|recommend buying)\b", combined_text): return "buy" - elif re.search(r'\b(strong sell|sell|short position|bearish|recommend selling)\b', combined_text): + elif re.search( + r"\b(strong sell|sell|short position|bearish|recommend selling)\b", combined_text + ): return "sell" - elif re.search(r'\b(hold|neutral|wait|avoid)\b', combined_text): + elif re.search(r"\b(hold|neutral|wait|avoid)\b", combined_text): return "hold" else: return "unclear" @@ -589,10 +636,7 @@ Lesson: {lesson_text} return "\n".join(lines) def _sample_high_movers( - self, - high_movers: List[Dict[str, Any]], - max_samples: int, - strategy: str + self, high_movers: List[Dict[str, Any]], max_samples: int, strategy: str ) -> List[Dict[str, Any]]: """ Sample high movers based on strategy to reduce analysis time. @@ -612,12 +656,12 @@ Lesson: {lesson_text} if strategy == "diverse": # Get balanced mix of up/down moves across different magnitudes - up_moves = [m for m in high_movers if m['direction'] == 'up'] - down_moves = [m for m in high_movers if m['direction'] == 'down'] + up_moves = [m for m in high_movers if m["direction"] == "up"] + down_moves = [m for m in high_movers if m["direction"] == "down"] # Sort each by magnitude - up_moves.sort(key=lambda x: abs(x['move_pct']), reverse=True) - down_moves.sort(key=lambda x: abs(x['move_pct']), reverse=True) + up_moves.sort(key=lambda x: abs(x["move_pct"]), reverse=True) + down_moves.sort(key=lambda x: abs(x["move_pct"]), reverse=True) # Take half from each direction (or proportional if imbalanced) up_count = min(len(up_moves), max_samples // 2) @@ -637,14 +681,14 @@ Lesson: {lesson_text} # Divide into 3 buckets by magnitude bucket_size = len(moves) // 3 large = moves[:bucket_size] - medium = moves[bucket_size:bucket_size*2] - small = moves[bucket_size*2:] + medium = moves[bucket_size : bucket_size * 2] + small = moves[bucket_size * 2 :] # Sample proportionally from each bucket samples = [] - samples.extend(large[:count // 3]) - samples.extend(medium[:count // 3]) - samples.extend(small[:count - (2 * (count // 3))]) + samples.extend(large[: count // 3]) + samples.extend(medium[: count // 3]) + samples.extend(small[: count - (2 * (count // 3))]) return samples sampled = [] @@ -655,12 +699,12 @@ Lesson: {lesson_text} elif strategy == "largest": # Take the largest absolute moves - sorted_movers = sorted(high_movers, key=lambda x: abs(x['move_pct']), reverse=True) + sorted_movers = sorted(high_movers, key=lambda x: abs(x["move_pct"]), reverse=True) return sorted_movers[:max_samples] elif strategy == "recent": # Take the most recent moves - sorted_movers = sorted(high_movers, key=lambda x: x['move_end_date'], reverse=True) + sorted_movers = sorted(high_movers, key=lambda x: x["move_end_date"], reverse=True) return sorted_movers[:max_samples] elif strategy == "random": @@ -687,7 +731,9 @@ Lesson: {lesson_text} # Get technical/price data (what Market Analyst sees) stock_data = execute_tool("get_stock_data", symbol=ticker, start_date=date) indicators = execute_tool("get_indicators", symbol=ticker, curr_date=date) - data["market_report"] = f"Stock Data:\n{stock_data}\n\nTechnical Indicators:\n{indicators}" + data["market_report"] = ( + f"Stock Data:\n{stock_data}\n\nTechnical Indicators:\n{indicators}" + ) except Exception as e: data["market_report"] = f"Error fetching market data: {e}" @@ -700,7 +746,9 @@ Lesson: {lesson_text} try: # Get sentiment (what Social Analyst sees) - sentiment = execute_tool("get_reddit_discussions", symbol=ticker, from_date=date, to_date=date) + sentiment = execute_tool( + "get_reddit_discussions", symbol=ticker, from_date=date, to_date=date + ) data["sentiment_report"] = sentiment except Exception as e: data["sentiment_report"] = f"Error fetching sentiment: {e}" @@ -727,14 +775,19 @@ Lesson: {lesson_text} """ try: # Get stock prices for both dates - start_data = execute_tool("get_stock_data", symbol=ticker, start_date=start_date, end_date=start_date) - end_data = execute_tool("get_stock_data", symbol=ticker, start_date=end_date, end_date=end_date) + start_data = execute_tool( + "get_stock_data", symbol=ticker, start_date=start_date, end_date=start_date + ) + end_data = execute_tool( + "get_stock_data", symbol=ticker, start_date=end_date, end_date=end_date + ) # Parse prices (this is simplified - you'd need to parse the actual response) # Assuming response has close price - adjust based on actual API response import re - start_match = re.search(r'Close[:\s]+\$?([\d.]+)', str(start_data)) - end_match = re.search(r'Close[:\s]+\$?([\d.]+)', str(end_data)) + + start_match = re.search(r"Close[:\s]+\$?([\d.]+)", str(start_data)) + end_match = re.search(r"Close[:\s]+\$?([\d.]+)", str(end_data)) if start_match and end_match: start_price = float(start_match.group(1)) @@ -743,10 +796,12 @@ Lesson: {lesson_text} return None except Exception as e: - print(f"Error calculating returns: {e}") + logger.error(f"Error calculating returns: {e}") return None - def _create_bull_researcher_memory(self, situation: str, returns: float, ticker: str, date: str) -> str: + def _create_bull_researcher_memory( + self, situation: str, returns: float, ticker: str, date: str + ) -> str: """Create memory for bull researcher based on outcome. Returns lesson learned from bullish perspective. @@ -780,7 +835,9 @@ Stock moved {returns:.2f}%, indicating mixed signals. Lesson: This pattern of indicators doesn't provide strong directional conviction. Look for clearer signals before making strong bullish arguments. """ - def _create_bear_researcher_memory(self, situation: str, returns: float, ticker: str, date: str) -> str: + def _create_bear_researcher_memory( + self, situation: str, returns: float, ticker: str, date: str + ) -> str: """Create memory for bear researcher based on outcome.""" if returns < -5: return f"""SUCCESSFUL BEARISH ANALYSIS for {ticker} on {date}: @@ -842,7 +899,9 @@ Trading lesson: Recommendation: Pattern recognition suggests {action} in similar future scenarios. """ - def _create_invest_judge_memory(self, situation: str, returns: float, ticker: str, date: str) -> str: + def _create_invest_judge_memory( + self, situation: str, returns: float, ticker: str, date: str + ) -> str: """Create memory for investment judge/research manager.""" if returns > 5: verdict = "Strong BUY recommendation was warranted" @@ -868,7 +927,9 @@ When synthesizing bull/bear arguments in similar conditions: Recommendation for similar situations: {verdict} """ - def _create_risk_manager_memory(self, situation: str, returns: float, ticker: str, date: str) -> str: + def _create_risk_manager_memory( + self, situation: str, returns: float, ticker: str, date: str + ) -> str: """Create memory for risk manager.""" volatility = "HIGH" if abs(returns) > 10 else "MEDIUM" if abs(returns) > 5 else "LOW" @@ -901,7 +962,7 @@ Recommendation: {risk_assessment} start_date: str, end_date: str, lookforward_days: int = 7, - interval_days: int = 30 + interval_days: int = 30, ) -> Dict[str, List[Tuple[str, str]]]: """Build historical memories for a stock across a date range. @@ -915,28 +976,22 @@ Recommendation: {risk_assessment} Returns: Dictionary mapping agent type to list of (situation, lesson) tuples """ - memories = { - "bull": [], - "bear": [], - "trader": [], - "invest_judge": [], - "risk_manager": [] - } + memories = {"bull": [], "bear": [], "trader": [], "invest_judge": [], "risk_manager": []} current_date = datetime.strptime(start_date, "%Y-%m-%d") end_dt = datetime.strptime(end_date, "%Y-%m-%d") - print(f"\n🧠 Building historical memories for {ticker}") - print(f" Period: {start_date} to {end_date}") - print(f" Lookforward: {lookforward_days} days") - print(f" Sampling interval: {interval_days} days\n") + logger.info(f"🧠 Building historical memories for {ticker}") + logger.info(f"Period: {start_date} to {end_date}") + logger.info(f"Lookforward: {lookforward_days} days") + logger.info(f"Sampling interval: {interval_days} days") sample_count = 0 while current_date <= end_dt: date_str = current_date.strftime("%Y-%m-%d") future_date_str = (current_date + timedelta(days=lookforward_days)).strftime("%Y-%m-%d") - print(f" 📊 Sampling {date_str}...", end=" ") + logger.info(f"📊 Sampling {date_str}...") # Get historical data for this period data = self._get_stock_data_for_period(ticker, date_str) @@ -946,42 +1001,49 @@ Recommendation: {risk_assessment} returns = self._calculate_returns(ticker, date_str, future_date_str) if returns is not None: - print(f"Return: {returns:+.2f}%") + logger.info(f"Return: {returns:+.2f}%") # Create agent-specific memories - memories["bull"].append(( - situation, - self._create_bull_researcher_memory(situation, returns, ticker, date_str) - )) + memories["bull"].append( + ( + situation, + self._create_bull_researcher_memory(situation, returns, ticker, date_str), + ) + ) - memories["bear"].append(( - situation, - self._create_bear_researcher_memory(situation, returns, ticker, date_str) - )) + memories["bear"].append( + ( + situation, + self._create_bear_researcher_memory(situation, returns, ticker, date_str), + ) + ) - memories["trader"].append(( - situation, - self._create_trader_memory(situation, returns, ticker, date_str) - )) + memories["trader"].append( + (situation, self._create_trader_memory(situation, returns, ticker, date_str)) + ) - memories["invest_judge"].append(( - situation, - self._create_invest_judge_memory(situation, returns, ticker, date_str) - )) + memories["invest_judge"].append( + ( + situation, + self._create_invest_judge_memory(situation, returns, ticker, date_str), + ) + ) - memories["risk_manager"].append(( - situation, - self._create_risk_manager_memory(situation, returns, ticker, date_str) - )) + memories["risk_manager"].append( + ( + situation, + self._create_risk_manager_memory(situation, returns, ticker, date_str), + ) + ) sample_count += 1 else: - print("⚠️ No data") + logger.warning("⚠️ No data") # Move to next interval current_date += timedelta(days=interval_days) - print(f"\n✅ Created {sample_count} memory samples for {ticker}") + logger.info(f"✅ Created {sample_count} memory samples for {ticker}") for agent_type in memories: self.memories_created[agent_type] += len(memories[agent_type]) @@ -993,7 +1055,7 @@ Recommendation: {risk_assessment} start_date: str, end_date: str, lookforward_days: int = 7, - interval_days: int = 30 + interval_days: int = 30, ) -> Dict[str, FinancialSituationMemory]: """Build and populate memories for all agent types across multiple stocks. @@ -1013,12 +1075,12 @@ Recommendation: {risk_assessment} "bear": FinancialSituationMemory("bear_memory", self.config), "trader": FinancialSituationMemory("trader_memory", self.config), "invest_judge": FinancialSituationMemory("invest_judge_memory", self.config), - "risk_manager": FinancialSituationMemory("risk_manager_memory", self.config) + "risk_manager": FinancialSituationMemory("risk_manager_memory", self.config), } - print("=" * 70) - print("🏗️ HISTORICAL MEMORY BUILDER") - print("=" * 70) + logger.info("=" * 70) + logger.info("🏗️ HISTORICAL MEMORY BUILDER") + logger.info("=" * 70) # Build memories for each ticker for ticker in tickers: @@ -1027,7 +1089,7 @@ Recommendation: {risk_assessment} start_date=start_date, end_date=end_date, lookforward_days=lookforward_days, - interval_days=interval_days + interval_days=interval_days, ) # Add memories to each agent's memory store @@ -1036,12 +1098,12 @@ Recommendation: {risk_assessment} agent_memories[agent_type].add_situations(memory_list) # Print summary - print("\n" + "=" * 70) - print("📊 MEMORY CREATION SUMMARY") - print("=" * 70) + logger.info("=" * 70) + logger.info("📊 MEMORY CREATION SUMMARY") + logger.info("=" * 70) for agent_type, count in self.memories_created.items(): - print(f" {agent_type.ljust(15)}: {count} memories") - print("=" * 70 + "\n") + logger.info(f"{agent_type.ljust(15)}: {count} memories") + logger.info("=" * 70) return agent_memories @@ -1060,19 +1122,19 @@ if __name__ == "__main__": tickers=tickers, start_date="2024-01-01", end_date="2024-12-01", - lookforward_days=7, # 1-week returns - interval_days=30 # Sample monthly + lookforward_days=7, # 1-week returns + interval_days=30, # Sample monthly ) # Test retrieval test_situation = "Strong earnings beat with positive sentiment and bullish technical indicators in tech sector" - print("\n🔍 Testing memory retrieval...") - print(f"Query: {test_situation}\n") + logger.info("🔍 Testing memory retrieval...") + logger.info(f"Query: {test_situation}") for agent_type, memory in memories.items(): - print(f"\n{agent_type.upper()} MEMORIES:") + logger.info(f"\n{agent_type.upper()} MEMORIES:") results = memory.get_memories(test_situation, n_matches=2) for i, result in enumerate(results, 1): - print(f"\n Match {i} (similarity: {result['similarity_score']:.2f}):") - print(f" {result['recommendation'][:200]}...") + logger.info(f"\n Match {i} (similarity: {result['similarity_score']:.2f}):") + logger.info(f" {result['recommendation'][:200]}...") diff --git a/tradingagents/agents/utils/llm_utils.py b/tradingagents/agents/utils/llm_utils.py new file mode 100644 index 00000000..eb1f6f14 --- /dev/null +++ b/tradingagents/agents/utils/llm_utils.py @@ -0,0 +1,59 @@ +from typing import Any, Dict, List, Union + +from langchain_core.messages import BaseMessage, HumanMessage +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder + + +def parse_llm_response(response_content: Union[str, List[Union[str, Dict[str, Any]]]]) -> str: + """ + Parse content from an LLM response, handling both string and list formats. + + This function standardizes extraction of text from various LLM provider response formats + (e.g., standard strings vs Anthropic's block format). + + Args: + response_content: The raw content field from an LLM response object. + + Returns: + The extracted text content as a string. + """ + if isinstance(response_content, list): + return "\n".join( + block.get("text", str(block)) if isinstance(block, dict) else str(block) + for block in response_content + ) + + return str(response_content) if response_content is not None else "" + + +def create_and_invoke_chain( + llm: Any, tools: List[Any], system_message: str, messages: List[BaseMessage] +) -> Any: + """ + Create and invoke a standard agent chain with tools. + + Args: + llm: The Language Model to use + tools: List of tools to bind to the LLM + system_message: The system prompt content + messages: The chat history messages + + Returns: + The LLM response (AIMessage) + """ + prompt = ChatPromptTemplate.from_messages( + [ + ("system", system_message), + MessagesPlaceholder(variable_name="messages"), + ] + ) + + # Ensure at least one non-system message for Gemini compatibility + # Gemini API requires at least one HumanMessage in addition to SystemMessage + if not messages: + messages = [ + HumanMessage(content="Please provide your analysis based on the context above.") + ] + + chain = prompt | llm.bind_tools(tools) + return chain.invoke({"messages": messages}) diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py index fdc3a1f2..468bc882 100644 --- a/tradingagents/agents/utils/memory.py +++ b/tradingagents/agents/utils/memory.py @@ -1,8 +1,12 @@ import os +from typing import Any, Dict, List, Optional, Tuple + import chromadb -from chromadb.config import Settings from openai import OpenAI -from typing import List, Dict, Any, Optional, Tuple + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class FinancialSituationMemory: @@ -17,7 +21,7 @@ class FinancialSituationMemory: self.embedding_backend = "https://api.openai.com/v1" self.embedding = "text-embedding-3-small" - self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + self.client = OpenAI(api_key=config.validate_key("openai_api_key", "OpenAI")) # Use persistent storage in project directory persist_directory = os.path.join(config.get("project_dir", "."), "memory_db") @@ -28,43 +32,52 @@ class FinancialSituationMemory: # Get or create collection try: self.situation_collection = self.chroma_client.get_collection(name=name) - except: + except Exception: self.situation_collection = self.chroma_client.create_collection(name=name) def get_embedding(self, text): """Get OpenAI embedding for a text""" - - response = self.client.embeddings.create( - model=self.embedding, input=text - ) + + response = self.client.embeddings.create(model=self.embedding, input=text) return response.data[0].embedding - def add_situations(self, situations_and_advice): - """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)""" + def _batch_add( + self, + documents: List[str], + metadatas: List[Dict[str, Any]], + embeddings: List[List[float]], + ids: List[str] = None, + ): + """Internal helper to batch add documents to ChromaDB.""" + if not documents: + return - situations = [] - advice = [] - ids = [] - embeddings = [] - - offset = self.situation_collection.count() - - for i, (situation, recommendation) in enumerate(situations_and_advice): - situations.append(situation) - advice.append(recommendation) - ids.append(str(offset + i)) - embeddings.append(self.get_embedding(situation)) + if ids is None: + offset = self.situation_collection.count() + ids = [str(offset + i) for i in range(len(documents))] self.situation_collection.add( - documents=situations, - metadatas=[{"recommendation": rec} for rec in advice], + documents=documents, + metadatas=metadatas, embeddings=embeddings, ids=ids, ) + def add_situations(self, situations_and_advice): + """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)""" + situations = [] + metadatas = [] + embeddings = [] + + for situation, recommendation in situations_and_advice: + situations.append(situation) + metadatas.append({"recommendation": recommendation}) + embeddings.append(self.get_embedding(situation)) + + self._batch_add(situations, metadatas, embeddings) + def add_situations_with_metadata( - self, - situations_and_outcomes: List[Tuple[str, str, Dict[str, Any]]] + self, situations_and_outcomes: List[Tuple[str, str, Dict[str, Any]]] ): """ Add financial situations with enhanced metadata for learning system. @@ -88,15 +101,11 @@ class FinancialSituationMemory: - etc. """ situations = [] - ids = [] - embeddings = [] metadatas = [] + embeddings = [] - offset = self.situation_collection.count() - - for i, (situation, recommendation, metadata) in enumerate(situations_and_outcomes): + for situation, recommendation, metadata in situations_and_outcomes: situations.append(situation) - ids.append(str(offset + i)) embeddings.append(self.get_embedding(situation)) # Merge recommendation with metadata @@ -107,12 +116,7 @@ class FinancialSituationMemory: full_metadata = self._sanitize_metadata(full_metadata) metadatas.append(full_metadata) - self.situation_collection.add( - documents=situations, - metadatas=metadatas, - embeddings=embeddings, - ids=ids, - ) + self._batch_add(situations, metadatas, embeddings) def _sanitize_metadata(self, metadata: Dict[str, Any]) -> Dict[str, Any]: """ @@ -164,7 +168,7 @@ class FinancialSituationMemory: current_situation: str, signal_filters: Optional[Dict[str, Any]] = None, n_matches: int = 3, - min_similarity: float = 0.5 + min_similarity: float = 0.5, ) -> List[Dict[str, Any]]: """ Hybrid search: Filter by structured signals, then rank by embedding similarity. @@ -216,18 +220,20 @@ class FinancialSituationMemory: metadata = results["metadatas"][0][i] - matched_results.append({ - "matched_situation": results["documents"][0][i], - "recommendation": metadata.get("recommendation", ""), - "similarity_score": similarity_score, - "metadata": metadata, - # Extract key fields for convenience - "ticker": metadata.get("ticker", ""), - "move_pct": metadata.get("move_pct", 0), - "move_direction": metadata.get("move_direction", ""), - "was_correct": metadata.get("was_correct", False), - "days_before_move": metadata.get("days_before_move", 0), - }) + matched_results.append( + { + "matched_situation": results["documents"][0][i], + "recommendation": metadata.get("recommendation", ""), + "similarity_score": similarity_score, + "metadata": metadata, + # Extract key fields for convenience + "ticker": metadata.get("ticker", ""), + "move_pct": metadata.get("move_pct", 0), + "move_direction": metadata.get("move_direction", ""), + "was_correct": metadata.get("was_correct", False), + "days_before_move": metadata.get("days_before_move", 0), + } + ) # Return top n_matches return matched_results[:n_matches] @@ -250,13 +256,11 @@ class FinancialSituationMemory: "total_memories": 0, "accuracy_rate": 0.0, "avg_move_pct": 0.0, - "signal_distribution": {} + "signal_distribution": {}, } # Get all memories - all_results = self.situation_collection.get( - include=["metadatas"] - ) + all_results = self.situation_collection.get(include=["metadatas"]) metadatas = all_results["metadatas"] @@ -283,7 +287,7 @@ class FinancialSituationMemory: "total_memories": total_count, "accuracy_rate": accuracy_rate, "avg_move_pct": avg_move_pct, - "signal_distribution": signal_distribution + "signal_distribution": signal_distribution, } @@ -324,10 +328,10 @@ if __name__ == "__main__": recommendations = matcher.get_memories(current_situation, n_matches=2) for i, rec in enumerate(recommendations, 1): - print(f"\nMatch {i}:") - print(f"Similarity Score: {rec['similarity_score']:.2f}") - print(f"Matched Situation: {rec['matched_situation']}") - print(f"Recommendation: {rec['recommendation']}") + logger.info(f"Match {i}:") + logger.info(f"Similarity Score: {rec['similarity_score']:.2f}") + logger.info(f"Matched Situation: {rec['matched_situation']}") + logger.info(f"Recommendation: {rec['recommendation']}") except Exception as e: - print(f"Error during recommendation: {str(e)}") + logger.error(f"Error during recommendation: {str(e)}") diff --git a/tradingagents/agents/utils/prompt_templates.py b/tradingagents/agents/utils/prompt_templates.py index 28abd091..66ee7010 100644 --- a/tradingagents/agents/utils/prompt_templates.py +++ b/tradingagents/agents/utils/prompt_templates.py @@ -38,11 +38,11 @@ def get_date_awareness_section(current_date: str) -> str: def validate_analyst_output(report: str, required_sections: list) -> dict: """ Validate that report contains all required sections. - + Args: report: The analyst report text to validate required_sections: List of section names to check for - + Returns: Dictionary mapping section names to boolean (True if found) """ @@ -50,28 +50,23 @@ def validate_analyst_output(report: str, required_sections: list) -> dict: for section in required_sections: # Check if section header exists (with ### or ##) validation[section] = ( - f"### {section}" in report - or f"## {section}" in report - or f"**{section}**" in report + f"### {section}" in report or f"## {section}" in report or f"**{section}**" in report ) return validation def format_analyst_prompt( - system_message: str, - current_date: str, - ticker: str, - tool_names: str + system_message: str, current_date: str, ticker: str, tool_names: str ) -> str: """ Format a complete analyst prompt with boilerplate and context. - + Args: system_message: The agent-specific system message current_date: Current analysis date ticker: Stock ticker symbol tool_names: Comma-separated list of tool names - + Returns: Formatted prompt string """ @@ -79,4 +74,3 @@ def format_analyst_prompt( f"{BASE_COLLABORATIVE_BOILERPLATE}\n\n{system_message}\n\n" f"Context: {ticker} | Date: {current_date} | Tools: {tool_names}" ) - diff --git a/tradingagents/agents/utils/twitter_data_tools.py b/tradingagents/agents/utils/twitter_data_tools.py index 46cdba13..37580b4c 100644 --- a/tradingagents/agents/utils/twitter_data_tools.py +++ b/tradingagents/agents/utils/twitter_data_tools.py @@ -1,7 +1,10 @@ -from langchain_core.tools import tool from typing import Annotated + +from langchain_core.tools import tool + from tradingagents.tools.executor import execute_tool + @tool def get_tweets( query: Annotated[str, "Search query for tweets (e.g. ticker symbol or topic)"], @@ -18,6 +21,7 @@ def get_tweets( """ return execute_tool("get_tweets", query=query, count=count) + @tool def get_tweets_from_user( username: Annotated[str, "Twitter username (without @) to fetch tweets from"], diff --git a/tradingagents/config.py b/tradingagents/config.py new file mode 100644 index 00000000..3026f9ae --- /dev/null +++ b/tradingagents/config.py @@ -0,0 +1,121 @@ +import os +from typing import Any, Optional + +from dotenv import load_dotenv + +from tradingagents.default_config import DEFAULT_CONFIG + +# Load environment variables from .env file +load_dotenv() + + +class Config: + """ + Centralized configuration management. + Merges environment variables with default configuration. + """ + + _instance = None + + def __new__(cls): + if cls._instance is None: + cls._instance = super(Config, cls).__new__(cls) + cls._instance._initialize() + return cls._instance + + def _initialize(self): + self._defaults = DEFAULT_CONFIG + self._env_cache = {} + + def _get_env(self, key: str, default: Any = None) -> Any: + """Helper to get env var with optional default from config dictionary.""" + val = os.getenv(key) + if val is not None: + return val + return default + + # --- API Keys --- + + @property + def openai_api_key(self) -> Optional[str]: + return self._get_env("OPENAI_API_KEY") + + @property + def alpha_vantage_api_key(self) -> Optional[str]: + return self._get_env("ALPHA_VANTAGE_API_KEY") + + @property + def finnhub_api_key(self) -> Optional[str]: + return self._get_env("FINNHUB_API_KEY") + + @property + def tradier_api_key(self) -> Optional[str]: + return self._get_env("TRADIER_API_KEY") + + @property + def fmp_api_key(self) -> Optional[str]: + return self._get_env("FMP_API_KEY") + + @property + def reddit_client_id(self) -> Optional[str]: + return self._get_env("REDDIT_CLIENT_ID") + + @property + def reddit_client_secret(self) -> Optional[str]: + return self._get_env("REDDIT_CLIENT_SECRET") + + @property + def reddit_user_agent(self) -> str: + return self._get_env("REDDIT_USER_AGENT", "TradingAgents/1.0") + + @property + def twitter_bearer_token(self) -> Optional[str]: + return self._get_env("TWITTER_BEARER_TOKEN") + + @property + def serper_api_key(self) -> Optional[str]: + return self._get_env("SERPER_API_KEY") + + @property + def gemini_api_key(self) -> Optional[str]: + return self._get_env("GEMINI_API_KEY") + + # --- Paths and Settings --- + + @property + def results_dir(self) -> str: + return self._defaults.get("results_dir", "./results") + + @property + def user_workspace(self) -> str: + return self._get_env("USER_WORKSPACE", self._defaults.get("project_dir")) + + # --- Methods --- + + def validate_key(self, key_property: str, service_name: str) -> str: + """ + Validate that a specific API key property is set. + Returns the key if valid, raises ValueError otherwise. + """ + key = getattr(self, key_property) + if not key: + raise ValueError( + f"{service_name} API Key not found. Please set correct environment variable." + ) + return key + + def get(self, key: str, default: Any = None) -> Any: + """ + Get configuration value. + Checks properties first, then defaults. + """ + if hasattr(self, key): + val = getattr(self, key) + if val is not None: + return val + + return self._defaults.get(key, default) + + +# Global config instance +config = Config() diff --git a/tradingagents/dataflows/alpha_vantage.py b/tradingagents/dataflows/alpha_vantage.py index d07b9c43..5461dc88 100644 --- a/tradingagents/dataflows/alpha_vantage.py +++ b/tradingagents/dataflows/alpha_vantage.py @@ -1,5 +1,28 @@ # Import functions from specialized modules + +from .alpha_vantage_fundamentals import ( + get_balance_sheet, + get_cashflow, + get_fundamentals, + get_income_statement, +) +from .alpha_vantage_news import ( + get_global_news, + get_insider_sentiment, + get_insider_transactions, + get_news, +) from .alpha_vantage_stock import get_stock, get_top_gainers_losers -from .alpha_vantage_indicator import get_indicator -from .alpha_vantage_fundamentals import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement -from .alpha_vantage_news import get_news, get_insider_transactions, get_insider_sentiment, get_global_news \ No newline at end of file + +__all__ = [ + "get_stock", + "get_top_gainers_losers", + "get_fundamentals", + "get_balance_sheet", + "get_cashflow", + "get_income_statement", + "get_news", + "get_global_news", + "get_insider_transactions", + "get_insider_sentiment", +] diff --git a/tradingagents/dataflows/alpha_vantage_analysts.py b/tradingagents/dataflows/alpha_vantage_analysts.py index 8a2fdd1c..5ddd8e11 100644 --- a/tradingagents/dataflows/alpha_vantage_analysts.py +++ b/tradingagents/dataflows/alpha_vantage_analysts.py @@ -3,17 +3,19 @@ Alpha Vantage Analyst Rating Changes Detection Tracks recent analyst upgrades/downgrades and price target changes """ -import os -import requests +import json from datetime import datetime, timedelta -from typing import Annotated, List +from typing import Annotated, Dict, List, Union + +from .alpha_vantage_common import _make_api_request def get_analyst_rating_changes( lookback_days: Annotated[int, "Number of days to look back for rating changes"] = 7, change_types: Annotated[List[str], "Types of changes to track"] = None, top_n: Annotated[int, "Number of top results to return"] = 20, -) -> str: + return_structured: Annotated[bool, "Return list of dicts instead of markdown"] = False, +) -> Union[List[Dict], str]: """ Track recent analyst upgrades/downgrades and rating changes. @@ -23,14 +25,12 @@ def get_analyst_rating_changes( lookback_days: Number of days to look back (default 7) change_types: Types of changes ["upgrade", "downgrade", "initiated", "reiterated"] top_n: Maximum number of results to return + return_structured: If True, returns list of dicts instead of markdown Returns: - Formatted markdown report of recent analyst rating changes + If return_structured=True: list of analyst change dicts + If return_structured=False: Formatted markdown report """ - api_key = os.getenv("ALPHA_VANTAGE_API_KEY") - if not api_key: - return "Error: ALPHA_VANTAGE_API_KEY not set in environment variables" - if change_types is None: change_types = ["upgrade", "downgrade", "initiated"] @@ -38,26 +38,31 @@ def get_analyst_rating_changes( # We'll use news sentiment API which includes analyst actions # For production, consider using Financial Modeling Prep or Benzinga API - url = "https://www.alphavantage.co/query" - try: # Get market news which includes analyst actions params = { - "function": "NEWS_SENTIMENT", "topics": "earnings,technology,finance", "sort": "LATEST", - "limit": 200, # Get more news to find analyst actions - "apikey": api_key, + "limit": "200", # Get more news to find analyst actions } - response = requests.get(url, params=params, timeout=30) - response.raise_for_status() - data = response.json() + response_text = _make_api_request("NEWS_SENTIMENT", params) + + try: + data = json.loads(response_text) + except json.JSONDecodeError: + if return_structured: + return [] + return f"API Error: Failed to parse JSON response: {response_text[:100]}" if "Note" in data: + if return_structured: + return [] return f"API Rate Limit: {data['Note']}" if "Error Message" in data: + if return_structured: + return [] return f"API Error: {data['Error Message']}" # Parse news for analyst actions @@ -79,10 +84,21 @@ def get_analyst_rating_changes( text = f"{title} {summary}" # Look for analyst action keywords - is_upgrade = any(word in text for word in ["upgrade", "upgrades", "raised", "raises rating"]) - is_downgrade = any(word in text for word in ["downgrade", "downgrades", "lowered", "lowers rating"]) - is_initiated = any(word in text for word in ["initiates", "initiated", "coverage", "starts coverage"]) - is_reiterated = any(word in text for word in ["reiterates", "reiterated", "maintains", "confirms"]) + is_upgrade = any( + word in text for word in ["upgrade", "upgrades", "raised", "raises rating"] + ) + is_downgrade = any( + word in text + for word in ["downgrade", "downgrades", "lowered", "lowers rating"] + ) + is_initiated = any( + word in text + for word in ["initiates", "initiated", "coverage", "starts coverage"] + ) + is_reiterated = any( + word in text + for word in ["reiterates", "reiterated", "maintains", "confirms"] + ) # Extract tickers from article tickers = [] @@ -108,36 +124,44 @@ def get_analyst_rating_changes( hours_old = (datetime.now() - article_date).total_seconds() / 3600 for ticker in tickers[:3]: # Max 3 tickers per article - analyst_changes.append({ - "ticker": ticker, - "action": action_type, - "date": time_published[:8], - "hours_old": int(hours_old), - "headline": article.get("title", "")[:100], - "source": article.get("source", "Unknown"), - "url": article.get("url", ""), - }) + analyst_changes.append( + { + "ticker": ticker, + "action": action_type, + "date": time_published[:8], + "hours_old": int(hours_old), + "headline": article.get("title", "")[:100], + "source": article.get("source", "Unknown"), + "url": article.get("url", ""), + } + ) - except (ValueError, KeyError) as e: + except (ValueError, KeyError): continue # Remove duplicates (keep most recent per ticker) seen_tickers = {} for change in analyst_changes: ticker = change["ticker"] - if ticker not in seen_tickers or change["hours_old"] < seen_tickers[ticker]["hours_old"]: + if ( + ticker not in seen_tickers + or change["hours_old"] < seen_tickers[ticker]["hours_old"] + ): seen_tickers[ticker] = change # Sort by freshness (most recent first) - sorted_changes = sorted( - seen_tickers.values(), - key=lambda x: x["hours_old"] - )[:top_n] + sorted_changes = sorted(seen_tickers.values(), key=lambda x: x["hours_old"])[:top_n] # Format output if not sorted_changes: + if return_structured: + return [] return f"No analyst rating changes found in the last {lookback_days} days" + # Return structured data if requested + if return_structured: + return sorted_changes + report = f"# Analyst Rating Changes - Last {lookback_days} Days\n\n" report += f"**Tracking**: {', '.join(change_types)}\n\n" report += f"**Found**: {len(sorted_changes)} recent analyst actions\n\n" @@ -146,7 +170,11 @@ def get_analyst_rating_changes( report += "|--------|--------|--------|-----------|----------|\n" for change in sorted_changes: - freshness = "🔥 FRESH" if change["hours_old"] < 24 else "🟢 Recent" if change["hours_old"] < 72 else "Older" + freshness = ( + "🔥 FRESH" + if change["hours_old"] < 24 + else "🟢 Recent" if change["hours_old"] < 72 else "Older" + ) report += f"| {change['ticker']} | " report += f"{change['action'].upper()} | " @@ -161,9 +189,9 @@ def get_analyst_rating_changes( return report - except requests.exceptions.RequestException as e: - return f"Error fetching analyst rating changes: {str(e)}" except Exception as e: + if return_structured: + return [] return f"Unexpected error in analyst rating detection: {str(e)}" diff --git a/tradingagents/dataflows/alpha_vantage_common.py b/tradingagents/dataflows/alpha_vantage_common.py index 55138892..fc0948b4 100644 --- a/tradingagents/dataflows/alpha_vantage_common.py +++ b/tradingagents/dataflows/alpha_vantage_common.py @@ -1,25 +1,29 @@ -import os -import requests -import pandas as pd import json from datetime import datetime from io import StringIO from typing import Union +import pandas as pd +import requests + +from tradingagents.config import config +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + API_BASE_URL = "https://www.alphavantage.co/query" + def get_api_key() -> str: """Retrieve the API key for Alpha Vantage from environment variables.""" - api_key = os.getenv("ALPHA_VANTAGE_API_KEY") - if not api_key: - raise ValueError("ALPHA_VANTAGE_API_KEY environment variable is not set.") - return api_key + return config.validate_key("alpha_vantage_api_key", "Alpha Vantage") + def format_datetime_for_api(date_input) -> str: """Convert various date formats to YYYYMMDDTHHMM format required by Alpha Vantage API.""" if isinstance(date_input, str): # If already in correct format, return as-is - if len(date_input) == 13 and 'T' in date_input: + if len(date_input) == 13 and "T" in date_input: return date_input # Try to parse common date formats try: @@ -36,39 +40,44 @@ def format_datetime_for_api(date_input) -> str: else: raise ValueError(f"Date must be string or datetime object, got {type(date_input)}") + class AlphaVantageRateLimitError(Exception): """Exception raised when Alpha Vantage API rate limit is exceeded.""" + pass + def _make_api_request(function_name: str, params: dict) -> Union[dict, str]: """Helper function to make API requests and handle responses. - + Raises: AlphaVantageRateLimitError: When API rate limit is exceeded """ # Create a copy of params to avoid modifying the original api_params = params.copy() - api_params.update({ - "function": function_name, - "apikey": get_api_key(), - "source": "trading_agents", - }) - + api_params.update( + { + "function": function_name, + "apikey": get_api_key(), + "source": "trading_agents", + } + ) + # Handle entitlement parameter if present in params or global variable - current_entitlement = globals().get('_current_entitlement') + current_entitlement = globals().get("_current_entitlement") entitlement = api_params.get("entitlement") or current_entitlement - + if entitlement: api_params["entitlement"] = entitlement elif "entitlement" in api_params: # Remove entitlement if it's None or empty api_params.pop("entitlement", None) - + response = requests.get(API_BASE_URL, params=api_params) response.raise_for_status() response_text = response.text - + # Check if response is JSON (error responses are typically JSON) try: response_json = json.loads(response_text) @@ -76,7 +85,9 @@ def _make_api_request(function_name: str, params: dict) -> Union[dict, str]: if "Information" in response_json: info_message = response_json["Information"] if "rate limit" in info_message.lower() or "api key" in info_message.lower(): - raise AlphaVantageRateLimitError(f"Alpha Vantage rate limit exceeded: {info_message}") + raise AlphaVantageRateLimitError( + f"Alpha Vantage rate limit exceeded: {info_message}" + ) except json.JSONDecodeError: # Response is not JSON (likely CSV data), which is normal pass @@ -84,7 +95,6 @@ def _make_api_request(function_name: str, params: dict) -> Union[dict, str]: return response_text - def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> str: """ Filter CSV data to include only rows within the specified date range. @@ -119,5 +129,5 @@ def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> except Exception as e: # If filtering fails, return original data with a warning - print(f"Warning: Failed to filter CSV data by date range: {e}") + logger.warning(f"Failed to filter CSV data by date range: {e}") return csv_data diff --git a/tradingagents/dataflows/alpha_vantage_fundamentals.py b/tradingagents/dataflows/alpha_vantage_fundamentals.py index 8b92faa6..e10e0b76 100644 --- a/tradingagents/dataflows/alpha_vantage_fundamentals.py +++ b/tradingagents/dataflows/alpha_vantage_fundamentals.py @@ -74,4 +74,3 @@ def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = } return _make_api_request("INCOME_STATEMENT", params) - diff --git a/tradingagents/dataflows/alpha_vantage_indicator.py b/tradingagents/dataflows/alpha_vantage_indicator.py index 6225b9bb..c7fcb255 100644 --- a/tradingagents/dataflows/alpha_vantage_indicator.py +++ b/tradingagents/dataflows/alpha_vantage_indicator.py @@ -1,5 +1,10 @@ +from tradingagents.utils.logger import get_logger + from .alpha_vantage_common import _make_api_request +logger = get_logger(__name__) + + def get_indicator( symbol: str, indicator: str, @@ -7,7 +12,7 @@ def get_indicator( look_back_days: int, interval: str = "daily", time_period: int = 14, - series_type: str = "close" + series_type: str = "close", ) -> str: """ Returns Alpha Vantage technical indicator values over a time window. @@ -25,6 +30,7 @@ def get_indicator( String containing indicator values and description """ from datetime import datetime + from dateutil.relativedelta import relativedelta supported_indicators = { @@ -39,7 +45,7 @@ def get_indicator( "boll_ub": ("Bollinger Upper Band", "close"), "boll_lb": ("Bollinger Lower Band", "close"), "atr": ("ATR", None), - "vwma": ("VWMA", "close") + "vwma": ("VWMA", "close"), } indicator_descriptions = { @@ -54,7 +60,7 @@ def get_indicator( "boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.", "boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.", "atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.", - "vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses." + "vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.", } if indicator not in supported_indicators: @@ -75,73 +81,100 @@ def get_indicator( try: # Get indicator data for the period if indicator == "close_50_sma": - data = _make_api_request("SMA", { - "symbol": symbol, - "interval": interval, - "time_period": "50", - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "SMA", + { + "symbol": symbol, + "interval": interval, + "time_period": "50", + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "close_200_sma": - data = _make_api_request("SMA", { - "symbol": symbol, - "interval": interval, - "time_period": "200", - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "SMA", + { + "symbol": symbol, + "interval": interval, + "time_period": "200", + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "close_10_ema": - data = _make_api_request("EMA", { - "symbol": symbol, - "interval": interval, - "time_period": "10", - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "EMA", + { + "symbol": symbol, + "interval": interval, + "time_period": "10", + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "macd": - data = _make_api_request("MACD", { - "symbol": symbol, - "interval": interval, - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "MACD", + { + "symbol": symbol, + "interval": interval, + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "macds": - data = _make_api_request("MACD", { - "symbol": symbol, - "interval": interval, - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "MACD", + { + "symbol": symbol, + "interval": interval, + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "macdh": - data = _make_api_request("MACD", { - "symbol": symbol, - "interval": interval, - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "MACD", + { + "symbol": symbol, + "interval": interval, + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "rsi": - data = _make_api_request("RSI", { - "symbol": symbol, - "interval": interval, - "time_period": str(time_period), - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "RSI", + { + "symbol": symbol, + "interval": interval, + "time_period": str(time_period), + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator in ["boll", "boll_ub", "boll_lb"]: - data = _make_api_request("BBANDS", { - "symbol": symbol, - "interval": interval, - "time_period": "20", - "series_type": series_type, - "datatype": "csv" - }) + data = _make_api_request( + "BBANDS", + { + "symbol": symbol, + "interval": interval, + "time_period": "20", + "series_type": series_type, + "datatype": "csv", + }, + ) elif indicator == "atr": - data = _make_api_request("ATR", { - "symbol": symbol, - "interval": interval, - "time_period": str(time_period), - "datatype": "csv" - }) + data = _make_api_request( + "ATR", + { + "symbol": symbol, + "interval": interval, + "time_period": str(time_period), + "datatype": "csv", + }, + ) elif indicator == "vwma": # Alpha Vantage doesn't have direct VWMA, so we'll return an informative message # In a real implementation, this would need to be calculated from OHLCV data @@ -150,23 +183,30 @@ def get_indicator( return f"Error: Indicator {indicator} not implemented yet." # Parse CSV data and extract values for the date range - lines = data.strip().split('\n') + lines = data.strip().split("\n") if len(lines) < 2: return f"Error: No data returned for {indicator}" # Parse header and data - header = [col.strip() for col in lines[0].split(',')] + header = [col.strip() for col in lines[0].split(",")] try: - date_col_idx = header.index('time') + date_col_idx = header.index("time") except ValueError: return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}" # Map internal indicator names to expected CSV column names from Alpha Vantage col_name_map = { - "macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist", - "boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band", - "rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA", - "close_50_sma": "SMA", "close_200_sma": "SMA" + "macd": "MACD", + "macds": "MACD_Signal", + "macdh": "MACD_Hist", + "boll": "Real Middle Band", + "boll_ub": "Real Upper Band", + "boll_lb": "Real Lower Band", + "rsi": "RSI", + "atr": "ATR", + "close_10_ema": "EMA", + "close_50_sma": "SMA", + "close_200_sma": "SMA", } target_col_name = col_name_map.get(indicator) @@ -184,7 +224,7 @@ def get_indicator( for line in lines[1:]: if not line.strip(): continue - values = line.split(',') + values = line.split(",") if len(values) > value_col_idx: try: date_str = values[date_col_idx].strip() @@ -218,5 +258,5 @@ def get_indicator( return result_str except Exception as e: - print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}") + logger.error(f"Error getting Alpha Vantage indicator data for {indicator}: {e}") return f"Error retrieving {indicator} data: {str(e)}" diff --git a/tradingagents/dataflows/alpha_vantage_news.py b/tradingagents/dataflows/alpha_vantage_news.py index 8002735e..b8f13469 100644 --- a/tradingagents/dataflows/alpha_vantage_news.py +++ b/tradingagents/dataflows/alpha_vantage_news.py @@ -1,7 +1,11 @@ -from typing import Union, Dict, Optional +from typing import Dict, Union + from .alpha_vantage_common import _make_api_request, format_datetime_for_api -def get_news(ticker: str = None, start_date: str = None, end_date: str = None, query: str = None) -> Union[Dict[str, str], str]: + +def get_news( + ticker: str = None, start_date: str = None, end_date: str = None, query: str = None +) -> Union[Dict[str, str], str]: """Returns live and historical market news & sentiment data. Args: @@ -25,11 +29,13 @@ def get_news(ticker: str = None, start_date: str = None, end_date: str = None, q "sort": "LATEST", "limit": "50", } - + return _make_api_request("NEWS_SENTIMENT", params) -def get_global_news(date: str, look_back_days: int = 7, limit: int = 5) -> Union[Dict[str, str], str]: +def get_global_news( + date: str, look_back_days: int = 7, limit: int = 5 +) -> Union[Dict[str, str], str]: """Returns global market news & sentiment data. Args: @@ -49,7 +55,41 @@ def get_global_news(date: str, look_back_days: int = 7, limit: int = 5) -> Union return _make_api_request("NEWS_SENTIMENT", params) -def get_insider_transactions(symbol: str = None, ticker: str = None, curr_date: str = None) -> Union[Dict[str, str], str]: + +def get_alpha_vantage_news_feed( + topics: str = None, time_from: str = None, limit: int = 50 +) -> Union[Dict[str, str], str]: + """Returns news feed from Alpha Vantage with optional topic filtering. + + Args: + topics: Comma-separated topics (e.g., "technology,finance,earnings"). + Valid topics: blockchain, earnings, ipo, mergers_and_acquisitions, + financial_markets, economy_fiscal, economy_monetary, economy_macro, + energy_transportation, finance, life_sciences, manufacturing, + real_estate, retail_wholesale, technology + time_from: Start time in format YYYYMMDDTHHMM (e.g., "20240101T0000"). + limit: Maximum number of articles to return. + + Returns: + Dictionary containing news sentiment data or JSON string. + """ + params = { + "sort": "LATEST", + "limit": str(limit), + } + + if topics: + params["topics"] = topics + + if time_from: + params["time_from"] = time_from + + return _make_api_request("NEWS_SENTIMENT", params) + + +def get_insider_transactions( + symbol: str = None, ticker: str = None, curr_date: str = None +) -> Union[Dict[str, str], str]: """Returns latest and historical insider transactions. Args: @@ -70,14 +110,15 @@ def get_insider_transactions(symbol: str = None, ticker: str = None, curr_date: return _make_api_request("INSIDER_TRANSACTIONS", params) + def get_insider_sentiment(symbol: str = None, ticker: str = None, curr_date: str = None) -> str: """Returns insider sentiment data derived from Alpha Vantage transactions. - + Args: symbol: Ticker symbol. ticker: Alias for symbol. curr_date: Current date. - + Returns: Formatted string containing insider sentiment analysis. """ @@ -87,24 +128,24 @@ def get_insider_sentiment(symbol: str = None, ticker: str = None, curr_date: str import json from datetime import datetime, timedelta - + # Fetch transactions params = { "symbol": target_symbol, } response_text = _make_api_request("INSIDER_TRANSACTIONS", params) - + try: data = json.loads(response_text) if "Information" in data: return f"Error: {data['Information']}" - + # Alpha Vantage INSIDER_TRANSACTIONS returns a dictionary with "symbol" and "data" (list) # or sometimes just the list depending on the endpoint version, but usually it's under a key. # Let's handle the standard response structure. # Based on docs, it returns CSV by default? No, _make_api_request handles JSON. # Actually, Alpha Vantage INSIDER_TRANSACTIONS returns JSON by default. - + # Structure check transactions = [] if "data" in data: @@ -114,16 +155,16 @@ def get_insider_sentiment(symbol: str = None, ticker: str = None, curr_date: str else: # If we can't find the list, return the raw text return f"Raw Data: {str(data)[:500]}" - + # Filter and Aggregate # We want recent transactions (e.g. last 3 months) if curr_date: curr_dt = datetime.strptime(curr_date, "%Y-%m-%d") else: curr_dt = datetime.now() - + start_dt = curr_dt - timedelta(days=90) - + relevant_txs = [] for tx in transactions: # Date format in AV is usually YYYY-MM-DD @@ -132,44 +173,44 @@ def get_insider_sentiment(symbol: str = None, ticker: str = None, curr_date: str if not tx_date_str: continue tx_date = datetime.strptime(tx_date_str, "%Y-%m-%d") - + if start_dt <= tx_date <= curr_dt: relevant_txs.append(tx) except ValueError: continue - + if not relevant_txs: return f"No insider transactions found for {symbol} in the 90 days before {curr_date}." - + # Calculate metrics total_bought = 0 total_sold = 0 net_shares = 0 - + for tx in relevant_txs: shares = int(float(tx.get("shares", 0))) # acquisition_or_disposal: "A" (Acquisition) or "D" (Disposal) # transaction_code: "P" (Purchase), "S" (Sale) # We can use acquisition_or_disposal if available, or transaction_code - + code = tx.get("acquisition_or_disposal") if not code: # Fallback to transaction code logic if needed, but A/D is standard for AV pass - + if code == "A": total_bought += shares net_shares += shares elif code == "D": total_sold += shares net_shares -= shares - + sentiment = "NEUTRAL" if net_shares > 0: sentiment = "POSITIVE" elif net_shares < 0: sentiment = "NEGATIVE" - + report = f"## Insider Sentiment for {symbol} (Last 90 Days)\n" report += f"**Overall Sentiment:** {sentiment}\n" report += f"**Net Shares:** {net_shares:,}\n" @@ -177,13 +218,13 @@ def get_insider_sentiment(symbol: str = None, ticker: str = None, curr_date: str report += f"**Total Sold:** {total_sold:,}\n" report += f"**Transaction Count:** {len(relevant_txs)}\n\n" report += "### Recent Transactions:\n" - + # List top 5 recent relevant_txs.sort(key=lambda x: x.get("transaction_date", ""), reverse=True) for tx in relevant_txs[:5]: report += f"- {tx.get('transaction_date')}: {tx.get('executive')} - {tx.get('acquisition_or_disposal')} {tx.get('shares')} shares at ${tx.get('transaction_price')}\n" - + return report except Exception as e: - return f"Error processing insider sentiment: {str(e)}\nRaw response: {response_text[:200]}" \ No newline at end of file + return f"Error processing insider sentiment: {str(e)}\nRaw response: {response_text[:200]}" diff --git a/tradingagents/dataflows/alpha_vantage_stock.py b/tradingagents/dataflows/alpha_vantage_stock.py index 61ca7970..8a14143d 100644 --- a/tradingagents/dataflows/alpha_vantage_stock.py +++ b/tradingagents/dataflows/alpha_vantage_stock.py @@ -1,11 +1,9 @@ from datetime import datetime -from .alpha_vantage_common import _make_api_request, _filter_csv_by_date_range -def get_stock( - symbol: str, - start_date: str, - end_date: str -) -> str: +from .alpha_vantage_common import _filter_csv_by_date_range, _make_api_request + + +def get_stock(symbol: str, start_date: str, end_date: str) -> str: """ Returns raw daily OHLCV values, adjusted close values, and historical split/dividend events filtered to the specified date range. @@ -38,48 +36,77 @@ def get_stock( return _filter_csv_by_date_range(response, start_date, end_date) -def get_top_gainers_losers(limit: int = 10) -> str: +def get_top_gainers_losers(limit: int = 10, return_structured: bool = False): """ Returns the top gainers, losers, and most active stocks from Alpha Vantage. + + Args: + limit: Maximum number of items per category + return_structured: If True, returns dict with raw data instead of markdown + + Returns: + If return_structured=True: dict with 'gainers', 'losers', 'most_active' lists + If return_structured=False: Formatted markdown string """ params = {} - + # This returns a JSON string response_text = _make_api_request("TOP_GAINERS_LOSERS", params) - + try: import json + data = json.loads(response_text) - + if "top_gainers" not in data: + if return_structured: + return {"error": f"Unexpected response format: {response_text[:200]}..."} return f"Error: Unexpected response format: {response_text[:200]}..." - + + # Apply limit to data + gainers = data.get("top_gainers", [])[:limit] + losers = data.get("top_losers", [])[:limit] + most_active = data.get("most_actively_traded", [])[:limit] + + # Return structured data if requested + if return_structured: + return { + "gainers": gainers, + "losers": losers, + "most_active": most_active, + } + + # Format as markdown report report = "## Top Market Movers (Alpha Vantage)\n\n" - + # Top Gainers report += "### Top Gainers\n" report += "| Ticker | Price | Change % | Volume |\n" report += "|--------|-------|----------|--------|\n" - for item in data.get("top_gainers", [])[:limit]: + for item in gainers: report += f"| {item['ticker']} | {item['price']} | {item['change_percentage']} | {item['volume']} |\n" - + # Top Losers report += "\n### Top Losers\n" report += "| Ticker | Price | Change % | Volume |\n" report += "|--------|-------|----------|--------|\n" - for item in data.get("top_losers", [])[:limit]: + for item in losers: report += f"| {item['ticker']} | {item['price']} | {item['change_percentage']} | {item['volume']} |\n" - + # Most Active report += "\n### Most Active\n" report += "| Ticker | Price | Change % | Volume |\n" report += "|--------|-------|----------|--------|\n" - for item in data.get("most_actively_traded", [])[:limit]: + for item in most_active: report += f"| {item['ticker']} | {item['price']} | {item['change_percentage']} | {item['volume']} |\n" - + return report - + except json.JSONDecodeError: + if return_structured: + return {"error": f"Failed to parse JSON response: {response_text[:200]}..."} return f"Error: Failed to parse JSON response: {response_text[:200]}..." except Exception as e: - return f"Error processing market movers: {str(e)}" \ No newline at end of file + if return_structured: + return {"error": str(e)} + return f"Error processing market movers: {str(e)}" diff --git a/tradingagents/dataflows/alpha_vantage_volume.py b/tradingagents/dataflows/alpha_vantage_volume.py index d528c23c..2094c321 100644 --- a/tradingagents/dataflows/alpha_vantage_volume.py +++ b/tradingagents/dataflows/alpha_vantage_volume.py @@ -3,26 +3,27 @@ Unusual Volume Detection using yfinance Identifies stocks with unusual volume but minimal price movement (accumulation signal) """ -from datetime import datetime -from typing import Annotated, List, Dict, Optional, Union import hashlib -import pandas as pd -import yfinance as yf import json -from pathlib import Path from concurrent.futures import ThreadPoolExecutor, as_completed -from tradingagents.dataflows.y_finance import _get_ticker_universe +from datetime import datetime +from pathlib import Path +from typing import Annotated, Dict, List, Optional, Union + +import pandas as pd +from tradingagents.dataflows.y_finance import _get_ticker_universe, get_ticker_history +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) -def _get_cache_path( - ticker_universe: Union[str, List[str]] -) -> Path: +def _get_cache_path(ticker_universe: Union[str, List[str]]) -> Path: """ Get the cache file path for unusual volume raw data. - + Args: ticker_universe: Universe identifier - + Returns: Path to cache file """ @@ -30,7 +31,7 @@ def _get_cache_path( current_file = Path(__file__) cache_dir = current_file.parent / "data_cache" cache_dir.mkdir(exist_ok=True) - + # Create cache key from universe only (thresholds are applied later) if isinstance(ticker_universe, str): universe_key = ticker_universe @@ -40,38 +41,38 @@ def _get_cache_path( hash_suffix = hashlib.md5(",".join(sorted(clean_tickers)).encode()).hexdigest()[:8] universe_key = f"custom_{hash_suffix}" cache_key = f"unusual_volume_raw_{universe_key}".replace(".", "_") - + return cache_dir / f"{cache_key}.json" def _load_cache(cache_path: Path) -> Optional[Dict]: """ Load cached unusual volume raw data if it exists and is from today. - + Args: cache_path: Path to cache file - + Returns: Cached results dict if valid, None otherwise """ if not cache_path.exists(): return None - + try: - with open(cache_path, 'r') as f: + with open(cache_path, "r") as f: cache_data = json.load(f) - + # Check if cache is from today - cache_date = cache_data.get('date') - today = datetime.now().strftime('%Y-%m-%d') - has_raw_data = bool(cache_data.get('raw_data')) - + cache_date = cache_data.get("date") + today = datetime.now().strftime("%Y-%m-%d") + has_raw_data = bool(cache_data.get("raw_data")) + if cache_date == today and has_raw_data: return cache_data else: # Cache is stale, return None to trigger recompute return None - + except Exception: # If cache is corrupted, return None to trigger recompute return None @@ -80,35 +81,38 @@ def _load_cache(cache_path: Path) -> Optional[Dict]: def _save_cache(cache_path: Path, raw_data: Dict[str, List[Dict]], date: str): """ Save unusual volume raw data to cache. - + Args: cache_path: Path to cache file raw_data: Raw ticker data to cache date: Date string (YYYY-MM-DD) """ try: - cache_data = { - 'date': date, - 'raw_data': raw_data, - 'timestamp': datetime.now().isoformat() - } - - with open(cache_path, 'w') as f: + cache_data = {"date": date, "raw_data": raw_data, "timestamp": datetime.now().isoformat()} + + with open(cache_path, "w") as f: json.dump(cache_data, f, indent=2) - + except Exception as e: # If caching fails, just continue without cache - print(f"Warning: Could not save cache: {e}") + logger.warning(f"Could not save cache: {e}") def _history_to_records(hist: pd.DataFrame) -> List[Dict[str, Union[str, float, int]]]: """Convert a yfinance history DataFrame to a cache-friendly list of dicts.""" - hist_for_cache = hist[["Close", "Volume"]].copy() + # Include Open price for intraday direction analysis (accumulation vs distribution) + cols_to_use = ["Close", "Volume"] + if "Open" in hist.columns: + cols_to_use = ["Open", "Close", "Volume"] + + hist_for_cache = hist[cols_to_use].copy() hist_for_cache = hist_for_cache.reset_index() date_col = "Date" if "Date" in hist_for_cache.columns else hist_for_cache.columns[0] hist_for_cache.rename(columns={date_col: "Date"}, inplace=True) - hist_for_cache["Date"] = pd.to_datetime(hist_for_cache["Date"]).dt.strftime('%Y-%m-%d') - hist_for_cache = hist_for_cache[["Date", "Close", "Volume"]] + hist_for_cache["Date"] = pd.to_datetime(hist_for_cache["Date"]).dt.strftime("%Y-%m-%d") + + final_cols = ["Date"] + cols_to_use + hist_for_cache = hist_for_cache[final_cols] return hist_for_cache.to_dict(orient="records") @@ -122,23 +126,194 @@ def _records_to_dataframe(history_records: List[Dict[str, Union[str, float, int] return hist_df +def get_cached_average_volume( + symbol: str, + lookback_days: int = 20, + curr_date: Optional[str] = None, + cache_key: str = "default", + fallback_download: bool = True, +) -> Dict[str, Union[str, float, int, None]]: + """Get average volume using cached unusual-volume data, with optional fallback download.""" + symbol = symbol.upper() + cache_path = _get_cache_path(cache_key) + cache_date = None + history_records = None + + if cache_path.exists(): + try: + with open(cache_path, "r") as f: + cache_data = json.load(f) + cache_date = cache_data.get("date") + raw_data = cache_data.get("raw_data") or {} + history_records = raw_data.get(symbol) + except Exception: + history_records = None + + source = "cache" + if not history_records and fallback_download: + history_records = _download_ticker_history( + symbol, history_period_days=max(90, lookback_days * 2) + ) + source = "download" + + if not history_records: + return { + "symbol": symbol, + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "source": source, + "cache_date": cache_date, + "error": "No volume data found", + } + + hist_df = _records_to_dataframe(history_records) + if hist_df.empty or "Volume" not in hist_df.columns: + return { + "symbol": symbol, + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "source": source, + "cache_date": cache_date, + "error": "No volume data found", + } + + if curr_date: + curr_dt = pd.to_datetime(curr_date) + hist_df = hist_df[hist_df["Date"] <= curr_dt] + + recent = hist_df.tail(lookback_days) + if recent.empty: + return { + "symbol": symbol, + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "source": source, + "cache_date": cache_date, + "error": "No recent volume data found", + } + + average_volume = float(recent["Volume"].mean()) + latest_volume = float(recent["Volume"].iloc[-1]) + + return { + "symbol": symbol, + "average_volume": average_volume, + "latest_volume": latest_volume, + "lookback_days": lookback_days, + "source": source, + "cache_date": cache_date, + } + + +def get_cached_average_volume_batch( + symbols: List[str], + lookback_days: int = 20, + curr_date: Optional[str] = None, + cache_key: str = "default", + fallback_download: bool = True, +) -> Dict[str, Dict[str, Union[str, float, int, None]]]: + """Get average volumes for multiple tickers using the cache once.""" + cache_path = _get_cache_path(cache_key) + cache_date = None + raw_data = {} + + if cache_path.exists(): + try: + with open(cache_path, "r") as f: + cache_data = json.load(f) + cache_date = cache_data.get("date") + raw_data = cache_data.get("raw_data") or {} + except Exception: + raw_data = {} + + results: Dict[str, Dict[str, Union[str, float, int, None]]] = {} + symbols_upper = [s.upper() for s in symbols if isinstance(s, str)] + + def compute_from_records(symbol: str, history_records: List[Dict[str, Union[str, float, int]]]): + hist_df = _records_to_dataframe(history_records) + if hist_df.empty or "Volume" not in hist_df.columns: + return None, None, "No volume data found" + if curr_date: + curr_dt = pd.to_datetime(curr_date) + hist_df = hist_df[hist_df["Date"] <= curr_dt] + recent = hist_df.tail(lookback_days) + if recent.empty: + return None, None, "No recent volume data found" + avg_volume = float(recent["Volume"].mean()) + latest_volume = float(recent["Volume"].iloc[-1]) + return avg_volume, latest_volume, None + + missing = [] + for symbol in symbols_upper: + history_records = raw_data.get(symbol) + if history_records: + avg_volume, latest_volume, error = compute_from_records(symbol, history_records) + results[symbol] = { + "symbol": symbol, + "average_volume": avg_volume, + "latest_volume": latest_volume, + "lookback_days": lookback_days, + "source": "cache", + "cache_date": cache_date, + "error": error, + } + else: + missing.append(symbol) + + if fallback_download and missing: + for symbol in missing: + history_records = _download_ticker_history( + symbol, history_period_days=max(90, lookback_days * 2) + ) + if history_records: + avg_volume, latest_volume, error = compute_from_records(symbol, history_records) + results[symbol] = { + "symbol": symbol, + "average_volume": avg_volume, + "latest_volume": latest_volume, + "lookback_days": lookback_days, + "source": "download", + "cache_date": cache_date, + "error": error, + } + else: + results[symbol] = { + "symbol": symbol, + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "source": "download", + "cache_date": cache_date, + "error": "No volume data found", + } + + return results + + def _evaluate_unusual_volume_from_history( ticker: str, history_records: List[Dict[str, Union[str, float, int]]], min_volume_multiple: float, max_price_change: float, - lookback_days: int = 30 + lookback_days: int = 30, ) -> Optional[Dict]: """ Evaluate a ticker's cached history for unusual volume patterns. - + + Now includes DIRECTION ANALYSIS to distinguish: + - Accumulation (high volume + price holds/rises) = BULLISH - keep + - Distribution (high volume + price drops) = BEARISH - skip + Args: ticker: Stock ticker symbol history_records: Cached price/volume history records min_volume_multiple: Minimum volume multiple vs average max_price_change: Maximum absolute price change percentage lookback_days: Days to look back for average volume calculation - + Returns: Dict with ticker data if unusual volume detected, None otherwise """ @@ -148,48 +323,76 @@ def _evaluate_unusual_volume_from_history( return None current_data = hist.iloc[-1] - current_volume = current_data['Volume'] - current_price = current_data['Close'] + current_volume = current_data["Volume"] + current_price = current_data["Close"] - avg_volume = hist['Volume'].iloc[-(lookback_days+1):-1].mean() + avg_volume = hist["Volume"].iloc[-(lookback_days + 1) : -1].mean() if pd.isna(avg_volume) or avg_volume <= 0: return None volume_ratio = current_volume / avg_volume - - price_start = hist['Close'].iloc[-(lookback_days+1)] + + price_start = hist["Close"].iloc[-(lookback_days + 1)] price_end = current_price price_change_pct = ((price_end - price_start) / price_start) * 100 - + + # === DIRECTION ANALYSIS (NEW) === + # Check intraday direction to distinguish accumulation from distribution + intraday_change_pct = 0.0 + direction = "neutral" + + if "Open" in current_data and pd.notna(current_data["Open"]): + open_price = current_data["Open"] + if open_price > 0: + intraday_change_pct = ((current_price - open_price) / open_price) * 100 + + # Classify direction based on intraday movement + if intraday_change_pct > 0.5: + direction = "bullish" # Closed higher than open + elif intraday_change_pct < -1.5: + direction = "bearish" # Closed significantly lower than open + else: + direction = "neutral" # Flat intraday + + # === DISTRIBUTION FILTER (NEW) === + # Skip if high volume + bearish direction = likely distribution (selling) + if volume_ratio >= min_volume_multiple and direction == "bearish": + # This is likely DISTRIBUTION - smart money selling, not accumulation + # Return None to filter it out + return None + # Filter: High volume multiple AND low price change (accumulation signal) if volume_ratio >= min_volume_multiple and abs(price_change_pct) < max_price_change: - # Determine signal type - if abs(price_change_pct) < 2.0: + # Determine signal type with direction context + if direction == "bullish" and abs(price_change_pct) < 3.0: + signal = "strong_accumulation" # Best signal: high volume, rising intraday + elif abs(price_change_pct) < 2.0: signal = "accumulation" elif abs(price_change_pct) < 5.0: signal = "moderate_activity" else: signal = "building_momentum" - + return { "ticker": ticker.upper(), "volume": int(current_volume), "price": round(float(current_price), 2), "price_change_pct": round(price_change_pct, 2), + "intraday_change_pct": round(intraday_change_pct, 2), + "direction": direction, "volume_ratio": round(volume_ratio, 2), "avg_volume": int(avg_volume), - "signal": signal + "signal": signal, } - + return None - + except Exception: return None def _download_ticker_history( - ticker: str, - history_period_days: int = 90 + ticker: str, history_period_days: int = 90 ) -> Optional[List[Dict[str, Union[str, float, int]]]]: """ Download raw history for a ticker and return cache-friendly records. @@ -202,8 +405,7 @@ def _download_ticker_history( List of history records or None if insufficient data """ try: - stock = yf.Ticker(ticker.upper()) - hist = stock.history(period=f"{history_period_days}d") + hist = get_ticker_history(ticker, period=f"{history_period_days}d") if hist.empty: return None @@ -239,7 +441,7 @@ def download_volume_data( Returns: Dict mapping ticker symbols to their history records """ - today = datetime.now().strftime('%Y-%m-%d') + today = datetime.now().strftime("%Y-%m-%d") # Get cache path (we always need it for saving) cache_path = _get_cache_path(cache_key) @@ -249,16 +451,16 @@ def download_volume_data( cached_data = _load_cache(cache_path) # Check if cache is fresh (from today) - if cached_data and cached_data.get('date') == today: - print(f" Using cached volume data from {cached_data['date']}") - return cached_data['raw_data'] + if cached_data and cached_data.get("date") == today: + logger.info(f"Using cached volume data from {cached_data['date']}") + return cached_data["raw_data"] elif cached_data: - print(f" Cache is stale (from {cached_data.get('date')}), re-downloading...") + logger.info(f"Cache is stale (from {cached_data.get('date')}), re-downloading...") else: - print(f" Skipping cache (use_cache=False), forcing fresh download...") + logger.info("Skipping cache (use_cache=False), forcing fresh download...") # Download fresh data - print(f" Downloading {history_period_days} days of volume data for {len(tickers)} tickers...") + logger.info(f"Downloading {history_period_days} days of volume data for {len(tickers)} tickers...") raw_data = {} with ThreadPoolExecutor(max_workers=15) as executor: @@ -271,7 +473,7 @@ def download_volume_data( for future in as_completed(futures): completed += 1 if completed % 50 == 0: - print(f" Progress: {completed}/{len(tickers)} tickers downloaded...") + logger.info(f"Progress: {completed}/{len(tickers)} tickers downloaded...") ticker_symbol = futures[future].upper() history_records = future.result() @@ -280,7 +482,7 @@ def download_volume_data( # Always save fresh data to cache (so it's available next time) if cache_path and raw_data: - print(f" Saving {len(raw_data)} tickers to cache...") + logger.info(f"Saving {len(raw_data)} tickers to cache...") _save_cache(cache_path, raw_data, today) return raw_data @@ -294,7 +496,8 @@ def get_unusual_volume( tickers: Annotated[Optional[List[str]], "Custom ticker list or None to use config file"] = None, max_tickers_to_scan: Annotated[int, "Maximum number of tickers to scan"] = 3000, use_cache: Annotated[bool, "Use cached raw data when available"] = True, -) -> str: + return_structured: Annotated[bool, "Return list of dicts instead of markdown"] = False, +): """ Find stocks with unusual volume but minimal price movement. @@ -309,13 +512,15 @@ def get_unusual_volume( tickers: Custom list of ticker symbols, or None to load from config file max_tickers_to_scan: Maximum number of tickers to scan (default: 3000, scans all) use_cache: Whether to reuse/save cached raw data + return_structured: If True, returns list of candidate dicts instead of markdown Returns: - Formatted markdown report of stocks with unusual volume + If return_structured=True: list of candidate dicts with ticker, volume_ratio, signal, etc. + If return_structured=False: Formatted markdown report """ try: lookback_days = 30 - today = datetime.now().strftime('%Y-%m-%d') + today = datetime.now().strftime("%Y-%m-%d") analysis_date = date or today ticker_list = _get_ticker_universe(tickers=tickers, max_tickers=max_tickers_to_scan) @@ -327,15 +532,13 @@ def get_unusual_volume( # Create cache key from ticker list or "default" if isinstance(tickers, list): import hashlib + cache_key = "custom_" + hashlib.md5(",".join(sorted(tickers)).encode()).hexdigest()[:8] else: cache_key = "default" raw_data = download_volume_data( - tickers=ticker_list, - history_period_days=90, - use_cache=use_cache, - cache_key=cache_key + tickers=ticker_list, history_period_days=90, use_cache=use_cache, cache_key=cache_key ) if not raw_data: @@ -352,38 +555,52 @@ def get_unusual_volume( history_records, min_volume_multiple, max_price_change, - lookback_days=lookback_days + lookback_days=lookback_days, ) if candidate: unusual_candidates.append(candidate) if not unusual_candidates: + if return_structured: + return [] return f"No stocks found with unusual volume patterns matching criteria\n\nScanned {len(ticker_list)} tickers." # Sort by volume ratio (highest first) sorted_candidates = sorted( - unusual_candidates, - key=lambda x: (x.get("volume_ratio", 0), x["volume"]), - reverse=True + unusual_candidates, key=lambda x: (x.get("volume_ratio", 0), x["volume"]), reverse=True ) # Take top N for display sorted_candidates = sorted_candidates[:top_n] + # Return structured data if requested + if return_structured: + return sorted_candidates + # Format output report = f"# Unusual Volume Detected - {analysis_date}\n\n" - report += f"**Criteria**: \n" + report += "**Criteria**: \n" report += f"- Price Change: <{max_price_change}% (accumulation pattern)\n" report += f"- Volume Multiple: Current volume ≥ {min_volume_multiple}x 30-day average\n" report += f"- Tickers Scanned: {ticker_count}\n\n" report += f"**Found**: {len(sorted_candidates)} stocks with unusual activity\n\n" report += "## Top Unusual Volume Candidates\n\n" - report += "| Ticker | Price | Volume | Avg Volume | Volume Ratio | Price Change % | Signal |\n" - report += "|--------|-------|--------|------------|--------------|----------------|--------|\n" + report += ( + "| Ticker | Price | Volume | Avg Volume | Volume Ratio | Price Change % | Signal |\n" + ) + report += ( + "|--------|-------|--------|------------|--------------|----------------|--------|\n" + ) for candidate in sorted_candidates: - volume_ratio_str = f"{candidate.get('volume_ratio', 'N/A')}x" if candidate.get('volume_ratio') else "N/A" - avg_vol_str = f"{candidate.get('avg_volume', 0):,}" if candidate.get('avg_volume') else "N/A" + volume_ratio_str = ( + f"{candidate.get('volume_ratio', 'N/A')}x" + if candidate.get("volume_ratio") + else "N/A" + ) + avg_vol_str = ( + f"{candidate.get('avg_volume', 0):,}" if candidate.get("avg_volume") else "N/A" + ) report += f"| {candidate['ticker']} | " report += f"${candidate['price']:.2f} | " report += f"{candidate['volume']:,} | " @@ -393,13 +610,19 @@ def get_unusual_volume( report += f"{candidate['signal']} |\n" report += "\n\n## Signal Definitions\n\n" + report += "- **strong_accumulation**: High volume + bullish intraday direction - Strongest buy signal\n" report += "- **accumulation**: High volume, minimal price change (<2%) - Smart money building position\n" - report += "- **moderate_activity**: Elevated volume with 2-5% price change - Early momentum\n" + report += ( + "- **moderate_activity**: Elevated volume with 2-5% price change - Early momentum\n" + ) report += "- **building_momentum**: High volume with moderate price change - Conviction building\n" + report += "\n**Note**: Distribution patterns (high volume + bearish direction) are automatically filtered out.\n" return report except Exception as e: + if return_structured: + return [] return f"Unexpected error in unusual volume detection: {str(e)}" @@ -414,11 +637,5 @@ def get_alpha_vantage_unusual_volume( ) -> str: """Alias for get_unusual_volume to match registry naming convention""" return get_unusual_volume( - date, - min_volume_multiple, - max_price_change, - top_n, - tickers, - max_tickers_to_scan, - use_cache + date, min_volume_multiple, max_price_change, top_n, tickers, max_tickers_to_scan, use_cache ) diff --git a/tradingagents/dataflows/config.py b/tradingagents/dataflows/config.py index b8a8f8aa..6d58b112 100644 --- a/tradingagents/dataflows/config.py +++ b/tradingagents/dataflows/config.py @@ -1,6 +1,7 @@ -import tradingagents.default_config as default_config from typing import Dict, Optional +import tradingagents.default_config as default_config + # Use default config but allow it to be overridden _config: Optional[Dict] = None DATA_DIR: Optional[str] = None diff --git a/tradingagents/dataflows/delisted_cache.py b/tradingagents/dataflows/delisted_cache.py new file mode 100644 index 00000000..83832aa6 --- /dev/null +++ b/tradingagents/dataflows/delisted_cache.py @@ -0,0 +1,147 @@ +""" +Delisted Cache System +--------------------- +Track tickers that consistently fail data fetches (likely delisted). + +SAFETY: Only cache tickers that: +- Passed initial format validation (not units/warrants/common words) +- Failed multiple times over multiple days +- Have consistent failure patterns (not temporary API issues) +""" + +import json +from datetime import datetime +from pathlib import Path + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +class DelistedCache: + """ + Track tickers that consistently fail data fetches (likely delisted). + + SAFETY: Only cache tickers that: + - Passed initial format validation (not units/warrants/common words) + - Failed multiple times over multiple days + - Have consistent failure patterns (not temporary API issues) + """ + + def __init__(self, cache_file="data/delisted_cache.json"): + self.cache_file = Path(cache_file) + self.cache = self._load_cache() + + def _load_cache(self): + if self.cache_file.exists(): + with open(self.cache_file, "r") as f: + return json.load(f) + return {} + + def mark_failed(self, ticker, reason="no_data", error_code=None): + """ + Record a failed data fetch for a ticker. + + Args: + ticker: Stock symbol + reason: Human-readable failure reason + error_code: Specific error (e.g., "404", "no_price_data", "empty_history") + """ + ticker = ticker.upper() + + if ticker not in self.cache: + self.cache[ticker] = { + "first_failed": datetime.now().isoformat(), + "last_failed": datetime.now().isoformat(), + "fail_count": 1, + "reason": reason, + "error_code": error_code, + "fail_dates": [datetime.now().date().isoformat()], + } + else: + self.cache[ticker]["fail_count"] += 1 + self.cache[ticker]["last_failed"] = datetime.now().isoformat() + self.cache[ticker]["reason"] = reason # Update to latest reason + + # Track unique failure dates + today = datetime.now().date().isoformat() + if today not in self.cache[ticker].get("fail_dates", []): + self.cache[ticker].setdefault("fail_dates", []).append(today) + + self._save_cache() + + def is_likely_delisted(self, ticker, fail_threshold=5, days_threshold=14, min_unique_days=3): + """ + Conservative check: ticker must fail multiple times across multiple days. + + Args: + fail_threshold: Minimum number of total failures (default: 5) + days_threshold: Must have failed within this many days (default: 14) + min_unique_days: Must have failed on at least this many different days (default: 3) + + Returns: + bool: True if ticker is likely delisted + """ + ticker = ticker.upper() + if ticker not in self.cache: + return False + + data = self.cache[ticker] + last_failed = datetime.fromisoformat(data["last_failed"]) + days_since = (datetime.now() - last_failed).days + + # Count unique failure days + unique_fail_days = len(set(data.get("fail_dates", []))) + + # Conservative criteria: + # - Must have failed at least 5 times + # - Must have failed on at least 3 different days (not just repeated same-day attempts) + # - Last failure within 14 days (don't cache stale data) + return ( + data["fail_count"] >= fail_threshold + and unique_fail_days >= min_unique_days + and days_since <= days_threshold + ) + + def get_failure_summary(self, ticker): + """Get detailed failure info for manual review.""" + ticker = ticker.upper() + if ticker not in self.cache: + return None + + data = self.cache[ticker] + return { + "ticker": ticker, + "fail_count": data["fail_count"], + "unique_days": len(set(data.get("fail_dates", []))), + "first_failed": data["first_failed"], + "last_failed": data["last_failed"], + "reason": data["reason"], + "is_likely_delisted": self.is_likely_delisted(ticker), + } + + def _save_cache(self): + self.cache_file.parent.mkdir(parents=True, exist_ok=True) + with open(self.cache_file, "w") as f: + json.dump(self.cache, f, indent=2) + + def export_review_list(self, output_file="data/delisted_review.txt"): + """Export tickers that need manual review to add to DELISTED_TICKERS.""" + likely_delisted = [ + ticker for ticker in self.cache.keys() if self.is_likely_delisted(ticker) + ] + + if not likely_delisted: + return + + with open(output_file, "w") as f: + f.write( + "# Tickers that have failed consistently (review before adding to DELISTED_TICKERS)\n\n" + ) + for ticker in sorted(likely_delisted): + summary = self.get_failure_summary(ticker) + f.write( + f"{ticker:8s} - Failed {summary['fail_count']:2d} times across {summary['unique_days']} days - {summary['reason']}\n" + ) + + logger.info(f"📝 Review list exported to: {output_file}") diff --git a/tradingagents/dataflows/discovery/analytics.py b/tradingagents/dataflows/discovery/analytics.py index 1d7c400e..2babdad2 100644 --- a/tradingagents/dataflows/discovery/analytics.py +++ b/tradingagents/dataflows/discovery/analytics.py @@ -5,6 +5,10 @@ from datetime import datetime from pathlib import Path from typing import Any, Dict, List +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + class DiscoveryAnalytics: """ @@ -18,10 +22,10 @@ class DiscoveryAnalytics: def update_performance_tracking(self): """Update performance metrics for all open recommendations.""" - print("📊 Updating recommendation performance tracking...") + logger.info("📊 Updating recommendation performance tracking...") if not self.recommendations_dir.exists(): - print(" No historical recommendations to track yet.") + logger.info("No historical recommendations to track yet.") return # Load all recommendations @@ -44,15 +48,15 @@ class DiscoveryAnalytics: ) all_recs.append(rec) except Exception as e: - print(f" Warning: Error loading {filepath}: {e}") + logger.warning(f"Error loading {filepath}: {e}") if not all_recs: - print(" No recommendations found to track.") + logger.info("No recommendations found to track.") return # Filter to only track open positions open_recs = [r for r in all_recs if r.get("status") != "closed"] - print(f" Tracking {len(open_recs)} open positions (out of {len(all_recs)} total)...") + logger.info(f"Tracking {len(open_recs)} open positions (out of {len(all_recs)} total)...") # Update performance today = datetime.now().strftime("%Y-%m-%d") @@ -109,10 +113,10 @@ class DiscoveryAnalytics: pass if updated_count > 0: - print(f" Updated {updated_count} positions") + logger.info(f"Updated {updated_count} positions") self._save_performance_db(all_recs) else: - print(" No updates needed") + logger.info("No updates needed") def _save_performance_db(self, all_recs: List[Dict]): """Save the aggregated performance database and recalculate stats.""" @@ -142,7 +146,7 @@ class DiscoveryAnalytics: with open(stats_path, "w") as f: json.dump(stats, f, indent=2) - print(" 💾 Updated performance database and statistics") + logger.info("💾 Updated performance database and statistics") def calculate_statistics(self, recommendations: list) -> dict: """Calculate aggregate statistics from historical performance.""" @@ -259,7 +263,7 @@ class DiscoveryAnalytics: return insights except Exception as e: - print(f" Warning: Could not load historical stats: {e}") + logger.warning(f"Could not load historical stats: {e}") return {"available": False, "message": "Error loading historical data"} def format_stats_summary(self, stats: dict) -> str: @@ -315,7 +319,7 @@ class DiscoveryAnalytics: try: entry_price = get_stock_price(ticker, curr_date=trade_date) except Exception as e: - print(f" Warning: Could not get entry price for {ticker}: {e}") + logger.warning(f"Could not get entry price for {ticker}: {e}") entry_price = None enriched_rankings.append( @@ -345,7 +349,7 @@ class DiscoveryAnalytics: indent=2, ) - print(f" 📊 Saved {len(enriched_rankings)} recommendations for tracking: {output_file}") + logger.info(f" 📊 Saved {len(enriched_rankings)} recommendations for tracking: {output_file}") def save_discovery_results(self, state: dict, trade_date: str, config: Dict[str, Any]): """Save full discovery results and tool logs.""" @@ -390,7 +394,7 @@ class DiscoveryAnalytics: f.write(f"- **{ticker}** ({strategy})\n") except Exception as e: - print(f" Error saving results: {e}") + logger.error(f"Error saving results: {e}") # Save as JSON try: @@ -404,19 +408,17 @@ class DiscoveryAnalytics: } json.dump(json_state, f, indent=2) except Exception as e: - print(f" Error saving JSON: {e}") + logger.error(f"Error saving JSON: {e}") # Save tool logs tool_logs = state.get("tool_logs", []) if tool_logs: tool_log_max_chars = ( - config.get("discovery", {}).get("tool_log_max_chars", 10_000) - if config - else 10_000 + config.get("discovery", {}).get("tool_log_max_chars", 10_000) if config else 10_000 ) self._save_tool_logs(results_dir, tool_logs, trade_date, tool_log_max_chars) - print(f" Results saved to: {results_dir}") + logger.info(f" Results saved to: {results_dir}") def _write_ranking_md(self, f, final_ranking): try: @@ -513,4 +515,4 @@ class DiscoveryAnalytics: f.write(f"### Output\n```\n{output}\n```\n\n") f.write("---\n\n") except Exception as e: - print(f" Error saving tool logs: {e}") + logger.error(f"Error saving tool logs: {e}") diff --git a/tradingagents/dataflows/discovery/common_utils.py b/tradingagents/dataflows/discovery/common_utils.py index 85b7d700..bd774b2c 100644 --- a/tradingagents/dataflows/discovery/common_utils.py +++ b/tradingagents/dataflows/discovery/common_utils.py @@ -1,9 +1,11 @@ """Common utilities for discovery scanners.""" -import re -import logging -from typing import List, Set, Optional -logger = logging.getLogger(__name__) +import re +from typing import List, Optional, Set + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) def get_common_stopwords() -> Set[str]: @@ -14,23 +16,84 @@ def get_common_stopwords() -> Set[str]: """ return { # Common words - 'THE', 'AND', 'FOR', 'ARE', 'BUT', 'NOT', 'YOU', 'ALL', 'CAN', - 'HER', 'WAS', 'ONE', 'OUR', 'OUT', 'DAY', 'WHO', 'HAS', 'HAD', - 'NEW', 'NOW', 'GET', 'GOT', 'PUT', 'SET', 'RUN', 'TOP', 'BIG', + "THE", + "AND", + "FOR", + "ARE", + "BUT", + "NOT", + "YOU", + "ALL", + "CAN", + "HER", + "WAS", + "ONE", + "OUR", + "OUT", + "DAY", + "WHO", + "HAS", + "HAD", + "NEW", + "NOW", + "GET", + "GOT", + "PUT", + "SET", + "RUN", + "TOP", + "BIG", # Financial terms - 'CEO', 'CFO', 'CTO', 'COO', 'USD', 'USA', 'SEC', 'IPO', 'ETF', - 'NYSE', 'NASDAQ', 'WSB', 'DD', 'YOLO', 'FD', 'ATH', 'ATL', 'GDP', - 'STOCK', 'STOCKS', 'MARKET', 'NEWS', 'PRICE', 'TRADE', 'SALES', + "CEO", + "CFO", + "CTO", + "COO", + "USD", + "USA", + "SEC", + "IPO", + "ETF", + "NYSE", + "NASDAQ", + "WSB", + "DD", + "YOLO", + "FD", + "ATH", + "ATL", + "GDP", + "STOCK", + "STOCKS", + "MARKET", + "NEWS", + "PRICE", + "TRADE", + "SALES", # Time - 'JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', - 'OCT', 'NOV', 'DEC', 'MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT', 'SUN', + "JAN", + "FEB", + "MAR", + "APR", + "MAY", + "JUN", + "JUL", + "AUG", + "SEP", + "OCT", + "NOV", + "DEC", + "MON", + "TUE", + "WED", + "THU", + "FRI", + "SAT", + "SUN", } def extract_tickers_from_text( - text: str, - stop_words: Optional[Set[str]] = None, - max_text_length: int = 100_000 + text: str, stop_words: Optional[Set[str]] = None, max_text_length: int = 100_000 ) -> List[str]: """Extract valid ticker symbols from text. @@ -51,13 +114,11 @@ def extract_tickers_from_text( """ # Truncate oversized text to prevent ReDoS if len(text) > max_text_length: - logger.warning( - f"Truncating oversized text from {len(text)} to {max_text_length} chars" - ) + logger.warning(f"Truncating oversized text from {len(text)} to {max_text_length} chars") text = text[:max_text_length] # Match: $TICKER or standalone TICKER (2-5 uppercase letters) - ticker_pattern = r'\b([A-Z]{2,5})\b|\$([A-Z]{2,5})' + ticker_pattern = r"\b([A-Z]{2,5})\b|\$([A-Z]{2,5})" matches = re.findall(ticker_pattern, text) # Flatten tuples and deduplicate @@ -82,7 +143,7 @@ def validate_ticker_format(ticker: str) -> bool: if not ticker or not isinstance(ticker, str): return False - return bool(re.match(r'^[A-Z]{2,5}$', ticker.strip().upper())) + return bool(re.match(r"^[A-Z]{2,5}$", ticker.strip().upper())) def validate_candidate_structure(candidate: dict) -> bool: @@ -94,7 +155,7 @@ def validate_candidate_structure(candidate: dict) -> bool: Returns: True if candidate has all required keys with valid types """ - required_keys = {'ticker', 'source', 'context', 'priority'} + required_keys = {"ticker", "source", "context", "priority"} if not isinstance(candidate, dict): return False @@ -105,12 +166,12 @@ def validate_candidate_structure(candidate: dict) -> bool: return False # Validate ticker format - if not validate_ticker_format(candidate.get('ticker', '')): + if not validate_ticker_format(candidate.get("ticker", "")): logger.warning(f"Invalid ticker format: {candidate.get('ticker')}") return False # Validate priority is string - if not isinstance(candidate.get('priority'), str): + if not isinstance(candidate.get("priority"), str): logger.warning(f"Invalid priority type: {type(candidate.get('priority'))}") return False diff --git a/tradingagents/dataflows/discovery/discovery_config.py b/tradingagents/dataflows/discovery/discovery_config.py new file mode 100644 index 00000000..77be1217 --- /dev/null +++ b/tradingagents/dataflows/discovery/discovery_config.py @@ -0,0 +1,210 @@ +"""Typed discovery configuration — single source of truth for all discovery consumers.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List + + +@dataclass +class FilterConfig: + """Filter-stage settings (from discovery.filters.*).""" + + min_average_volume: int = 500_000 + volume_lookback_days: int = 10 + filter_same_day_movers: bool = True + intraday_movement_threshold: float = 10.0 + filter_recent_movers: bool = True + recent_movement_lookback_days: int = 7 + recent_movement_threshold: float = 10.0 + recent_mover_action: str = "filter" + # Volume / compression detection + volume_cache_key: str = "default" + min_market_cap: int = 0 + compression_atr_pct_max: float = 2.0 + compression_bb_width_max: float = 6.0 + compression_min_volume_ratio: float = 1.3 + + +@dataclass +class EnrichmentConfig: + """Enrichment-stage settings (from discovery.enrichment.*).""" + + batch_news_vendor: str = "google" + batch_news_batch_size: int = 150 + news_lookback_days: float = 0.5 + context_max_snippets: int = 2 + context_snippet_max_chars: int = 140 + earnings_lookforward_days: int = 30 + + +@dataclass +class RankerConfig: + """Ranker settings (from discovery root level).""" + + max_candidates_to_analyze: int = 200 + analyze_all_candidates: bool = False + final_recommendations: int = 15 + truncate_ranking_context: bool = False + max_news_chars: int = 500 + max_insider_chars: int = 300 + max_recommendations_chars: int = 300 + + +@dataclass +class ChartConfig: + """Console price chart settings (from discovery root level).""" + + enabled: bool = True + library: str = "plotille" + windows: List[str] = field(default_factory=lambda: ["1d", "7d", "1m", "6m", "1y"]) + lookback_days: int = 30 + width: int = 60 + height: int = 12 + max_tickers: int = 10 + show_movement_stats: bool = True + + +@dataclass +class LoggingConfig: + """Tool execution logging settings (from discovery root level).""" + + log_tool_calls: bool = True + log_tool_calls_console: bool = False + log_prompts_console: bool = False # Show LLM prompts in console (always saved to log file) + tool_log_max_chars: int = 10_000 + tool_log_exclude: List[str] = field(default_factory=lambda: ["validate_ticker"]) + + +@dataclass +class DiscoveryConfig: + """ + Consolidated discovery configuration. + + All defaults match ``default_config.py``. Consumers should create an + instance via ``DiscoveryConfig.from_config(raw_config)`` rather than + reaching into the raw dict themselves. + """ + + # Nested configs + filters: FilterConfig = field(default_factory=FilterConfig) + enrichment: EnrichmentConfig = field(default_factory=EnrichmentConfig) + ranker: RankerConfig = field(default_factory=RankerConfig) + charts: ChartConfig = field(default_factory=ChartConfig) + logging: LoggingConfig = field(default_factory=LoggingConfig) + + # Flat settings at discovery root level + deep_dive_max_workers: int = 1 + discovery_mode: str = "hybrid" + + @classmethod + def from_config(cls, raw_config: Dict[str, Any]) -> DiscoveryConfig: + """Build a ``DiscoveryConfig`` from the raw application config dict.""" + disc = raw_config.get("discovery", {}) + + # Default instances — used to read fallback values for fields that + # use default_factory (which aren't available as class-level attrs). + _fd = FilterConfig() + _ed = EnrichmentConfig() + _rd = RankerConfig() + _cd = ChartConfig() + _ld = LoggingConfig() + + # Filters — nested under "filters" key, fallback to root for old configs + f = disc.get("filters", disc) + filters = FilterConfig( + min_average_volume=f.get("min_average_volume", _fd.min_average_volume), + volume_lookback_days=f.get("volume_lookback_days", _fd.volume_lookback_days), + filter_same_day_movers=f.get("filter_same_day_movers", _fd.filter_same_day_movers), + intraday_movement_threshold=f.get( + "intraday_movement_threshold", _fd.intraday_movement_threshold + ), + filter_recent_movers=f.get("filter_recent_movers", _fd.filter_recent_movers), + recent_movement_lookback_days=f.get( + "recent_movement_lookback_days", _fd.recent_movement_lookback_days + ), + recent_movement_threshold=f.get( + "recent_movement_threshold", _fd.recent_movement_threshold + ), + recent_mover_action=f.get("recent_mover_action", _fd.recent_mover_action), + volume_cache_key=f.get("volume_cache_key", _fd.volume_cache_key), + min_market_cap=f.get("min_market_cap", _fd.min_market_cap), + compression_atr_pct_max=f.get("compression_atr_pct_max", _fd.compression_atr_pct_max), + compression_bb_width_max=f.get( + "compression_bb_width_max", _fd.compression_bb_width_max + ), + compression_min_volume_ratio=f.get( + "compression_min_volume_ratio", _fd.compression_min_volume_ratio + ), + ) + + # Enrichment — nested under "enrichment" key, fallback to root + e = disc.get("enrichment", disc) + enrichment = EnrichmentConfig( + batch_news_vendor=e.get("batch_news_vendor", _ed.batch_news_vendor), + batch_news_batch_size=e.get("batch_news_batch_size", _ed.batch_news_batch_size), + news_lookback_days=e.get("news_lookback_days", _ed.news_lookback_days), + context_max_snippets=e.get("context_max_snippets", _ed.context_max_snippets), + context_snippet_max_chars=e.get( + "context_snippet_max_chars", _ed.context_snippet_max_chars + ), + earnings_lookforward_days=e.get( + "earnings_lookforward_days", _ed.earnings_lookforward_days + ), + ) + + # Ranker + ranker = RankerConfig( + max_candidates_to_analyze=disc.get( + "max_candidates_to_analyze", _rd.max_candidates_to_analyze + ), + analyze_all_candidates=disc.get( + "analyze_all_candidates", _rd.analyze_all_candidates + ), + final_recommendations=disc.get("final_recommendations", _rd.final_recommendations), + truncate_ranking_context=disc.get( + "truncate_ranking_context", _rd.truncate_ranking_context + ), + max_news_chars=disc.get("max_news_chars", _rd.max_news_chars), + max_insider_chars=disc.get("max_insider_chars", _rd.max_insider_chars), + max_recommendations_chars=disc.get( + "max_recommendations_chars", _rd.max_recommendations_chars + ), + ) + + # Charts — keys prefixed with "price_chart_" at discovery root level + charts = ChartConfig( + enabled=disc.get("console_price_charts", _cd.enabled), + library=disc.get("price_chart_library", _cd.library), + windows=disc.get("price_chart_windows", _cd.windows), + lookback_days=disc.get("price_chart_lookback_days", _cd.lookback_days), + width=disc.get("price_chart_width", _cd.width), + height=disc.get("price_chart_height", _cd.height), + max_tickers=disc.get("price_chart_max_tickers", _cd.max_tickers), + show_movement_stats=disc.get( + "price_chart_show_movement_stats", _cd.show_movement_stats + ), + ) + + # Logging + logging_cfg = LoggingConfig( + log_tool_calls=disc.get("log_tool_calls", _ld.log_tool_calls), + log_tool_calls_console=disc.get( + "log_tool_calls_console", _ld.log_tool_calls_console + ), + log_prompts_console=disc.get( + "log_prompts_console", _ld.log_prompts_console + ), + tool_log_max_chars=disc.get("tool_log_max_chars", _ld.tool_log_max_chars), + tool_log_exclude=disc.get("tool_log_exclude", _ld.tool_log_exclude), + ) + + return cls( + filters=filters, + enrichment=enrichment, + ranker=ranker, + charts=charts, + logging=logging_cfg, + deep_dive_max_workers=disc.get("deep_dive_max_workers", 1), + discovery_mode=disc.get("discovery_mode", "hybrid"), + ) diff --git a/tradingagents/dataflows/discovery/filter.py b/tradingagents/dataflows/discovery/filter.py index 46c2030f..720e5c90 100644 --- a/tradingagents/dataflows/discovery/filter.py +++ b/tradingagents/dataflows/discovery/filter.py @@ -1,15 +1,21 @@ import json import re -from datetime import timedelta +from datetime import datetime, timedelta from typing import Any, Callable, Dict, List +import pandas as pd + from tradingagents.dataflows.discovery.candidate import Candidate +from tradingagents.dataflows.discovery.discovery_config import DiscoveryConfig from tradingagents.dataflows.discovery.utils import ( PRIORITY_ORDER, Strategy, is_valid_ticker, resolve_trade_date, ) +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) def _parse_market_cap_to_billions(value: Any) -> Any: @@ -107,34 +113,35 @@ class CandidateFilter: self.config = config self.execute_tool = tool_executor - # Discovery Settings - discovery_config = config.get("discovery", {}) + dc = DiscoveryConfig.from_config(config) - # Filter settings (nested under "filters" section, with backward compatibility) - filter_config = discovery_config.get("filters", discovery_config) # Fallback to root for old configs - self.filter_same_day_movers = filter_config.get("filter_same_day_movers", True) - self.intraday_movement_threshold = filter_config.get("intraday_movement_threshold", 10.0) - self.filter_recent_movers = filter_config.get("filter_recent_movers", True) - self.recent_movement_lookback_days = filter_config.get("recent_movement_lookback_days", 7) - self.recent_movement_threshold = filter_config.get("recent_movement_threshold", 10.0) - self.recent_mover_action = filter_config.get("recent_mover_action", "filter") - self.min_average_volume = filter_config.get("min_average_volume", 500_000) - self.volume_lookback_days = filter_config.get("volume_lookback_days", 10) + # Filter settings + self.filter_same_day_movers = dc.filters.filter_same_day_movers + self.intraday_movement_threshold = dc.filters.intraday_movement_threshold + self.filter_recent_movers = dc.filters.filter_recent_movers + self.recent_movement_lookback_days = dc.filters.recent_movement_lookback_days + self.recent_movement_threshold = dc.filters.recent_movement_threshold + self.recent_mover_action = dc.filters.recent_mover_action + self.min_average_volume = dc.filters.min_average_volume + self.volume_lookback_days = dc.filters.volume_lookback_days - # Enrichment settings (nested under "enrichment" section, with backward compatibility) - enrichment_config = discovery_config.get("enrichment", discovery_config) # Fallback to root - self.batch_news_vendor = enrichment_config.get("batch_news_vendor", "openai") - self.batch_news_batch_size = enrichment_config.get("batch_news_batch_size", 50) + # Filter extras (volume/compression detection) + self.volume_cache_key = dc.filters.volume_cache_key + self.min_market_cap = dc.filters.min_market_cap + self.compression_atr_pct_max = dc.filters.compression_atr_pct_max + self.compression_bb_width_max = dc.filters.compression_bb_width_max + self.compression_min_volume_ratio = dc.filters.compression_min_volume_ratio - # Other settings (remain at discovery level) - self.news_lookback_days = discovery_config.get("news_lookback_days", 3) - self.volume_cache_key = discovery_config.get("volume_cache_key", "avg_volume_cache") - self.min_market_cap = discovery_config.get("min_market_cap", 0) - self.compression_atr_pct_max = discovery_config.get("compression_atr_pct_max", 2.0) - self.compression_bb_width_max = discovery_config.get("compression_bb_width_max", 6.0) - self.compression_min_volume_ratio = discovery_config.get("compression_min_volume_ratio", 1.3) - self.context_max_snippets = discovery_config.get("context_max_snippets", 2) - self.context_snippet_max_chars = discovery_config.get("context_snippet_max_chars", 140) + # Enrichment settings + self.batch_news_vendor = dc.enrichment.batch_news_vendor + self.batch_news_batch_size = dc.enrichment.batch_news_batch_size + self.news_lookback_days = dc.enrichment.news_lookback_days + self.context_max_snippets = dc.enrichment.context_max_snippets + self.context_snippet_max_chars = dc.enrichment.context_snippet_max_chars + + # ML predictor (loaded lazily — None if no model file exists) + self._ml_predictor = None + self._ml_predictor_loaded = False def filter(self, state: Dict[str, Any]) -> Dict[str, Any]: """Filter candidates based on strategy and enrich with additional data.""" @@ -150,7 +157,7 @@ class CandidateFilter: start_date = start_date_obj.strftime("%Y-%m-%d") end_date = end_date_obj.strftime("%Y-%m-%d") - print(f"🔍 Filtering and enriching {len(candidates)} candidates...") + logger.info(f"🔍 Filtering and enriching {len(candidates)} candidates...") priority_order = self._priority_order() candidates = self._dedupe_candidates(candidates, priority_order) @@ -178,12 +185,12 @@ class CandidateFilter: # Print consolidated list of failed tickers if failed_tickers: - print(f"\n ⚠️ {len(failed_tickers)} tickers failed data fetch (possibly delisted)") + logger.warning(f"⚠️ {len(failed_tickers)} tickers failed data fetch (possibly delisted)") if len(failed_tickers) <= 10: - print(f" {', '.join(failed_tickers)}") + logger.warning(f"{', '.join(failed_tickers)}") else: - print( - f" {', '.join(failed_tickers[:10])} ... and {len(failed_tickers)-10} more" + logger.warning( + f"{', '.join(failed_tickers[:10])} ... and {len(failed_tickers)-10} more" ) # Export review list delisted_cache.export_review_list() @@ -255,6 +262,16 @@ class CandidateFilter: unique_candidates[ticker] = primary + # Compute confluence scores and boost priority for multi-source candidates + for candidate in unique_candidates.values(): + source_count = len(candidate.all_sources) + candidate.extras["confluence_score"] = source_count + + if source_count >= 3 and candidate.priority != "critical": + candidate.priority = "critical" + elif source_count >= 2 and candidate.priority in ("medium", "low", "unknown"): + candidate.priority = "high" + return [candidate.to_dict() for candidate in unique_candidates.values()] def _sort_by_priority( @@ -268,8 +285,8 @@ class CandidateFilter: high_priority = sum(1 for c in candidates if c.get("priority") == "high") medium_priority = sum(1 for c in candidates if c.get("priority") == "medium") low_priority = sum(1 for c in candidates if c.get("priority") == "low") - print( - f" Priority breakdown: {critical_priority} critical, {high_priority} high, {medium_priority} medium, {low_priority} low" + logger.info( + f"Priority breakdown: {critical_priority} critical, {high_priority} high, {medium_priority} medium, {low_priority} low" ) def _fetch_batch_volume( @@ -299,7 +316,7 @@ class CandidateFilter: if self.batch_news_vendor == "google": from tradingagents.dataflows.openai import get_batch_stock_news_google - print(f" 📰 Batch fetching news (Google) for {len(all_tickers)} tickers...") + logger.info(f"📰 Batch fetching news (Google) for {len(all_tickers)} tickers...") news_by_ticker = self._run_call( "batch fetching news (Google)", get_batch_stock_news_google, @@ -312,7 +329,7 @@ class CandidateFilter: else: # Default to OpenAI from tradingagents.dataflows.openai import get_batch_stock_news_openai - print(f" 📰 Batch fetching news (OpenAI) for {len(all_tickers)} tickers...") + logger.info(f"📰 Batch fetching news (OpenAI) for {len(all_tickers)} tickers...") news_by_ticker = self._run_call( "batch fetching news (OpenAI)", get_batch_stock_news_openai, @@ -322,10 +339,10 @@ class CandidateFilter: end_date=end_date, batch_size=self.batch_news_batch_size, ) - print(f" ✓ Batch news fetched for {len(news_by_ticker)} tickers") + logger.info(f"✓ Batch news fetched for {len(news_by_ticker)} tickers") return news_by_ticker except Exception as e: - print(f" Warning: Batch news fetch failed, will skip news enrichment: {e}") + logger.warning(f"Batch news fetch failed, will skip news enrichment: {e}") return {} def _filter_and_enrich_candidates( @@ -368,8 +385,8 @@ class CandidateFilter: if intraday_check.get("already_moved"): filtered_reasons["intraday_moved"] += 1 intraday_pct = intraday_check.get("intraday_change_pct", 0) - print( - f" Filtered {ticker}: Already moved {intraday_pct:+.1f}% today (stale)" + logger.info( + f"Filtered {ticker}: Already moved {intraday_pct:+.1f}% today (stale)" ) continue @@ -378,7 +395,7 @@ class CandidateFilter: except Exception as e: # Don't filter out if check fails, just log - print(f" Warning: Could not check intraday movement for {ticker}: {e}") + logger.warning(f"Could not check intraday movement for {ticker}: {e}") # Recent multi-day mover filter (avoid stocks that already ran) if self.filter_recent_movers: @@ -397,8 +414,8 @@ class CandidateFilter: if self.recent_mover_action == "filter": filtered_reasons["recent_moved"] += 1 change_pct = reaction.get("price_change_pct", 0) - print( - f" Filtered {ticker}: Already moved {change_pct:+.1f}% in last " + logger.info( + f"Filtered {ticker}: Already moved {change_pct:+.1f}% in last " f"{self.recent_movement_lookback_days} days" ) continue @@ -411,7 +428,7 @@ class CandidateFilter: f"over {self.recent_movement_lookback_days}d" ) except Exception as e: - print(f" Warning: Could not check recent movement for {ticker}: {e}") + logger.warning(f"Could not check recent movement for {ticker}: {e}") # Liquidity filter based on average volume if self.min_average_volume: @@ -482,13 +499,37 @@ class CandidateFilter: cand["business_description"] = ( f"{company_name} - Business description not available." ) + + # Extract short interest from fundamentals (no extra API call) + short_pct_raw = fund.get("ShortPercentOfFloat", fund.get("ShortPercentFloat")) + short_interest_pct = None + if short_pct_raw and short_pct_raw != "N/A": + try: + short_interest_pct = round(float(short_pct_raw) * 100, 2) + except (ValueError, TypeError): + pass + cand["short_interest_pct"] = short_interest_pct + cand["high_short_interest"] = ( + short_interest_pct is not None and short_interest_pct > 15.0 + ) + short_ratio_raw = fund.get("ShortRatio") + if short_ratio_raw and short_ratio_raw != "N/A": + try: + cand["short_ratio"] = float(short_ratio_raw) + except (ValueError, TypeError): + cand["short_ratio"] = None + else: + cand["short_ratio"] = None else: cand["fundamentals"] = {} cand["business_description"] = ( f"{ticker} - Business description not available." ) + cand["short_interest_pct"] = None + cand["high_short_interest"] = False + cand["short_ratio"] = None except Exception as e: - print(f" Warning: Could not fetch fundamentals for {ticker}: {e}") + logger.warning(f"Could not fetch fundamentals for {ticker}: {e}") delisted_cache.mark_failed(ticker, str(e)) failed_tickers.append(ticker) cand["current_price"] = None @@ -630,10 +671,59 @@ class CandidateFilter: else: cand["has_bullish_options_flow"] = False + # Normalize options signal for quantitative scoring + cand["options_signal"] = cand.get("options_flow", {}).get("signal", "neutral") + + # 5. Earnings Estimate Enrichment + from tradingagents.dataflows.finnhub_api import get_ticker_earnings_estimate + + earnings_to = ( + datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=30) + ).strftime("%Y-%m-%d") + earnings_data = self._run_call( + "fetching earnings estimate", + get_ticker_earnings_estimate, + default={}, + ticker=ticker, + from_date=end_date, + to_date=earnings_to, + ) + if earnings_data.get("has_upcoming_earnings"): + cand["has_upcoming_earnings"] = True + cand["days_to_earnings"] = earnings_data.get("days_to_earnings") + cand["eps_estimate"] = earnings_data.get("eps_estimate") + cand["revenue_estimate"] = earnings_data.get("revenue_estimate") + cand["earnings_date"] = earnings_data.get("earnings_date") + else: + cand["has_upcoming_earnings"] = False + + # Extract derived signals for quant scoring + tech_report = cand.get("technical_indicators", "") + rsi_match = re.search( + r"RSI.*?Value[:\s]*(\d+\.?\d*)", tech_report, re.IGNORECASE | re.DOTALL + ) + if rsi_match: + cand["rsi_value"] = float(rsi_match.group(1)) + + insider_text = cand.get("insider_transactions", "") + cand["has_insider_buying"] = ( + isinstance(insider_text, str) and "Purchase" in insider_text + ) + + # Compute quantitative pre-score + cand["quant_score"] = self._compute_quant_score(cand) + + # ML win probability prediction (if model available) + ml_result = self._predict_ml(cand, ticker, end_date) + if ml_result: + cand["ml_win_probability"] = ml_result["win_prob"] + cand["ml_prediction"] = ml_result["prediction"] + cand["ml_loss_probability"] = ml_result["loss_prob"] + filtered_candidates.append(cand) except Exception as e: - print(f" Error checking {ticker}: {e}") + logger.error(f"Error checking {ticker}: {e}") return filtered_candidates, filtered_reasons, failed_tickers, delisted_cache @@ -643,19 +733,116 @@ class CandidateFilter: filtered_candidates: List[Dict[str, Any]], filtered_reasons: Dict[str, int], ) -> None: - print("\n 📊 Filtering Summary:") - print(f" Starting candidates: {len(candidates)}") + logger.info("\n 📊 Filtering Summary:") + logger.info(f" Starting candidates: {len(candidates)}") if filtered_reasons.get("intraday_moved", 0) > 0: - print(f" ❌ Same-day movers: {filtered_reasons['intraday_moved']}") + logger.info(f" ❌ Same-day movers: {filtered_reasons['intraday_moved']}") if filtered_reasons.get("recent_moved", 0) > 0: - print(f" ❌ Recent movers: {filtered_reasons['recent_moved']}") + logger.info(f" ❌ Recent movers: {filtered_reasons['recent_moved']}") if filtered_reasons.get("volume", 0) > 0: - print(f" ❌ Low volume: {filtered_reasons['volume']}") + logger.info(f" ❌ Low volume: {filtered_reasons['volume']}") if filtered_reasons.get("market_cap", 0) > 0: - print(f" ❌ Below market cap: {filtered_reasons['market_cap']}") + logger.info(f" ❌ Below market cap: {filtered_reasons['market_cap']}") if filtered_reasons.get("no_data", 0) > 0: - print(f" ❌ No data available: {filtered_reasons['no_data']}") - print(f" ✅ Passed filters: {len(filtered_candidates)}") + logger.info(f" ❌ No data available: {filtered_reasons['no_data']}") + logger.info(f" ✅ Passed filters: {len(filtered_candidates)}") + + def _predict_ml( + self, cand: Dict[str, Any], ticker: str, end_date: str + ) -> Any: + """Run ML win probability prediction for a candidate.""" + # Lazy-load predictor on first call + if not self._ml_predictor_loaded: + self._ml_predictor_loaded = True + try: + from tradingagents.ml.predictor import MLPredictor + + self._ml_predictor = MLPredictor.load() + if self._ml_predictor: + logger.info("ML predictor loaded — will add win probabilities") + except Exception as e: + logger.debug(f"ML predictor not available: {e}") + + if self._ml_predictor is None: + return None + + try: + from tradingagents.ml.feature_engineering import ( + compute_features_single, + ) + from tradingagents.dataflows.y_finance import download_history + + # Fetch OHLCV for feature computation (needs ~210 rows of history) + ohlcv = download_history( + ticker, + start=pd.Timestamp(end_date) - pd.DateOffset(years=2), + end=end_date, + multi_level_index=False, + progress=False, + auto_adjust=True, + ) + + if ohlcv.empty: + return None + + ohlcv = ohlcv.reset_index() + market_cap = cand.get("market_cap_bil", 0) + market_cap_usd = market_cap * 1e9 if market_cap else None + + features = compute_features_single(ohlcv, end_date, market_cap=market_cap_usd) + if features is None: + return None + + return self._ml_predictor.predict(features) + + except Exception as e: + logger.debug(f"ML prediction failed for {ticker}: {e}") + return None + + def _compute_quant_score(self, cand: Dict[str, Any]) -> int: + """Compute a 0-100 quantitative pre-score from hard data.""" + score = 0 + + # Volume ratio (max +15) + vol_ratio = cand.get("volume_ratio") + if vol_ratio is not None: + if vol_ratio >= 2.0: + score += 15 + elif vol_ratio >= 1.5: + score += 10 + elif vol_ratio >= 1.3: + score += 5 + + # Confluence — per independent source, max 3 (max +30) + confluence = cand.get("confluence_score", 1) + score += min(confluence, 3) * 10 + + # Options flow signal (max +20) + options_signal = cand.get("options_signal", "neutral") + if options_signal == "very_bullish": + score += 20 + elif options_signal == "bullish": + score += 15 + + # Insider buying detected (max +10) + if cand.get("has_insider_buying"): + score += 10 + + # Volatility compression with volume uptick (max +10) + if cand.get("has_volatility_compression"): + score += 10 + + # Healthy RSI momentum: 40-65 range (max +5) + rsi = cand.get("rsi_value") + if rsi is not None and 40 <= rsi <= 65: + score += 5 + + # Short squeeze potential: 5-20% short interest (max +5) + short_pct = cand.get("short_interest_pct") + if short_pct is not None and 5.0 <= short_pct <= 20.0: + score += 5 + + return min(score, 100) def _run_tool( self, @@ -674,7 +861,7 @@ class CandidateFilter: **params, ) except Exception as e: - print(f" Error during {step}: {e}") + logger.error(f"Error during {step}: {e}") return default def _run_call( @@ -687,7 +874,7 @@ class CandidateFilter: try: return func(**kwargs) except Exception as e: - print(f" Error {label}: {e}") + logger.error(f"Error {label}: {e}") return default def _assign_strategy(self, cand: Dict[str, Any]): diff --git a/tradingagents/dataflows/discovery/performance/position_tracker.py b/tradingagents/dataflows/discovery/performance/position_tracker.py index 2fcc4fae..da47509a 100644 --- a/tradingagents/dataflows/discovery/performance/position_tracker.py +++ b/tradingagents/dataflows/discovery/performance/position_tracker.py @@ -6,9 +6,12 @@ Maintains complete price time-series and calculates real-time metrics. """ import json -import os from datetime import datetime from pathlib import Path + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) from typing import Any, Dict, List, Optional @@ -189,6 +192,6 @@ class PositionTracker: open_positions.append(position) except (json.JSONDecodeError, IOError) as e: # Log error but continue loading other positions - print(f"Error loading position from {filepath}: {e}") + logger.error(f"Error loading position from {filepath}: {e}") return open_positions diff --git a/tradingagents/dataflows/discovery/ranker.py b/tradingagents/dataflows/discovery/ranker.py index ae4e0b18..330f7857 100644 --- a/tradingagents/dataflows/discovery/ranker.py +++ b/tradingagents/dataflows/discovery/ranker.py @@ -7,6 +7,7 @@ from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.messages import HumanMessage from pydantic import BaseModel, Field +from tradingagents.dataflows.discovery.discovery_config import DiscoveryConfig from tradingagents.dataflows.discovery.utils import append_llm_log, resolve_llm_name from tradingagents.utils.logger import get_logger @@ -51,7 +52,7 @@ class StockRanking(BaseModel): strategy_match: str = Field(description="Strategy that matched") final_score: int = Field(description="Score 0-100") confidence: int = Field(description="Confidence 1-10") - reason: str = Field(description="Investment thesis") + reason: str = Field(description="Detailed investment thesis (4-6 sentences) defending the trade with specific catalysts, risk/reward, and timing") description: str = Field(description="Company description") @@ -71,15 +72,18 @@ class CandidateRanker: self.llm = llm self.analytics = analytics - discovery_config = config.get("discovery", {}) - self.max_candidates_to_analyze = discovery_config.get("max_candidates_to_analyze", 30) - self.final_recommendations = discovery_config.get("final_recommendations", 3) + dc = DiscoveryConfig.from_config(config) + self.max_candidates_to_analyze = dc.ranker.max_candidates_to_analyze + self.final_recommendations = dc.ranker.final_recommendations # Truncation settings - self.truncate_context = discovery_config.get("truncate_ranking_context", False) - self.max_news_chars = discovery_config.get("max_news_chars", 500) - self.max_insider_chars = discovery_config.get("max_insider_chars", 300) - self.max_recommendations_chars = discovery_config.get("max_recommendations_chars", 300) + self.truncate_context = dc.ranker.truncate_ranking_context + self.max_news_chars = dc.ranker.max_news_chars + self.max_insider_chars = dc.ranker.max_insider_chars + self.max_recommendations_chars = dc.ranker.max_recommendations_chars + + # Prompt logging + self.log_prompts_console = dc.logging.log_prompts_console def rank(self, state: Dict[str, Any]) -> Dict[str, Any]: """Rank all filtered candidates and select the top opportunities.""" @@ -87,7 +91,7 @@ class CandidateRanker: trade_date = state.get("trade_date", datetime.now().strftime("%Y-%m-%d")) if len(candidates) == 0: - print("⚠️ No candidates to rank.") + logger.warning("⚠️ No candidates to rank.") return { "opportunities": [], "final_ranking": "[]", @@ -98,20 +102,20 @@ class CandidateRanker: # Limit candidates to prevent token overflow max_candidates = min(self.max_candidates_to_analyze, 200) if len(candidates) > max_candidates: - print( - f" ⚠️ Too many candidates ({len(candidates)}), limiting to top {max_candidates} by priority" + logger.warning( + f"⚠️ Too many candidates ({len(candidates)}), limiting to top {max_candidates} by priority" ) candidates = candidates[:max_candidates] - print( + logger.info( f"🏆 Ranking {len(candidates)} candidates to select top {self.final_recommendations}..." ) # Load historical performance statistics historical_stats = self.analytics.load_historical_stats() if historical_stats.get("available"): - print( - f" 📊 Loaded historical stats: {historical_stats.get('total_tracked', 0)} tracked recommendations" + logger.info( + f"📊 Loaded historical stats: {historical_stats.get('total_tracked', 0)} tracked recommendations" ) # Build RICH context for each candidate @@ -213,10 +217,41 @@ class CandidateRanker: recommendations_text[: self.max_recommendations_chars] + "..." ) + # New enrichment fields + confluence_score = cand.get("confluence_score", 1) + quant_score = cand.get("quant_score", "N/A") + + # ML prediction + ml_win_prob = cand.get("ml_win_probability") + ml_prediction = cand.get("ml_prediction") + if ml_win_prob is not None: + ml_str = f"{ml_win_prob:.1%} (Predicted: {ml_prediction})" + else: + ml_str = "N/A" + short_interest_pct = cand.get("short_interest_pct") + high_short = cand.get("high_short_interest", False) + short_str = f"{short_interest_pct:.1f}%" if short_interest_pct else "N/A" + if high_short: + short_str += " (HIGH)" + + # Earnings estimate + if cand.get("has_upcoming_earnings"): + days = cand.get("days_to_earnings", "?") + eps_est = cand.get("eps_estimate") + rev_est = cand.get("revenue_estimate") + earnings_date = cand.get("earnings_date", "N/A") + eps_str = f"${eps_est:.2f}" if isinstance(eps_est, (int, float)) else "N/A" + rev_str = f"${rev_est:,.0f}" if isinstance(rev_est, (int, float)) else "N/A" + earnings_section = f"Earnings in {days} days ({earnings_date}): EPS Est {eps_str}, Rev Est {rev_str}" + else: + earnings_section = "No upcoming earnings within 30 days" + summary = f"""### {ticker} (Priority: {priority.upper()}) - **Strategy Match**: {strategy} -- **Sources**: {source_str} +- **Sources**: {source_str} | **Confluence**: {confluence_score} source(s) +- **Quant Pre-Score**: {quant_score}/100 | **ML Win Probability**: {ml_str} - **Price**: {price_str} | **Current Price (numeric)**: {current_price if isinstance(current_price, (int, float)) else "N/A"} | **Intraday**: {intraday_str} | **Avg Volume**: {volume_str} +- **Short Interest**: {short_str} - **Discovery Context**: {context} - **Business**: {business_description} - **News**: {news_summary} @@ -234,6 +269,8 @@ class CandidateRanker: **Options Activity**: {options_activity if options_activity else "N/A"} + +**Upcoming Earnings**: {earnings_section} """ candidate_summaries.append(summary) @@ -256,12 +293,14 @@ CANDIDATES FOR REVIEW: INSTRUCTIONS: 1. Analyze each candidate's "Discovery Context" (why it was found) and "Strategy Match". 2. Cross-reference with Technicals (RSI, etc.) and Fundamentals. -3. Prioritize "LEADING" indicators (Undiscovered DD, Earnings Accumulation, Insider Buying) over lagging ones. -4. Select exactly {self.final_recommendations} winners. -5. Use ONLY the information provided in the candidates section; do NOT invent catalysts, prices, or metrics. -6. If a required field is missing, set it to null (do not guess). -7. Rank only tickers from the candidates list. -8. Reasons must reference at least two concrete facts from the candidate context. +3. Use the Quantitative Pre-Score as an objective baseline. Scores above 50 indicate strong multi-factor alignment. +4. The ML Win Probability is a trained model's estimate that this stock hits +5% within 7 days. Treat scores above 60% as strong ML confirmation. +5. Prioritize "LEADING" indicators (Undiscovered DD, Earnings Accumulation, Insider Buying) over lagging ones. +6. Select exactly {self.final_recommendations} winners. +7. Use ONLY the information provided in the candidates section; do NOT invent catalysts, prices, or metrics. +8. If a required field is missing, set it to null (do not guess). +9. Rank only tickers from the candidates list. +10. Reasons must reference at least two concrete facts from the candidate context. Output a JSON object with a 'rankings' list. Each item should have: - rank: 1 to {self.final_recommendations} @@ -271,17 +310,20 @@ Output a JSON object with a 'rankings' list. Each item should have: - strategy_match: main strategy - final_score: 0-100 score - confidence: 1-10 confidence level -- reason: Detailed investment thesis (2-3 sentences) explaining WHY this will move NOW. +- reason: Detailed investment thesis (4-6 sentences). Defend the trade: (1) what is the catalyst/edge, (2) why NOW and not later, (3) what does the risk/reward look like, (4) what could go wrong. Reference specific data points from the candidate context. - description: Brief company description. JSON FORMAT ONLY. No markdown, no extra text. All numeric fields must be numbers (not strings).""" # Invoke LLM with structured output - print(" 🧠 Deep Thinking Ranker analyzing opportunities...") + logger.info("🧠 Deep Thinking Ranker analyzing opportunities...") logger.info( f"Invoking ranking LLM with {len(candidates)} candidates, prompt length: {len(prompt)} chars" ) - logger.debug(f"Full ranking prompt:\n{prompt}") + if self.log_prompts_console: + logger.info(f"Full ranking prompt:\n{prompt}") + else: + logger.debug(f"Full ranking prompt:\n{prompt}") try: # Use structured output with include_raw for debugging @@ -364,7 +406,7 @@ JSON FORMAT ONLY. No markdown, no extra text. All numeric fields must be numbers final_ranking_list = [ranking.model_dump() for ranking in result.rankings] - print(f" ✅ Selected {len(final_ranking_list)} top recommendations") + logger.info(f"✅ Selected {len(final_ranking_list)} top recommendations") logger.info( f"Successfully ranked {len(final_ranking_list)} opportunities: " f"{[r['ticker'] for r in final_ranking_list]}" @@ -407,7 +449,7 @@ JSON FORMAT ONLY. No markdown, no extra text. All numeric fields must be numbers ) state["tool_logs"] = tool_logs # Structured output validation failed - print(f" ❌ Error: {e}") + logger.error(f"❌ Error: {e}") logger.error(f"Structured output validation error: {e}") return {"final_ranking": [], "opportunities": [], "status": "ranking_failed"} @@ -423,7 +465,7 @@ JSON FORMAT ONLY. No markdown, no extra text. All numeric fields must be numbers error=str(e), ) state["tool_logs"] = tool_logs - print(f" ❌ Error during ranking: {e}") + logger.error(f"❌ Error during ranking: {e}") logger.exception(f"Unexpected error during ranking: {e}") return {"final_ranking": [], "opportunities": [], "status": "error"} diff --git a/tradingagents/dataflows/discovery/scanner_registry.py b/tradingagents/dataflows/discovery/scanner_registry.py index a1caf707..e96982e4 100644 --- a/tradingagents/dataflows/discovery/scanner_registry.py +++ b/tradingagents/dataflows/discovery/scanner_registry.py @@ -1,8 +1,9 @@ from abc import ABC, abstractmethod from typing import Any, Dict, List, Type -import logging -logger = logging.getLogger(__name__) +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class BaseScanner(ABC): @@ -43,9 +44,7 @@ class BaseScanner(ABC): candidates = self.scan(state) if not isinstance(candidates, list): - logger.error( - f"{self.name}: scan() returned {type(candidates)}, expected list" - ) + logger.error(f"{self.name}: scan() returned {type(candidates)}, expected list") return [] # Validate each candidate @@ -58,7 +57,7 @@ class BaseScanner(ABC): else: logger.warning( f"{self.name}: Invalid candidate #{i}: {candidate}", - extra={"scanner": self.name, "pipeline": self.pipeline} + extra={"scanner": self.name, "pipeline": self.pipeline}, ) if len(valid_candidates) < len(candidates): @@ -76,8 +75,8 @@ class BaseScanner(ABC): extra={ "scanner": self.name, "pipeline": self.pipeline, - "error_type": type(e).__name__ - } + "error_type": type(e).__name__, + }, ) return [] @@ -101,12 +100,12 @@ class ScannerRegistry: # Check for duplicate registration if scanner_class.name in self.scanners: - logger.warning( - f"Scanner '{scanner_class.name}' already registered, overwriting" - ) + logger.warning(f"Scanner '{scanner_class.name}' already registered, overwriting") self.scanners[scanner_class.name] = scanner_class - logger.info(f"Registered scanner: {scanner_class.name} (pipeline: {scanner_class.pipeline})") + logger.info( + f"Registered scanner: {scanner_class.name} (pipeline: {scanner_class.pipeline})" + ) def get_scanners_by_pipeline(self, pipeline: str) -> List[Type[BaseScanner]]: return [sc for sc in self.scanners.values() if sc.pipeline == pipeline] diff --git a/tradingagents/dataflows/discovery/scanners.py b/tradingagents/dataflows/discovery/scanners.py index 0b46178f..6b2a8a31 100644 --- a/tradingagents/dataflows/discovery/scanners.py +++ b/tradingagents/dataflows/discovery/scanners.py @@ -12,6 +12,9 @@ from tradingagents.dataflows.discovery.utils import ( resolve_trade_date_str, ) from tradingagents.schemas import RedditTickerList +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) @dataclass @@ -129,7 +132,7 @@ class TraditionalScanner: try: return spec.handler(state) except Exception as e: - print(f" Error running scanner '{spec.name}': {e}") + logger.error(f"Error running scanner '{spec.name}': {e}") return [] def _run_tool( @@ -149,7 +152,7 @@ class TraditionalScanner: **params, ) except Exception as e: - print(f" Error during {step}: {e}") + logger.error(f"Error during {step}: {e}") return default def _run_call( @@ -162,7 +165,7 @@ class TraditionalScanner: try: return func(**kwargs) except Exception as e: - print(f" Error {label}: {e}") + logger.error(f"Error {label}: {e}") return default def _scan_reddit(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: @@ -183,7 +186,7 @@ class TraditionalScanner: try: from tradingagents.dataflows.reddit_api import get_reddit_undiscovered_dd - print(" 🔍 Scanning Reddit for undiscovered DD...") + logger.info("🔍 Scanning Reddit for undiscovered DD...") # Note: get_reddit_undiscovered_dd is not a tool in strict sense but a direct function call # that uses an LLM. We call it directly here as in original code. reddit_dd_report = self._run_call( @@ -195,7 +198,7 @@ class TraditionalScanner: llm_evaluator=self.llm, # Use fast LLM for evaluation ) except Exception as e: - print(f" Error fetching undiscovered DD: {e}") + logger.error(f"Error fetching undiscovered DD: {e}") # BATCHED LLM CALL: Extract tickers from both Reddit sources in ONE call # Uses proper Pydantic structured output for clean, validated results @@ -220,7 +223,9 @@ IMPORTANT RULES: {reddit_dd_report} """ - combined_prompt += """Extract ALL mentioned stock tickers with their source and context.""" + combined_prompt += ( + """Extract ALL mentioned stock tickers with their source and context.""" + ) # Use proper Pydantic structured output (not raw JSON schema) structured_llm = self.llm.with_structured_output(RedditTickerList) @@ -276,8 +281,8 @@ IMPORTANT RULES: ) trending_count += 1 - print( - f" Found {trending_count} trending + {dd_count} DD tickers from Reddit " + logger.info( + f"Found {trending_count} trending + {dd_count} DD tickers from Reddit " f"(skipped {skipped_low_confidence} low-confidence)" ) except Exception as e: @@ -292,7 +297,7 @@ IMPORTANT RULES: error=str(e), ) state["tool_logs"] = tool_logs - print(f" Error extracting Reddit tickers: {e}") + logger.error(f"Error extracting Reddit tickers: {e}") return candidates @@ -301,7 +306,7 @@ IMPORTANT RULES: candidates: List[Dict[str, Any]] = [] from tradingagents.dataflows.alpha_vantage_stock import get_top_gainers_losers - print(" 📊 Fetching market movers (direct parsing)...") + logger.info("📊 Fetching market movers (direct parsing)...") movers_data = self._run_call( "fetching market movers", get_top_gainers_losers, @@ -343,9 +348,9 @@ IMPORTANT RULES: ) movers_count += 1 - print(f" Found {movers_count} market movers (direct)") + logger.info(f"Found {movers_count} market movers (direct)") else: - print(" Market movers returned error or empty") + logger.warning("Market movers returned error or empty") return candidates @@ -361,7 +366,7 @@ IMPORTANT RULES: from_date = today.strftime("%Y-%m-%d") to_date = (today + timedelta(days=self.max_days_until_earnings)).strftime("%Y-%m-%d") - print(f" 📅 Fetching earnings calendar (next {self.max_days_until_earnings} days)...") + logger.info(f"📅 Fetching earnings calendar (next {self.max_days_until_earnings} days)...") earnings_data = self._run_call( "fetching earnings calendar", get_earnings_calendar, @@ -465,8 +470,8 @@ IMPORTANT RULES: } ) - print( - f" Found {len(earnings_candidates)} earnings candidates (filtered from {len(earnings_data)} total, cap: {self.max_earnings_candidates})" + logger.info( + f"Found {len(earnings_candidates)} earnings candidates (filtered from {len(earnings_data)} total, cap: {self.max_earnings_candidates})" ) return candidates @@ -474,7 +479,7 @@ IMPORTANT RULES: def _scan_ipo(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: """Fetch IPO calendar.""" candidates: List[Dict[str, Any]] = [] - from datetime import datetime, timedelta + from datetime import timedelta from tradingagents.dataflows.finnhub_api import get_ipo_calendar @@ -482,7 +487,7 @@ IMPORTANT RULES: from_date = (today - timedelta(days=7)).strftime("%Y-%m-%d") to_date = (today + timedelta(days=14)).strftime("%Y-%m-%d") - print(" 🆕 Fetching IPO calendar (direct parsing)...") + logger.info("🆕 Fetching IPO calendar (direct parsing)...") ipo_data = self._run_call( "fetching IPO calendar", get_ipo_calendar, @@ -515,7 +520,7 @@ IMPORTANT RULES: ) ipo_count += 1 - print(f" Found {ipo_count} IPO candidates (direct)") + logger.info(f"Found {ipo_count} IPO candidates (direct)") return candidates @@ -524,7 +529,7 @@ IMPORTANT RULES: candidates: List[Dict[str, Any]] = [] from tradingagents.dataflows.finviz_scraper import get_short_interest - print(" 🩳 Fetching short interest (direct parsing)...") + logger.info("🩳 Fetching short interest (direct parsing)...") short_data = self._run_call( "fetching short interest", get_short_interest, @@ -554,7 +559,7 @@ IMPORTANT RULES: ) short_count += 1 - print(f" Found {short_count} short squeeze candidates (direct)") + logger.info(f"Found {short_count} short squeeze candidates (direct)") return candidates @@ -565,7 +570,7 @@ IMPORTANT RULES: today = resolve_trade_date_str(state) - print(" 📈 Fetching unusual volume (direct parsing)...") + logger.info("📈 Fetching unusual volume (direct parsing)...") volume_data = self._run_call( "fetching unusual volume", get_unusual_volume, @@ -593,7 +598,9 @@ IMPORTANT RULES: # Build context with direction info direction_emoji = "🟢" if direction == "bullish" else "⚪" context = f"Volume: {vol_ratio}x avg, Price: {price_change:+.1f}%, " - context += f"Intraday: {intraday_change:+.1f}% {direction_emoji}, Signal: {signal}" + context += ( + f"Intraday: {intraday_change:+.1f}% {direction_emoji}, Signal: {signal}" + ) # Strong accumulation gets highest priority priority = "critical" if signal == "strong_accumulation" else "high" @@ -608,7 +615,9 @@ IMPORTANT RULES: ) volume_count += 1 - print(f" Found {volume_count} unusual volume candidates (direct, distribution filtered)") + logger.info( + f"Found {volume_count} unusual volume candidates (direct, distribution filtered)" + ) return candidates @@ -618,7 +627,7 @@ IMPORTANT RULES: from tradingagents.dataflows.alpha_vantage_analysts import get_analyst_rating_changes from tradingagents.dataflows.y_finance import check_if_price_reacted - print(" 📊 Fetching analyst rating changes (direct parsing)...") + logger.info("📊 Fetching analyst rating changes (direct parsing)...") analyst_data = self._run_call( "fetching analyst rating changes", get_analyst_rating_changes, @@ -639,9 +648,7 @@ IMPORTANT RULES: hours_old = entry.get("hours_old") or 0 freshness = ( - "🔥 FRESH" - if hours_old < 24 - else "🟢 Recent" if hours_old < 72 else "Older" + "🔥 FRESH" if hours_old < 24 else "🟢 Recent" if hours_old < 72 else "Older" ) context = f"{action.upper()} from {source} ({freshness}, {hours_old}h ago)" @@ -651,12 +658,12 @@ IMPORTANT RULES: ticker, lookback_days=3, reaction_threshold=10.0 ) if reaction["status"] == "leading": - context += ( - f" | 💎 EARLY: Price {reaction['price_change_pct']:+.1f}%" - ) + context += f" | 💎 EARLY: Price {reaction['price_change_pct']:+.1f}%" priority = "high" elif reaction["status"] == "lagging": - context += f" | ⚠️ LATE: Already moved {reaction['price_change_pct']:+.1f}%" + context += ( + f" | ⚠️ LATE: Already moved {reaction['price_change_pct']:+.1f}%" + ) priority = "low" else: priority = "medium" @@ -673,7 +680,7 @@ IMPORTANT RULES: ) analyst_count += 1 - print(f" Found {analyst_count} analyst upgrade candidates (direct)") + logger.info(f"Found {analyst_count} analyst upgrade candidates (direct)") return candidates @@ -682,7 +689,7 @@ IMPORTANT RULES: candidates: List[Dict[str, Any]] = [] from tradingagents.dataflows.finviz_scraper import get_insider_buying_screener - print(" 💰 Fetching insider buying (direct parsing)...") + logger.info("💰 Fetching insider buying (direct parsing)...") insider_data = self._run_call( "fetching insider buying", get_insider_buying_screener, @@ -718,7 +725,7 @@ IMPORTANT RULES: ) insider_count += 1 - print(f" Found {insider_count} insider buying candidates (direct)") + logger.info(f"Found {insider_count} insider buying candidates (direct)") return candidates @@ -749,10 +756,10 @@ IMPORTANT RULES: ] removed = before_count - len(candidates) if removed: - print(f" Removed {removed} invalid tickers after batch validation.") + logger.info(f"Removed {removed} invalid tickers after batch validation.") else: - print(" Batch validation returned no valid tickers; skipping filter.") + logger.warning("Batch validation returned no valid tickers; skipping filter.") except Exception as e: - print(f" Error during batch validation: {e}") + logger.error(f"Error during batch validation: {e}") return candidates diff --git a/tradingagents/dataflows/discovery/scanners/__init__.py b/tradingagents/dataflows/discovery/scanners/__init__.py index bd9152f8..556ac8ce 100644 --- a/tradingagents/dataflows/discovery/scanners/__init__.py +++ b/tradingagents/dataflows/discovery/scanners/__init__.py @@ -1,11 +1,14 @@ """Discovery scanners for modular pipeline architecture.""" # Import all scanners to trigger registration -from . import insider_buying # noqa: F401 -from . import options_flow # noqa: F401 -from . import reddit_trending # noqa: F401 -from . import market_movers # noqa: F401 -from . import volume_accumulation # noqa: F401 -from . import semantic_news # noqa: F401 -from . import reddit_dd # noqa: F401 -from . import earnings_calendar # noqa: F401 +from . import ( + earnings_calendar, # noqa: F401 + insider_buying, # noqa: F401 + market_movers, # noqa: F401 + options_flow, # noqa: F401 + reddit_dd, # noqa: F401 + reddit_trending, # noqa: F401 + semantic_news, # noqa: F401 + volume_accumulation, # noqa: F401 + ml_signal, # noqa: F401 +) diff --git a/tradingagents/dataflows/discovery/scanners/earnings_calendar.py b/tradingagents/dataflows/discovery/scanners/earnings_calendar.py index f7706f56..84e87426 100644 --- a/tradingagents/dataflows/discovery/scanners/earnings_calendar.py +++ b/tradingagents/dataflows/discovery/scanners/earnings_calendar.py @@ -1,10 +1,14 @@ """Earnings calendar scanner for upcoming earnings events.""" -from typing import Any, Dict, List -from datetime import datetime, timedelta -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from datetime import datetime, timedelta +from typing import Any, Dict, List + +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority from tradingagents.tools.executor import execute_tool +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class EarningsCalendarScanner(BaseScanner): @@ -23,17 +27,19 @@ class EarningsCalendarScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 📅 Scanning earnings calendar (next {self.max_days_until_earnings} days)...") + logger.info(f"📅 Scanning earnings calendar (next {self.max_days_until_earnings} days)...") try: # Get earnings calendar from Finnhub or Alpha Vantage from_date = datetime.now().strftime("%Y-%m-%d") - to_date = (datetime.now() + timedelta(days=self.max_days_until_earnings)).strftime("%Y-%m-%d") + to_date = (datetime.now() + timedelta(days=self.max_days_until_earnings)).strftime( + "%Y-%m-%d" + ) result = execute_tool("get_earnings_calendar", from_date=from_date, to_date=to_date) if not result: - print(f" Found 0 earnings events") + logger.info("Found 0 earnings events") return [] candidates = [] @@ -55,21 +61,23 @@ class EarningsCalendarScanner(BaseScanner): candidates.sort(key=lambda x: x.get("days_until", 999)) # Apply limit - candidates = candidates[:self.limit] + candidates = candidates[: self.limit] - print(f" Found {len(candidates)} upcoming earnings") + logger.info(f"Found {len(candidates)} upcoming earnings") return candidates except Exception as e: - print(f" ⚠️ Earnings calendar failed: {e}") + logger.warning(f"⚠️ Earnings calendar failed: {e}") return [] - def _parse_structured_earnings(self, earnings_list: List[Dict], seen_tickers: set) -> List[Dict[str, Any]]: + def _parse_structured_earnings( + self, earnings_list: List[Dict], seen_tickers: set + ) -> List[Dict[str, Any]]: """Parse structured earnings data.""" candidates = [] today = datetime.now().date() - for event in earnings_list[:self.max_candidates * 2]: + for event in earnings_list[: self.max_candidates * 2]: ticker = event.get("ticker", event.get("symbol", "")).upper() if not ticker or ticker in seen_tickers: continue @@ -82,7 +90,9 @@ class EarningsCalendarScanner(BaseScanner): try: # Parse date (handle different formats) if isinstance(earnings_date_str, str): - earnings_date = datetime.strptime(earnings_date_str.split()[0], "%Y-%m-%d").date() + earnings_date = datetime.strptime( + earnings_date_str.split()[0], "%Y-%m-%d" + ).date() else: earnings_date = earnings_date_str @@ -107,15 +117,19 @@ class EarningsCalendarScanner(BaseScanner): else: priority = Priority.LOW.value - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Earnings in {days_until} day(s) on {earnings_date_str}", - "priority": priority, - "strategy": "pre_earnings_accumulation" if days_until > 1 else "earnings_play", - "days_until": days_until, - "earnings_date": earnings_date_str, - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Earnings in {days_until} day(s) on {earnings_date_str}", + "priority": priority, + "strategy": ( + "pre_earnings_accumulation" if days_until > 1 else "earnings_play" + ), + "days_until": days_until, + "earnings_date": earnings_date_str, + } + ) if len(candidates) >= self.max_candidates: break @@ -133,12 +147,12 @@ class EarningsCalendarScanner(BaseScanner): today = datetime.now().date() # Split by date sections (### 2026-02-05) - date_sections = re.split(r'###\s+(\d{4}-\d{2}-\d{2})', text) + date_sections = re.split(r"###\s+(\d{4}-\d{2}-\d{2})", text) current_date = None for i, section in enumerate(date_sections): # Check if this is a date line - if re.match(r'\d{4}-\d{2}-\d{2}', section): + if re.match(r"\d{4}-\d{2}-\d{2}", section): current_date = section continue @@ -146,7 +160,7 @@ class EarningsCalendarScanner(BaseScanner): continue # Find tickers in this section (format: **TICKER** (timing)) - ticker_pattern = r'\*\*([A-Z]{2,5})\*\*\s*\(([^\)]+)\)' + ticker_pattern = r"\*\*([A-Z]{2,5})\*\*\s*\(([^\)]+)\)" ticker_matches = re.findall(ticker_pattern, section) for ticker, timing in ticker_matches: @@ -174,20 +188,24 @@ class EarningsCalendarScanner(BaseScanner): if timing == "bmo": # Before market open strategy = "earnings_play" elif timing == "amc": # After market close - strategy = "pre_earnings_accumulation" if days_until > 0 else "earnings_play" + strategy = ( + "pre_earnings_accumulation" if days_until > 0 else "earnings_play" + ) else: strategy = "pre_earnings_accumulation" - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Earnings {timing} in {days_until} day(s) on {current_date}", - "priority": priority, - "strategy": strategy, - "days_until": days_until, - "earnings_date": current_date, - "timing": timing, - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Earnings {timing} in {days_until} day(s) on {current_date}", + "priority": priority, + "strategy": strategy, + "days_until": days_until, + "earnings_date": current_date, + "timing": timing, + } + ) if len(candidates) >= self.max_candidates: return candidates diff --git a/tradingagents/dataflows/discovery/scanners/insider_buying.py b/tradingagents/dataflows/discovery/scanners/insider_buying.py index 24506537..000bbb82 100644 --- a/tradingagents/dataflows/discovery/scanners/insider_buying.py +++ b/tradingagents/dataflows/discovery/scanners/insider_buying.py @@ -1,10 +1,12 @@ """SEC Form 4 insider buying scanner.""" -import re -from datetime import datetime, timedelta + from typing import Any, Dict, List -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class InsiderBuyingScanner(BaseScanner): @@ -22,7 +24,7 @@ class InsiderBuyingScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 💼 Scanning insider buying (last {self.lookback_days} days)...") + logger.info(f"💼 Scanning insider buying (last {self.lookback_days} days)...") try: # Use Finviz insider buying screener @@ -32,11 +34,11 @@ class InsiderBuyingScanner(BaseScanner): transaction_type="buy", lookback_days=self.lookback_days, min_value=self.min_transaction_value, - top_n=self.limit + top_n=self.limit, ) if not result or not isinstance(result, str): - print(f" Found 0 insider purchases") + logger.info("Found 0 insider purchases") return [] # Parse the markdown result @@ -45,12 +47,13 @@ class InsiderBuyingScanner(BaseScanner): # Extract tickers from markdown table import re - lines = result.split('\n') + + lines = result.split("\n") for line in lines: - if '|' not in line or 'Ticker' in line or '---' in line: + if "|" not in line or "Ticker" in line or "---" in line: continue - parts = [p.strip() for p in line.split('|')] + parts = [p.strip() for p in line.split("|")] if len(parts) < 3: continue @@ -61,29 +64,30 @@ class InsiderBuyingScanner(BaseScanner): continue # Validate ticker format - if not re.match(r'^[A-Z]{1,5}$', ticker): + if not re.match(r"^[A-Z]{1,5}$", ticker): continue seen_tickers.add(ticker) - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Insider purchase detected (Finviz)", - "priority": Priority.HIGH.value, - "strategy": "insider_buying", - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": "Insider purchase detected (Finviz)", + "priority": Priority.HIGH.value, + "strategy": "insider_buying", + } + ) if len(candidates) >= self.limit: break - print(f" Found {len(candidates)} insider purchases") + logger.info(f"Found {len(candidates)} insider purchases") return candidates except Exception as e: - print(f" ⚠️ Insider buying failed: {e}") + logger.warning(f"⚠️ Insider buying failed: {e}") return [] - SCANNER_REGISTRY.register(InsiderBuyingScanner) diff --git a/tradingagents/dataflows/discovery/scanners/market_movers.py b/tradingagents/dataflows/discovery/scanners/market_movers.py index d5903c34..2573b4ac 100644 --- a/tradingagents/dataflows/discovery/scanners/market_movers.py +++ b/tradingagents/dataflows/discovery/scanners/market_movers.py @@ -1,8 +1,12 @@ """Market movers scanner - migrated from legacy TraditionalScanner.""" + from typing import Any, Dict, List -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class MarketMoversScanner(BaseScanner): @@ -18,58 +22,59 @@ class MarketMoversScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 📈 Scanning market movers...") + logger.info("📈 Scanning market movers...") from tradingagents.tools.executor import execute_tool try: - result = execute_tool( - "get_market_movers", - return_structured=True - ) + result = execute_tool("get_market_movers", return_structured=True) if not result or not isinstance(result, dict): return [] if "error" in result: - print(f" ⚠️ API error: {result['error']}") + logger.warning(f"⚠️ API error: {result['error']}") return [] candidates = [] # Process gainers - for gainer in result.get("gainers", [])[:self.limit // 2]: + for gainer in result.get("gainers", [])[: self.limit // 2]: ticker = gainer.get("ticker", "").upper() if not ticker: continue - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Top gainer: {gainer.get('change_percentage', 0)} change", - "priority": Priority.MEDIUM.value, - "strategy": "momentum", - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Top gainer: {gainer.get('change_percentage', 0)} change", + "priority": Priority.MEDIUM.value, + "strategy": "momentum", + } + ) # Process losers (potential reversal plays) - for loser in result.get("losers", [])[:self.limit // 2]: + for loser in result.get("losers", [])[: self.limit // 2]: ticker = loser.get("ticker", "").upper() if not ticker: continue - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Top loser: {loser.get('change_percentage', 0)} change (reversal play)", - "priority": Priority.LOW.value, - "strategy": "oversold_reversal", - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Top loser: {loser.get('change_percentage', 0)} change (reversal play)", + "priority": Priority.LOW.value, + "strategy": "oversold_reversal", + } + ) - print(f" Found {len(candidates)} market movers") + logger.info(f"Found {len(candidates)} market movers") return candidates except Exception as e: - print(f" ⚠️ Market movers failed: {e}") + logger.warning(f"⚠️ Market movers failed: {e}") return [] diff --git a/tradingagents/dataflows/discovery/scanners/ml_signal.py b/tradingagents/dataflows/discovery/scanners/ml_signal.py new file mode 100644 index 00000000..b0744e3a --- /dev/null +++ b/tradingagents/dataflows/discovery/scanners/ml_signal.py @@ -0,0 +1,295 @@ +"""ML signal scanner — surfaces high P(WIN) setups from a ticker universe. + +Universe is loaded from a text file (one ticker per line, # comments allowed). +Default: data/tickers.txt. Override via config: discovery.scanners.ml_signal.ticker_file +""" + +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Any, Dict, List, Optional + +import numpy as np +import pandas as pd + +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner +from tradingagents.dataflows.discovery.utils import Priority +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + +# Default ticker file path (relative to project root) +DEFAULT_TICKER_FILE = "data/tickers.txt" + + +def _load_tickers_from_file(path: str) -> List[str]: + """Load ticker symbols from a text file (one per line, # comments allowed).""" + try: + with open(path) as f: + tickers = [ + line.strip().upper() + for line in f + if line.strip() and not line.strip().startswith("#") + ] + if tickers: + logger.info(f"ML scanner: loaded {len(tickers)} tickers from {path}") + return tickers + except FileNotFoundError: + logger.warning(f"Ticker file not found: {path}") + except Exception as e: + logger.warning(f"Failed to load ticker file {path}: {e}") + return [] + + +class MLSignalScanner(BaseScanner): + """Scan a ticker universe for high ML win-probability setups. + + Loads the trained LightGBM/TabPFN model, fetches recent OHLCV data + for a universe of tickers, computes technical features, and returns + candidates whose predicted P(WIN) exceeds a configurable threshold. + + Optimized for large universes (500+ tickers): + - Single batch yfinance download (1 HTTP request) + - Parallel feature computation via ThreadPoolExecutor + - Market cap skipped by default (1 NaN feature out of 30) + """ + + name = "ml_signal" + pipeline = "momentum" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.min_win_prob = self.scanner_config.get("min_win_prob", 0.35) + self.lookback_period = self.scanner_config.get("lookback_period", "1y") + self.max_workers = self.scanner_config.get("max_workers", 8) + self.fetch_market_cap = self.scanner_config.get("fetch_market_cap", False) + + # Load universe: config list > config file > default tickers file + if "ticker_universe" in self.scanner_config: + self.universe = self.scanner_config["ticker_universe"] + else: + ticker_file = self.scanner_config.get( + "ticker_file", + config.get("tickers_file", DEFAULT_TICKER_FILE), + ) + self.universe = _load_tickers_from_file(ticker_file) + if not self.universe: + logger.warning(f"No tickers loaded from {ticker_file} — scanner will be empty") + + def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: + if not self.is_enabled(): + return [] + + logger.info( + f"Running ML signal scanner on {len(self.universe)} tickers " + f"(min P(WIN) = {self.min_win_prob:.0%})..." + ) + + # 1. Load ML model + predictor = self._load_predictor() + if predictor is None: + logger.warning("No ML model available — skipping ml_signal scanner") + return [] + + # 2. Batch-fetch OHLCV data (single HTTP request) + ohlcv_by_ticker = self._fetch_universe_ohlcv() + if not ohlcv_by_ticker: + logger.warning("No OHLCV data fetched — skipping ml_signal scanner") + return [] + + # 3. Compute features and predict in parallel + candidates = self._predict_universe(predictor, ohlcv_by_ticker) + + # 4. Sort by P(WIN) descending and apply limit + candidates.sort(key=lambda c: c.get("ml_win_prob", 0), reverse=True) + candidates = candidates[: self.limit] + + logger.info( + f"ML signal scanner: {len(candidates)} candidates above " + f"{self.min_win_prob:.0%} threshold (from {len(ohlcv_by_ticker)} tickers)" + ) + + # Log individual candidate results + if candidates: + header = f"{'Ticker':<8} {'P(WIN)':>8} {'P(LOSS)':>9} {'Prediction':>12} {'Priority':>10}" + separator = "-" * len(header) + lines = ["\n ML Signal Scanner Results:", f" {header}", f" {separator}"] + for c in candidates: + lines.append( + f" {c['ticker']:<8} {c.get('ml_win_prob', 0):>7.1%} " + f"{c.get('ml_loss_prob', 0):>9.1%} " + f"{c.get('ml_prediction', 'N/A'):>12} " + f"{c.get('priority', 'N/A'):>10}" + ) + lines.append(f" {separator}") + logger.info("\n".join(lines)) + + return candidates + + def _load_predictor(self): + """Load the trained ML model.""" + try: + from tradingagents.ml.predictor import MLPredictor + + return MLPredictor.load() + except Exception as e: + logger.warning(f"Failed to load ML predictor: {e}") + return None + + def _fetch_universe_ohlcv(self) -> Dict[str, pd.DataFrame]: + """Batch-fetch OHLCV data for the entire ticker universe. + + Uses yfinance batch download — a single HTTP request regardless of + universe size. This is the key optimization for large universes. + """ + try: + from tradingagents.dataflows.y_finance import download_history + + logger.info(f"Batch-downloading {len(self.universe)} tickers ({self.lookback_period})...") + + # yfinance batch download — single HTTP request for all tickers + raw = download_history( + " ".join(self.universe), + period=self.lookback_period, + auto_adjust=True, + progress=False, + ) + + if raw.empty: + return {} + + # Handle multi-level columns from batch download + result = {} + if isinstance(raw.columns, pd.MultiIndex): + # Multi-ticker: columns are (Price, Ticker) + tickers_in_data = raw.columns.get_level_values(1).unique() + for ticker in tickers_in_data: + try: + ticker_df = raw.xs(ticker, level=1, axis=1).copy() + ticker_df = ticker_df.reset_index() + if len(ticker_df) > 0: + result[ticker] = ticker_df + except (KeyError, ValueError): + continue + else: + # Single ticker fallback + raw = raw.reset_index() + if len(self.universe) == 1: + result[self.universe[0]] = raw + + logger.info(f"Fetched OHLCV for {len(result)} tickers") + return result + + except Exception as e: + logger.warning(f"OHLCV batch fetch failed: {e}") + return {} + + def _predict_universe( + self, predictor, ohlcv_by_ticker: Dict[str, pd.DataFrame] + ) -> List[Dict[str, Any]]: + """Predict P(WIN) for all tickers using parallel feature computation.""" + candidates = [] + + if self.max_workers <= 1 or len(ohlcv_by_ticker) <= 10: + # Serial execution for small universes + for ticker, ohlcv in ohlcv_by_ticker.items(): + result = self._predict_ticker(predictor, ticker, ohlcv) + if result is not None: + candidates.append(result) + else: + # Parallel feature computation for large universes + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + futures = { + executor.submit(self._predict_ticker, predictor, ticker, ohlcv): ticker + for ticker, ohlcv in ohlcv_by_ticker.items() + } + for future in as_completed(futures): + try: + result = future.result(timeout=10) + if result is not None: + candidates.append(result) + except Exception as e: + ticker = futures[future] + logger.debug(f"{ticker}: prediction timed out or failed — {e}") + + return candidates + + def _predict_ticker( + self, predictor, ticker: str, ohlcv: pd.DataFrame + ) -> Optional[Dict[str, Any]]: + """Compute features and predict P(WIN) for a single ticker.""" + try: + from tradingagents.ml.feature_engineering import ( + MIN_HISTORY_ROWS, + compute_features_single, + ) + + if len(ohlcv) < MIN_HISTORY_ROWS: + return None + + # Market cap: skip by default for speed (1 NaN out of 30 features) + market_cap = self._get_market_cap(ticker) if self.fetch_market_cap else None + + # Compute features for the most recent date + latest_date = pd.to_datetime(ohlcv["Date"]).max().strftime("%Y-%m-%d") + features = compute_features_single(ohlcv, latest_date, market_cap=market_cap) + if features is None: + return None + + # Run ML prediction + prediction = predictor.predict(features) + if prediction is None: + return None + + win_prob = prediction.get("win_prob", 0) + loss_prob = prediction.get("loss_prob", 0) + + if win_prob < self.min_win_prob: + return None + + # Determine priority from P(WIN) + if win_prob >= 0.50: + priority = Priority.CRITICAL.value + elif win_prob >= 0.40: + priority = Priority.HIGH.value + else: + priority = Priority.MEDIUM.value + + return { + "ticker": ticker, + "source": self.name, + "context": ( + f"ML model: {win_prob:.0%} win probability, " + f"{loss_prob:.0%} loss probability " + f"({prediction.get('prediction', 'N/A')})" + ), + "priority": priority, + "strategy": "ml_signal", + "ml_win_prob": win_prob, + "ml_loss_prob": loss_prob, + "ml_prediction": prediction.get("prediction", "N/A"), + } + + except Exception as e: + logger.debug(f"{ticker}: ML prediction failed — {e}") + return None + + def _get_market_cap(self, ticker: str) -> Optional[float]: + """Get market cap (best-effort, cached in memory for the scan).""" + if not hasattr(self, "_market_cap_cache"): + self._market_cap_cache: Dict[str, Optional[float]] = {} + + if ticker in self._market_cap_cache: + return self._market_cap_cache[ticker] + + try: + from tradingagents.dataflows.y_finance import get_ticker_info + + info = get_ticker_info(ticker) + cap = info.get("marketCap") + self._market_cap_cache[ticker] = cap + return cap + except Exception: + self._market_cap_cache[ticker] = None + return None + + +SCANNER_REGISTRY.register(MLSignalScanner) diff --git a/tradingagents/dataflows/discovery/scanners/options_flow.py b/tradingagents/dataflows/discovery/scanners/options_flow.py index a3176409..5ff3312d 100644 --- a/tradingagents/dataflows/discovery/scanners/options_flow.py +++ b/tradingagents/dataflows/discovery/scanners/options_flow.py @@ -1,8 +1,12 @@ """Unusual options activity scanner.""" -from typing import Any, Dict, List -import yfinance as yf -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from typing import Any, Dict, List + +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner +from tradingagents.dataflows.y_finance import get_option_chain, get_ticker_options +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class OptionsFlowScanner(BaseScanner): @@ -16,15 +20,15 @@ class OptionsFlowScanner(BaseScanner): self.min_volume_oi_ratio = self.scanner_config.get("unusual_volume_multiple", 2.0) self.min_volume = self.scanner_config.get("min_volume", 1000) self.min_premium = self.scanner_config.get("min_premium", 25000) - self.ticker_universe = self.scanner_config.get("ticker_universe", [ - "AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "AMD", "TSLA" - ]) + self.ticker_universe = self.scanner_config.get( + "ticker_universe", ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "AMD", "TSLA"] + ) def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: if not self.is_enabled(): return [] - print(f" Scanning unusual options activity...") + logger.info("Scanning unusual options activity...") candidates = [] @@ -38,17 +42,16 @@ class OptionsFlowScanner(BaseScanner): except Exception: continue - print(f" Found {len(candidates)} unusual options flows") + logger.info(f"Found {len(candidates)} unusual options flows") return candidates def _analyze_ticker_options(self, ticker: str) -> Dict[str, Any]: try: - stock = yf.Ticker(ticker) - expirations = stock.options + expirations = get_ticker_options(ticker) if not expirations: return None - options = stock.option_chain(expirations[0]) + options = get_option_chain(ticker, expirations[0]) calls = options.calls puts = options.puts @@ -58,12 +61,9 @@ class OptionsFlowScanner(BaseScanner): vol = opt.get("volume", 0) oi = opt.get("openInterest", 0) if oi > 0 and vol > self.min_volume and (vol / oi) >= self.min_volume_oi_ratio: - unusual_strikes.append({ - "type": "call", - "strike": opt["strike"], - "volume": vol, - "oi": oi - }) + unusual_strikes.append( + {"type": "call", "strike": opt["strike"], "volume": vol, "oi": oi} + ) if not unusual_strikes: return None @@ -81,7 +81,7 @@ class OptionsFlowScanner(BaseScanner): "context": f"Unusual options: {len(unusual_strikes)} strikes, P/C={pc_ratio:.2f} ({sentiment})", "priority": "high" if sentiment == "bullish" else "medium", "strategy": "options_flow", - "put_call_ratio": round(pc_ratio, 2) + "put_call_ratio": round(pc_ratio, 2), } except Exception: diff --git a/tradingagents/dataflows/discovery/scanners/reddit_dd.py b/tradingagents/dataflows/discovery/scanners/reddit_dd.py index d49e0093..59e4135d 100644 --- a/tradingagents/dataflows/discovery/scanners/reddit_dd.py +++ b/tradingagents/dataflows/discovery/scanners/reddit_dd.py @@ -1,9 +1,13 @@ """Reddit DD (Due Diligence) scanner.""" + from typing import Any, Dict, List -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority from tradingagents.tools.executor import execute_tool +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class RedditDDScanner(BaseScanner): @@ -19,17 +23,14 @@ class RedditDDScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 📝 Scanning Reddit DD posts...") + logger.info("📝 Scanning Reddit DD posts...") try: # Use Reddit DD scanner tool - result = execute_tool( - "scan_reddit_dd", - limit=self.limit - ) + result = execute_tool("scan_reddit_dd", limit=self.limit) if not result: - print(f" Found 0 DD posts") + logger.info("Found 0 DD posts") return [] candidates = [] @@ -37,7 +38,7 @@ class RedditDDScanner(BaseScanner): # Handle different result formats if isinstance(result, list): # Structured result with DD posts - for post in result[:self.limit]: + for post in result[: self.limit]: ticker = post.get("ticker", "").upper() if not ticker: continue @@ -48,39 +49,43 @@ class RedditDDScanner(BaseScanner): # Higher score = higher priority priority = Priority.HIGH.value if score > 1000 else Priority.MEDIUM.value - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Reddit DD: {title[:80]}... (score: {score})", - "priority": priority, - "strategy": "undiscovered_dd", - "dd_score": score, - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Reddit DD: {title[:80]}... (score: {score})", + "priority": priority, + "strategy": "undiscovered_dd", + "dd_score": score, + } + ) elif isinstance(result, dict): # Dict format - for ticker_data in result.get("posts", [])[:self.limit]: + for ticker_data in result.get("posts", [])[: self.limit]: ticker = ticker_data.get("ticker", "").upper() if not ticker: continue - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Reddit DD post", - "priority": Priority.MEDIUM.value, - "strategy": "undiscovered_dd", - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": "Reddit DD post", + "priority": Priority.MEDIUM.value, + "strategy": "undiscovered_dd", + } + ) elif isinstance(result, str): # Text result - extract tickers candidates = self._parse_text_result(result) - print(f" Found {len(candidates)} DD posts") + logger.info(f"Found {len(candidates)} DD posts") return candidates except Exception as e: - print(f" ⚠️ Reddit DD scan failed, using fallback: {e}") + logger.warning(f"⚠️ Reddit DD scan failed, using fallback: {e}") return self._fallback_dd_scan() def _fallback_dd_scan(self) -> List[Dict[str, Any]]: @@ -99,7 +104,8 @@ class RedditDDScanner(BaseScanner): for submission in subreddit.search("flair:DD", limit=self.limit * 2): # Extract ticker from title import re - ticker_pattern = r'\$([A-Z]{2,5})\b|^([A-Z]{2,5})\s' + + ticker_pattern = r"\$([A-Z]{2,5})\b|^([A-Z]{2,5})\s" matches = re.findall(ticker_pattern, submission.title) if not matches: @@ -111,19 +117,21 @@ class RedditDDScanner(BaseScanner): seen_tickers.add(ticker) - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Reddit DD: {submission.title[:80]}...", - "priority": Priority.MEDIUM.value, - "strategy": "undiscovered_dd", - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Reddit DD: {submission.title[:80]}...", + "priority": Priority.MEDIUM.value, + "strategy": "undiscovered_dd", + } + ) if len(candidates) >= self.limit: break return candidates - except: + except Exception: return [] def _parse_text_result(self, text: str) -> List[Dict[str, Any]]: @@ -131,19 +139,21 @@ class RedditDDScanner(BaseScanner): import re candidates = [] - ticker_pattern = r'\$([A-Z]{2,5})\b|^([A-Z]{2,5})\s' + ticker_pattern = r"\$([A-Z]{2,5})\b|^([A-Z]{2,5})\s" matches = re.findall(ticker_pattern, text) tickers = list(set([t[0] or t[1] for t in matches if t[0] or t[1]])) - for ticker in tickers[:self.limit]: - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": "Reddit DD post", - "priority": Priority.MEDIUM.value, - "strategy": "undiscovered_dd", - }) + for ticker in tickers[: self.limit]: + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": "Reddit DD post", + "priority": Priority.MEDIUM.value, + "strategy": "undiscovered_dd", + } + ) return candidates diff --git a/tradingagents/dataflows/discovery/scanners/reddit_trending.py b/tradingagents/dataflows/discovery/scanners/reddit_trending.py index eb9cb416..ca7da9c3 100644 --- a/tradingagents/dataflows/discovery/scanners/reddit_trending.py +++ b/tradingagents/dataflows/discovery/scanners/reddit_trending.py @@ -1,8 +1,12 @@ """Reddit trending scanner - migrated from legacy TraditionalScanner.""" + from typing import Any, Dict, List -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class RedditTrendingScanner(BaseScanner): @@ -18,21 +22,18 @@ class RedditTrendingScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 📱 Scanning Reddit trending...") + logger.info("📱 Scanning Reddit trending...") from tradingagents.tools.executor import execute_tool try: - result = execute_tool( - "get_trending_tickers", - limit=self.limit - ) + result = execute_tool("get_trending_tickers", limit=self.limit) if not result or not isinstance(result, str): return [] if "Error" in result or "No trending" in result: - print(f" ⚠️ {result}") + logger.warning(f"⚠️ {result}") return [] # Extract tickers using common utility @@ -41,20 +42,22 @@ class RedditTrendingScanner(BaseScanner): tickers_found = extract_tickers_from_text(result) candidates = [] - for ticker in tickers_found[:self.limit]: - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Reddit trending discussion", - "priority": Priority.MEDIUM.value, - "strategy": "social_hype", - }) + for ticker in tickers_found[: self.limit]: + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": "Reddit trending discussion", + "priority": Priority.MEDIUM.value, + "strategy": "social_hype", + } + ) - print(f" Found {len(candidates)} Reddit trending tickers") + logger.info(f"Found {len(candidates)} Reddit trending tickers") return candidates except Exception as e: - print(f" ⚠️ Reddit trending failed: {e}") + logger.warning(f"⚠️ Reddit trending failed: {e}") return [] diff --git a/tradingagents/dataflows/discovery/scanners/semantic_news.py b/tradingagents/dataflows/discovery/scanners/semantic_news.py index fbaa67be..86f1fc9b 100644 --- a/tradingagents/dataflows/discovery/scanners/semantic_news.py +++ b/tradingagents/dataflows/discovery/scanners/semantic_news.py @@ -1,8 +1,12 @@ """Semantic news scanner for early catalyst detection.""" + from typing import Any, Dict, List -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class SemanticNewsScanner(BaseScanner): @@ -22,12 +26,13 @@ class SemanticNewsScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 📰 Scanning news catalysts...") + logger.info("📰 Scanning news catalysts...") try: - from tradingagents.tools.executor import execute_tool from datetime import datetime + from tradingagents.tools.executor import execute_tool + # Get recent global news date_str = datetime.now().strftime("%Y-%m-%d") result = execute_tool("get_global_news", date=date_str) @@ -37,30 +42,44 @@ class SemanticNewsScanner(BaseScanner): # Extract tickers mentioned in news import re - ticker_pattern = r'\b([A-Z]{2,5})\b|\$([A-Z]{2,5})' + + ticker_pattern = r"\b([A-Z]{2,5})\b|\$([A-Z]{2,5})" matches = re.findall(ticker_pattern, result) tickers = list(set([t[0] or t[1] for t in matches if t[0] or t[1]])) - stop_words = {'NYSE', 'NASDAQ', 'CEO', 'CFO', 'IPO', 'ETF', 'USA', 'SEC', 'NEWS', 'STOCK', 'MARKET'} + stop_words = { + "NYSE", + "NASDAQ", + "CEO", + "CFO", + "IPO", + "ETF", + "USA", + "SEC", + "NEWS", + "STOCK", + "MARKET", + } tickers = [t for t in tickers if t not in stop_words] candidates = [] - for ticker in tickers[:self.limit]: - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": "Mentioned in recent market news", - "priority": Priority.MEDIUM.value, - "strategy": "news_catalyst", - }) + for ticker in tickers[: self.limit]: + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": "Mentioned in recent market news", + "priority": Priority.MEDIUM.value, + "strategy": "news_catalyst", + } + ) - print(f" Found {len(candidates)} news mentions") + logger.info(f"Found {len(candidates)} news mentions") return candidates except Exception as e: - print(f" ⚠️ News scan failed: {e}") + logger.warning(f"⚠️ News scan failed: {e}") return [] - SCANNER_REGISTRY.register(SemanticNewsScanner) diff --git a/tradingagents/dataflows/discovery/scanners/volume_accumulation.py b/tradingagents/dataflows/discovery/scanners/volume_accumulation.py index aee80aa6..ae08818b 100644 --- a/tradingagents/dataflows/discovery/scanners/volume_accumulation.py +++ b/tradingagents/dataflows/discovery/scanners/volume_accumulation.py @@ -1,9 +1,13 @@ """Volume accumulation and compression scanner.""" + from typing import Any, Dict, List -from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY +from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner from tradingagents.dataflows.discovery.utils import Priority from tradingagents.tools.executor import execute_tool +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class VolumeAccumulationScanner(BaseScanner): @@ -21,18 +25,18 @@ class VolumeAccumulationScanner(BaseScanner): if not self.is_enabled(): return [] - print(f" 📊 Scanning volume accumulation...") + logger.info("📊 Scanning volume accumulation...") try: # Use volume scanner tool result = execute_tool( "get_unusual_volume", min_volume_multiple=self.unusual_volume_multiple, - top_n=self.limit + top_n=self.limit, ) if not result: - print(f" Found 0 volume accumulation candidates") + logger.info("Found 0 volume accumulation candidates") return [] candidates = [] @@ -43,7 +47,7 @@ class VolumeAccumulationScanner(BaseScanner): candidates = self._parse_text_result(result) elif isinstance(result, list): # Structured result - for item in result[:self.limit]: + for item in result[: self.limit]: ticker = item.get("ticker", "").upper() if not ticker: continue @@ -51,29 +55,35 @@ class VolumeAccumulationScanner(BaseScanner): volume_ratio = item.get("volume_ratio", 0) avg_volume = item.get("avg_volume", 0) - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": f"Unusual volume: {volume_ratio:.1f}x average ({avg_volume:,})", - "priority": Priority.MEDIUM.value if volume_ratio < 3.0 else Priority.HIGH.value, - "strategy": "volume_accumulation", - }) + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": f"Unusual volume: {volume_ratio:.1f}x average ({avg_volume:,})", + "priority": ( + Priority.MEDIUM.value if volume_ratio < 3.0 else Priority.HIGH.value + ), + "strategy": "volume_accumulation", + } + ) elif isinstance(result, dict): # Dict with tickers list - for ticker in result.get("tickers", [])[:self.limit]: - candidates.append({ - "ticker": ticker.upper(), - "source": self.name, - "context": f"Unusual volume accumulation", - "priority": Priority.MEDIUM.value, - "strategy": "volume_accumulation", - }) + for ticker in result.get("tickers", [])[: self.limit]: + candidates.append( + { + "ticker": ticker.upper(), + "source": self.name, + "context": "Unusual volume accumulation", + "priority": Priority.MEDIUM.value, + "strategy": "volume_accumulation", + } + ) - print(f" Found {len(candidates)} volume accumulation candidates") + logger.info(f"Found {len(candidates)} volume accumulation candidates") return candidates except Exception as e: - print(f" ⚠️ Volume accumulation failed: {e}") + logger.warning(f"⚠️ Volume accumulation failed: {e}") return [] def _parse_text_result(self, text: str) -> List[Dict[str, Any]]: @@ -83,14 +93,16 @@ class VolumeAccumulationScanner(BaseScanner): candidates = [] tickers = extract_tickers_from_text(text) - for ticker in tickers[:self.limit]: - candidates.append({ - "ticker": ticker, - "source": self.name, - "context": "Unusual volume detected", - "priority": Priority.MEDIUM.value, - "strategy": "volume_accumulation", - }) + for ticker in tickers[: self.limit]: + candidates.append( + { + "ticker": ticker, + "source": self.name, + "context": "Unusual volume detected", + "priority": Priority.MEDIUM.value, + "strategy": "volume_accumulation", + } + ) return candidates diff --git a/tradingagents/dataflows/discovery/ticker_matcher.py b/tradingagents/dataflows/discovery/ticker_matcher.py index d476f32c..f4b9e676 100644 --- a/tradingagents/dataflows/discovery/ticker_matcher.py +++ b/tradingagents/dataflows/discovery/ticker_matcher.py @@ -6,7 +6,7 @@ with the ticker universe CSV. Usage: from tradingagents.dataflows.discovery.ticker_matcher import match_company_to_ticker - + ticker = match_company_to_ticker("Apple Inc") # Returns: "AAPL" """ @@ -14,108 +14,116 @@ Usage: import csv import re from pathlib import Path -from typing import Dict, Optional, Tuple +from typing import Dict, Optional + from rapidfuzz import fuzz, process +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + # Global cache _TICKER_UNIVERSE: Optional[Dict[str, str]] = None # ticker -> name -_NAME_TO_TICKER: Optional[Dict[str, str]] = None # normalized_name -> ticker -_MATCH_CACHE: Dict[str, Optional[str]] = {} # company_name -> ticker +_NAME_TO_TICKER: Optional[Dict[str, str]] = None # normalized_name -> ticker +_MATCH_CACHE: Dict[str, Optional[str]] = {} # company_name -> ticker def _normalize_company_name(name: str) -> str: """ Normalize company name for matching. - + Removes common suffixes, punctuation, and standardizes format. """ if not name: return "" - + # Convert to uppercase name = name.upper() - + # Remove common suffixes suffixes = [ - r'\s+INC\.?', - r'\s+INCORPORATED', - r'\s+CORP\.?', - r'\s+CORPORATION', - r'\s+LTD\.?', - r'\s+LIMITED', - r'\s+LLC', - r'\s+L\.?L\.?C\.?', - r'\s+PLC', - r'\s+CO\.?', - r'\s+COMPANY', - r'\s+CLASS [A-Z]', - r'\s+COMMON STOCK', - r'\s+ORDINARY SHARES?', - r'\s+-\s+.*$', # Remove everything after dash - r'\s+\(.*?\)', # Remove parenthetical + r"\s+INC\.?", + r"\s+INCORPORATED", + r"\s+CORP\.?", + r"\s+CORPORATION", + r"\s+LTD\.?", + r"\s+LIMITED", + r"\s+LLC", + r"\s+L\.?L\.?C\.?", + r"\s+PLC", + r"\s+CO\.?", + r"\s+COMPANY", + r"\s+CLASS [A-Z]", + r"\s+COMMON STOCK", + r"\s+ORDINARY SHARES?", + r"\s+-\s+.*$", # Remove everything after dash + r"\s+\(.*?\)", # Remove parenthetical ] - + for suffix in suffixes: - name = re.sub(suffix, '', name, flags=re.IGNORECASE) - + name = re.sub(suffix, "", name, flags=re.IGNORECASE) + # Remove punctuation except spaces - name = re.sub(r'[^\w\s]', '', name) - + name = re.sub(r"[^\w\s]", "", name) + # Normalize whitespace - name = ' '.join(name.split()) - + name = " ".join(name.split()) + return name.strip() def load_ticker_universe(force_reload: bool = False) -> Dict[str, str]: """ Load ticker universe from CSV. - + Args: force_reload: Force reload even if already loaded - + Returns: Dict mapping ticker -> company name """ global _TICKER_UNIVERSE, _NAME_TO_TICKER - + if _TICKER_UNIVERSE is not None and not force_reload: return _TICKER_UNIVERSE - + # Find CSV file project_root = Path(__file__).parent.parent.parent.parent csv_path = project_root / "data" / "ticker_universe.csv" - + if not csv_path.exists(): raise FileNotFoundError(f"Ticker universe not found: {csv_path}") - + ticker_universe = {} name_to_ticker = {} - - with open(csv_path, 'r', encoding='utf-8') as f: + + with open(csv_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: - ticker = row['ticker'] - name = row['name'] - + ticker = row["ticker"] + name = row["name"] + # Store ticker -> name mapping ticker_universe[ticker] = name - + # Build reverse index (normalized name -> ticker) normalized = _normalize_company_name(name) if normalized: # If multiple tickers have same normalized name, prefer common stocks if normalized not in name_to_ticker: name_to_ticker[normalized] = ticker - elif "COMMON" in name.upper() and "COMMON" not in ticker_universe.get(name_to_ticker[normalized], "").upper(): + elif ( + "COMMON" in name.upper() + and "COMMON" not in ticker_universe.get(name_to_ticker[normalized], "").upper() + ): # Prefer common stock over other securities name_to_ticker[normalized] = ticker - + _TICKER_UNIVERSE = ticker_universe _NAME_TO_TICKER = name_to_ticker - - print(f" Loaded {len(ticker_universe)} tickers from universe") - + + logger.info(f"Loaded {len(ticker_universe)} tickers from universe") + return ticker_universe @@ -126,15 +134,15 @@ def match_company_to_ticker( ) -> Optional[str]: """ Match a company name to a ticker symbol using fuzzy matching. - + Args: company_name: Company name from 13F filing min_confidence: Minimum fuzzy match score (0-100) use_cache: Use cached results - + Returns: Ticker symbol or None if no good match found - + Examples: >>> match_company_to_ticker("Apple Inc") 'AAPL' @@ -145,51 +153,48 @@ def match_company_to_ticker( """ if not company_name: return None - + # Check cache if use_cache and company_name in _MATCH_CACHE: return _MATCH_CACHE[company_name] - + # Ensure universe is loaded if _TICKER_UNIVERSE is None or _NAME_TO_TICKER is None: load_ticker_universe() - + # Normalize input normalized_input = _normalize_company_name(company_name) - + if not normalized_input: return None - + # Try exact match first if normalized_input in _NAME_TO_TICKER: result = _NAME_TO_TICKER[normalized_input] _MATCH_CACHE[company_name] = result return result - + # Fuzzy match against all normalized names choices = list(_NAME_TO_TICKER.keys()) - + # Use token_sort_ratio for best results with company names match_result = process.extractOne( - normalized_input, - choices, - scorer=fuzz.token_sort_ratio, - score_cutoff=min_confidence + normalized_input, choices, scorer=fuzz.token_sort_ratio, score_cutoff=min_confidence ) - + if match_result: matched_name, score, _ = match_result ticker = _NAME_TO_TICKER[matched_name] - + # Log match for debugging if score < 95: - print(f" Fuzzy match: '{company_name}' -> {ticker} (score: {score:.1f})") - + logger.info(f"Fuzzy match: '{company_name}' -> {ticker} (score: {score:.1f})") + _MATCH_CACHE[company_name] = ticker return ticker - + # No match found - print(f" No ticker match for: '{company_name}'") + logger.info(f"No ticker match for: '{company_name}'") _MATCH_CACHE[company_name] = None return None @@ -197,26 +202,26 @@ def match_company_to_ticker( def get_match_confidence(company_name: str, ticker: str) -> float: """ Get confidence score for a company name -> ticker match. - + Args: company_name: Company name ticker: Ticker symbol - + Returns: Confidence score (0-100) """ if _TICKER_UNIVERSE is None: load_ticker_universe() - + if ticker not in _TICKER_UNIVERSE: return 0.0 - + ticker_name = _TICKER_UNIVERSE[ticker] - + # Normalize both names norm_input = _normalize_company_name(company_name) norm_ticker = _normalize_company_name(ticker_name) - + # Calculate similarity return fuzz.token_sort_ratio(norm_input, norm_ticker) diff --git a/tradingagents/dataflows/discovery/utils.py b/tradingagents/dataflows/discovery/utils.py index 7e2e672a..fcbaa76e 100644 --- a/tradingagents/dataflows/discovery/utils.py +++ b/tradingagents/dataflows/discovery/utils.py @@ -22,6 +22,7 @@ PERMANENTLY_DELISTED = { "SVIVU", } + # Priority and strategy enums for consistent labeling. class Priority(str, Enum): CRITICAL = "critical" @@ -123,6 +124,7 @@ def append_llm_log( tool_logs.append(entry) return entry + def get_delisted_tickers() -> Set[str]: """Get combined list of delisted tickers from permanent list + dynamic cache.""" # Local import to avoid circular dependencies if any diff --git a/tradingagents/dataflows/finnhub_api.py b/tradingagents/dataflows/finnhub_api.py index 607a6d7b..7a6359a6 100644 --- a/tradingagents/dataflows/finnhub_api.py +++ b/tradingagents/dataflows/finnhub_api.py @@ -1,50 +1,55 @@ -import os +from datetime import datetime +from typing import Annotated, Any, Dict + import finnhub -from typing import Annotated from dotenv import load_dotenv +from tradingagents.utils.logger import get_logger + +from tradingagents.config import config + load_dotenv() +logger = get_logger(__name__) + + def get_finnhub_client(): """Get authenticated Finnhub client.""" - api_key = os.getenv("FINNHUB_API_KEY") - if not api_key: - raise ValueError("FINNHUB_API_KEY not found in environment variables.") + api_key = config.validate_key("finnhub_api_key", "Finnhub") return finnhub.Client(api_key=api_key) -def get_recommendation_trends( - ticker: Annotated[str, "Ticker symbol of the company"] -) -> str: + +def get_recommendation_trends(ticker: Annotated[str, "Ticker symbol of the company"]) -> str: """ Get analyst recommendation trends for a stock. Shows the distribution of buy/hold/sell recommendations over time. - + Args: ticker: Stock ticker symbol (e.g., "AAPL", "TSLA") - + Returns: str: Formatted report of recommendation trends """ try: client = get_finnhub_client() data = client.recommendation_trends(ticker.upper()) - + if not data: return f"No recommendation trends data found for {ticker}" - + # Format the response result = f"## Analyst Recommendation Trends for {ticker.upper()}\n\n" - + for entry in data: - period = entry.get('period', 'N/A') - strong_buy = entry.get('strongBuy', 0) - buy = entry.get('buy', 0) - hold = entry.get('hold', 0) - sell = entry.get('sell', 0) - strong_sell = entry.get('strongSell', 0) - + period = entry.get("period", "N/A") + strong_buy = entry.get("strongBuy", 0) + buy = entry.get("buy", 0) + hold = entry.get("hold", 0) + sell = entry.get("sell", 0) + strong_sell = entry.get("strongSell", 0) + total = strong_buy + buy + hold + sell + strong_sell - + result += f"### {period}\n" result += f"- **Strong Buy**: {strong_buy}\n" result += f"- **Buy**: {buy}\n" @@ -52,32 +57,37 @@ def get_recommendation_trends( result += f"- **Sell**: {sell}\n" result += f"- **Strong Sell**: {strong_sell}\n" result += f"- **Total Analysts**: {total}\n\n" - + # Calculate sentiment if total > 0: bullish_pct = ((strong_buy + buy) / total) * 100 bearish_pct = ((sell + strong_sell) / total) * 100 - result += f"**Sentiment**: {bullish_pct:.1f}% Bullish, {bearish_pct:.1f}% Bearish\n\n" - + result += ( + f"**Sentiment**: {bullish_pct:.1f}% Bullish, {bearish_pct:.1f}% Bearish\n\n" + ) + return result - + except Exception as e: return f"Error fetching recommendation trends for {ticker}: {str(e)}" def get_earnings_calendar( from_date: Annotated[str, "Start date in yyyy-mm-dd format"], - to_date: Annotated[str, "End date in yyyy-mm-dd format"] -) -> str: + to_date: Annotated[str, "End date in yyyy-mm-dd format"], + return_structured: Annotated[bool, "Return list of dicts instead of markdown"] = False, +): """ Get earnings calendar for stocks with upcoming earnings announcements. Args: from_date: Start date in yyyy-mm-dd format to_date: End date in yyyy-mm-dd format + return_structured: If True, returns list of earnings dicts instead of markdown Returns: - str: Formatted report of upcoming earnings + If return_structured=True: list of earnings dicts with symbol, date, epsEstimate, etc. + If return_structured=False: Formatted markdown report """ try: client = get_finnhub_client() @@ -85,17 +95,25 @@ def get_earnings_calendar( _from=from_date, to=to_date, symbol="", # Empty string returns all stocks - international=False + international=False, ) - if not data or 'earningsCalendar' not in data: + if not data or "earningsCalendar" not in data: + if return_structured: + return [] return f"No earnings data found for period {from_date} to {to_date}" - earnings = data['earningsCalendar'] + earnings = data["earningsCalendar"] if not earnings: + if return_structured: + return [] return f"No earnings scheduled between {from_date} and {to_date}" + # Return structured data if requested + if return_structured: + return earnings + # Format the response result = f"## Earnings Calendar ({from_date} to {to_date})\n\n" result += f"**Total Companies**: {len(earnings)}\n\n" @@ -103,7 +121,7 @@ def get_earnings_calendar( # Group by date by_date = {} for entry in earnings: - date = entry.get('date', 'Unknown') + date = entry.get("date", "Unknown") if date not in by_date: by_date[date] = [] by_date[date].append(entry) @@ -113,28 +131,44 @@ def get_earnings_calendar( result += f"### {date}\n\n" for entry in by_date[date]: - symbol = entry.get('symbol', 'N/A') - eps_estimate = entry.get('epsEstimate', 'N/A') - eps_actual = entry.get('epsActual', 'N/A') - revenue_estimate = entry.get('revenueEstimate', 'N/A') - revenue_actual = entry.get('revenueActual', 'N/A') - hour = entry.get('hour', 'N/A') + symbol = entry.get("symbol", "N/A") + eps_estimate = entry.get("epsEstimate", "N/A") + eps_actual = entry.get("epsActual", "N/A") + revenue_estimate = entry.get("revenueEstimate", "N/A") + revenue_actual = entry.get("revenueActual", "N/A") + hour = entry.get("hour", "N/A") result += f"**{symbol}**" - if hour != 'N/A': + if hour != "N/A": result += f" ({hour})" result += "\n" - if eps_estimate != 'N/A': - result += f" - EPS Estimate: ${eps_estimate:.2f}" if isinstance(eps_estimate, (int, float)) else f" - EPS Estimate: {eps_estimate}" - if eps_actual != 'N/A': - result += f" | Actual: ${eps_actual:.2f}" if isinstance(eps_actual, (int, float)) else f" | Actual: {eps_actual}" + if eps_estimate != "N/A": + result += ( + f" - EPS Estimate: ${eps_estimate:.2f}" + if isinstance(eps_estimate, (int, float)) + else f" - EPS Estimate: {eps_estimate}" + ) + if eps_actual != "N/A": + result += ( + f" | Actual: ${eps_actual:.2f}" + if isinstance(eps_actual, (int, float)) + else f" | Actual: {eps_actual}" + ) result += "\n" - if revenue_estimate != 'N/A': - result += f" - Revenue Estimate: ${revenue_estimate:,.0f}M" if isinstance(revenue_estimate, (int, float)) else f" - Revenue Estimate: {revenue_estimate}" - if revenue_actual != 'N/A': - result += f" | Actual: ${revenue_actual:,.0f}M" if isinstance(revenue_actual, (int, float)) else f" | Actual: {revenue_actual}" + if revenue_estimate != "N/A": + result += ( + f" - Revenue Estimate: ${revenue_estimate:,.0f}M" + if isinstance(revenue_estimate, (int, float)) + else f" - Revenue Estimate: {revenue_estimate}" + ) + if revenue_actual != "N/A": + result += ( + f" | Actual: ${revenue_actual:,.0f}M" + if isinstance(revenue_actual, (int, float)) + else f" | Actual: {revenue_actual}" + ) result += "\n" result += "\n" @@ -142,38 +176,105 @@ def get_earnings_calendar( return result except Exception as e: + if return_structured: + return [] return f"Error fetching earnings calendar: {str(e)}" +def get_ticker_earnings_estimate( + ticker: str, + from_date: str, + to_date: str, +) -> Dict[str, Any]: + """ + Get upcoming earnings estimate for a single ticker. + + Returns dict with: has_upcoming_earnings, days_to_earnings, + eps_estimate, revenue_estimate, earnings_date, hour. + """ + result: Dict[str, Any] = { + "has_upcoming_earnings": False, + "days_to_earnings": None, + "eps_estimate": None, + "revenue_estimate": None, + "earnings_date": None, + "hour": None, + } + try: + client = get_finnhub_client() + data = client.earnings_calendar( + _from=from_date, + to=to_date, + symbol=ticker.upper(), + international=False, + ) + if not data or "earningsCalendar" not in data: + return result + + earnings = data["earningsCalendar"] + if not earnings: + return result + + # Take the nearest upcoming entry + entry = earnings[0] + earnings_date = entry.get("date") + if earnings_date: + try: + ed = datetime.strptime(earnings_date, "%Y-%m-%d") + fd = datetime.strptime(from_date, "%Y-%m-%d") + result["days_to_earnings"] = (ed - fd).days + except ValueError: + pass + + result["has_upcoming_earnings"] = True + result["earnings_date"] = earnings_date + result["eps_estimate"] = entry.get("epsEstimate") + result["revenue_estimate"] = entry.get("revenueEstimate") + result["hour"] = entry.get("hour") + return result + + except Exception as e: + logger.warning(f"Could not fetch earnings estimate for {ticker}: {e}") + return result + + def get_ipo_calendar( from_date: Annotated[str, "Start date in yyyy-mm-dd format"], - to_date: Annotated[str, "End date in yyyy-mm-dd format"] -) -> str: + to_date: Annotated[str, "End date in yyyy-mm-dd format"], + return_structured: Annotated[bool, "Return list of dicts instead of markdown"] = False, +): """ Get IPO calendar for upcoming and recent initial public offerings. Args: from_date: Start date in yyyy-mm-dd format to_date: End date in yyyy-mm-dd format + return_structured: If True, returns list of IPO dicts instead of markdown Returns: - str: Formatted report of IPOs + If return_structured=True: list of IPO dicts with symbol, name, date, etc. + If return_structured=False: Formatted markdown report """ try: client = get_finnhub_client() - data = client.ipo_calendar( - _from=from_date, - to=to_date - ) + data = client.ipo_calendar(_from=from_date, to=to_date) - if not data or 'ipoCalendar' not in data: + if not data or "ipoCalendar" not in data: + if return_structured: + return [] return f"No IPO data found for period {from_date} to {to_date}" - ipos = data['ipoCalendar'] + ipos = data["ipoCalendar"] if not ipos: + if return_structured: + return [] return f"No IPOs scheduled between {from_date} and {to_date}" + # Return structured data if requested + if return_structured: + return ipos + # Format the response result = f"## IPO Calendar ({from_date} to {to_date})\n\n" result += f"**Total IPOs**: {len(ipos)}\n\n" @@ -181,7 +282,7 @@ def get_ipo_calendar( # Group by date by_date = {} for entry in ipos: - date = entry.get('date', 'Unknown') + date = entry.get("date", "Unknown") if date not in by_date: by_date[date] = [] by_date[date].append(entry) @@ -191,29 +292,39 @@ def get_ipo_calendar( result += f"### {date}\n\n" for entry in by_date[date]: - symbol = entry.get('symbol', 'N/A') - name = entry.get('name', 'N/A') - exchange = entry.get('exchange', 'N/A') - price = entry.get('price', 'N/A') - shares = entry.get('numberOfShares', 'N/A') - total_shares = entry.get('totalSharesValue', 'N/A') - status = entry.get('status', 'N/A') + symbol = entry.get("symbol", "N/A") + name = entry.get("name", "N/A") + exchange = entry.get("exchange", "N/A") + price = entry.get("price", "N/A") + shares = entry.get("numberOfShares", "N/A") + total_shares = entry.get("totalSharesValue", "N/A") + status = entry.get("status", "N/A") result += f"**{symbol}** - {name}\n" result += f" - Exchange: {exchange}\n" - if price != 'N/A': + if price != "N/A": result += f" - Price: ${price}\n" - if shares != 'N/A': - result += f" - Shares Offered: {shares:,}\n" if isinstance(shares, (int, float)) else f" - Shares Offered: {shares}\n" + if shares != "N/A": + result += ( + f" - Shares Offered: {shares:,}\n" + if isinstance(shares, (int, float)) + else f" - Shares Offered: {shares}\n" + ) - if total_shares != 'N/A': - result += f" - Total Value: ${total_shares:,.0f}M\n" if isinstance(total_shares, (int, float)) else f" - Total Value: {total_shares}\n" + if total_shares != "N/A": + result += ( + f" - Total Value: ${total_shares:,.0f}M\n" + if isinstance(total_shares, (int, float)) + else f" - Total Value: {total_shares}\n" + ) result += f" - Status: {status}\n\n" return result except Exception as e: + if return_structured: + return [] return f"Error fetching IPO calendar: {str(e)}" diff --git a/tradingagents/dataflows/finviz_scraper.py b/tradingagents/dataflows/finviz_scraper.py index 68e7328f..ed661d3f 100644 --- a/tradingagents/dataflows/finviz_scraper.py +++ b/tradingagents/dataflows/finviz_scraper.py @@ -3,19 +3,25 @@ Finviz + Yahoo Finance Hybrid - Short Interest Discovery Uses Finviz to discover tickers with high short interest, then Yahoo Finance for exact data """ +import re +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Annotated + import requests from bs4 import BeautifulSoup -from typing import Annotated -import re -import yfinance as yf -from concurrent.futures import ThreadPoolExecutor, as_completed + +from tradingagents.dataflows.y_finance import get_ticker_info +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) def get_short_interest( min_short_interest_pct: Annotated[float, "Minimum short interest % of float"] = 10.0, min_days_to_cover: Annotated[float, "Minimum days to cover ratio"] = 2.0, top_n: Annotated[int, "Number of top results to return"] = 20, -) -> str: + return_structured: Annotated[bool, "Return dict with raw data instead of markdown"] = False, +): """ Discover stocks with high short interest using Finviz + Yahoo Finance. @@ -29,13 +35,17 @@ def get_short_interest( min_short_interest_pct: Minimum short interest as % of float min_days_to_cover: Minimum days to cover ratio top_n: Number of top results to return + return_structured: If True, returns list of dicts instead of markdown Returns: - Formatted markdown report of discovered high short interest stocks + If return_structured=True: list of candidate dicts with ticker, short_interest_pct, signal, etc. + If return_structured=False: Formatted markdown report """ try: # Step 1: Use Finviz screener to DISCOVER tickers with high short interest - print(f" Discovering tickers with short interest >{min_short_interest_pct}% from Finviz...") + logger.info( + f"Discovering tickers with short interest >{min_short_interest_pct}% from Finviz..." + ) # Determine Finviz filter if min_short_interest_pct >= 20: @@ -51,8 +61,8 @@ def get_short_interest( base_url = f"https://finviz.com/screener.ashx?v=152&f={short_filter}" headers = { - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', - 'Accept': 'text/html', + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36", + "Accept": "text/html", } discovered_tickers = [] @@ -68,31 +78,32 @@ def get_short_interest( response = requests.get(url, headers=headers, timeout=30) response.raise_for_status() - soup = BeautifulSoup(response.text, 'html.parser') + soup = BeautifulSoup(response.text, "html.parser") # Find ticker links in the page - ticker_links = soup.find_all('a', href=re.compile(r'quote\.ashx\?t=')) + ticker_links = soup.find_all("a", href=re.compile(r"quote\.ashx\?t=")) for link in ticker_links: ticker = link.get_text(strip=True) # Validate it's a ticker (1-5 uppercase letters) - if re.match(r'^[A-Z]{1,5}$', ticker) and ticker not in discovered_tickers: + if re.match(r"^[A-Z]{1,5}$", ticker) and ticker not in discovered_tickers: discovered_tickers.append(ticker) if not discovered_tickers: + if return_structured: + return [] return f"No stocks discovered with short interest >{min_short_interest_pct}% on Finviz." - print(f" Discovered {len(discovered_tickers)} tickers from Finviz") - print(f" Fetching detailed short interest data from Yahoo Finance...") + logger.info(f"Discovered {len(discovered_tickers)} tickers from Finviz") + logger.info("Fetching detailed short interest data from Yahoo Finance...") # Step 2: Use Yahoo Finance to get EXACT short interest data for discovered tickers def fetch_short_data(ticker): try: - stock = yf.Ticker(ticker) - info = stock.info + info = get_ticker_info(ticker) # Get short interest data - short_pct = info.get('shortPercentOfFloat', info.get('sharesPercentSharesOut', 0)) + short_pct = info.get("shortPercentOfFloat", info.get("sharesPercentSharesOut", 0)) if short_pct and isinstance(short_pct, (int, float)): short_pct = short_pct * 100 # Convert to percentage else: @@ -100,9 +111,9 @@ def get_short_interest( # Verify it meets criteria (Finviz filter might be outdated) if short_pct >= min_short_interest_pct: - price = info.get('currentPrice', info.get('regularMarketPrice', 0)) - market_cap = info.get('marketCap', 0) - volume = info.get('volume', info.get('regularMarketVolume', 0)) + price = info.get("currentPrice", info.get("regularMarketPrice", 0)) + market_cap = info.get("marketCap", 0) + volume = info.get("volume", info.get("regularMarketVolume", 0)) # Categorize squeeze potential if short_pct >= 30: @@ -128,7 +139,9 @@ def get_short_interest( # Fetch data in parallel (faster) all_candidates = [] with ThreadPoolExecutor(max_workers=10) as executor: - futures = {executor.submit(fetch_short_data, ticker): ticker for ticker in discovered_tickers} + futures = { + executor.submit(fetch_short_data, ticker): ticker for ticker in discovered_tickers + } for future in as_completed(futures): result = future.result() @@ -136,26 +149,30 @@ def get_short_interest( all_candidates.append(result) if not all_candidates: + if return_structured: + return [] return f"No stocks with verified short interest >{min_short_interest_pct}% (Finviz found {len(discovered_tickers)} tickers but Yahoo Finance data didn't confirm)." # Sort by short interest percentage (highest first) sorted_candidates = sorted( - all_candidates, - key=lambda x: x["short_interest_pct"], - reverse=True + all_candidates, key=lambda x: x["short_interest_pct"], reverse=True )[:top_n] + # Return structured data if requested + if return_structured: + return sorted_candidates + # Format output - report = f"# Discovered High Short Interest Stocks\n\n" + report = "# Discovered High Short Interest Stocks\n\n" report += f"**Criteria**: Short Interest >{min_short_interest_pct}%\n" - report += f"**Data Source**: Finviz Screener (Web Scraping)\n" + report += "**Data Source**: Finviz Screener (Web Scraping)\n" report += f"**Total Discovered**: {len(all_candidates)} stocks\n\n" report += f"**Top {len(sorted_candidates)} Candidates**:\n\n" report += "| Ticker | Price | Market Cap | Volume | Short % | Signal |\n" report += "|--------|-------|------------|--------|---------|--------|\n" for candidate in sorted_candidates: - market_cap_str = format_market_cap(candidate['market_cap']) + market_cap_str = format_market_cap(candidate["market_cap"]) report += f"| {candidate['ticker']} | " report += f"${candidate['price']:.2f} | " report += f"{market_cap_str} | " @@ -166,38 +183,44 @@ def get_short_interest( report += "\n\n## Signal Definitions\n\n" report += "- **extreme_squeeze_risk**: Short interest >30% - Very high squeeze potential\n" report += "- **high_squeeze_potential**: Short interest 20-30% - High squeeze risk\n" - report += "- **moderate_squeeze_potential**: Short interest 15-20% - Moderate squeeze risk\n" + report += ( + "- **moderate_squeeze_potential**: Short interest 15-20% - Moderate squeeze risk\n" + ) report += "- **low_squeeze_potential**: Short interest 10-15% - Lower squeeze risk\n\n" report += "**Note**: High short interest alone doesn't guarantee a squeeze. Look for positive catalysts.\n" return report except requests.exceptions.RequestException as e: + if return_structured: + return [] return f"Error scraping Finviz: {str(e)}" except Exception as e: + if return_structured: + return [] return f"Unexpected error discovering short interest stocks: {str(e)}" def parse_market_cap(market_cap_text: str) -> float: """Parse market cap from Finviz format (e.g., '1.23B', '456M').""" - if not market_cap_text or market_cap_text == '-': + if not market_cap_text or market_cap_text == "-": return 0.0 market_cap_text = market_cap_text.upper().strip() # Extract number and multiplier - match = re.match(r'([0-9.]+)([BMK])?', market_cap_text) + match = re.match(r"([0-9.]+)([BMK])?", market_cap_text) if not match: return 0.0 number = float(match.group(1)) multiplier = match.group(2) - if multiplier == 'B': + if multiplier == "B": return number * 1_000_000_000 - elif multiplier == 'M': + elif multiplier == "M": return number * 1_000_000 - elif multiplier == 'K': + elif multiplier == "K": return number * 1_000 else: return number @@ -220,3 +243,210 @@ def get_finviz_short_interest( ) -> str: """Alias for get_short_interest to match registry naming convention""" return get_short_interest(min_short_interest_pct, min_days_to_cover, top_n) + + +def get_insider_buying_screener( + transaction_type: Annotated[str, "Transaction type: 'buy', 'sell', or 'any'"] = "buy", + lookback_days: Annotated[int, "Days to look back for transactions"] = 7, + min_value: Annotated[int, "Minimum transaction value in dollars"] = 25000, + top_n: Annotated[int, "Number of top results to return"] = 20, + return_structured: Annotated[bool, "Return list of dicts instead of markdown"] = False, +): + """ + Discover stocks with recent insider buying/selling using OpenInsider. + + LEADING INDICATOR: Insiders buying their own stock before price moves. + Results are sorted by transaction value (largest first). + + Args: + transaction_type: "buy" for purchases, "sell" for sales + lookback_days: Days to look back (default 7) + min_value: Minimum transaction value in dollars + top_n: Number of top results to return + return_structured: If True, returns list of dicts instead of markdown + + Returns: + If return_structured=True: list of transaction dicts + If return_structured=False: Formatted markdown report + """ + try: + filter_desc = "insider buying" if transaction_type == "buy" else "insider selling" + logger.info(f"Discovering tickers with {filter_desc} from OpenInsider...") + + # OpenInsider screener URL + # xp=1 means exclude private transactions + # fd=7 means last 7 days filing date + # vl=25 means minimum value $25k + if transaction_type == "buy": + url = f"http://openinsider.com/screener?s=&o=&pl=&ph=&ll=&lh=&fd={lookback_days}&fdr=&td=0&tdr=&fdlyl=&fdlyh=&dtefrom=&dteto=&xp=1&vl={min_value // 1000}&vh=&ocl=&och=&session=all&cnt=100&page=1" + else: + url = f"http://openinsider.com/screener?s=&o=&pl=&ph=&ll=&lh=&fd={lookback_days}&fdr=&td=0&tdr=&fdlyl=&fdlyh=&dtefrom=&dteto=&xs=1&vl={min_value // 1000}&vh=&ocl=&och=&sic1=-1&sicl=100&sich=9999&grp=0&nfl=&nfh=&nil=&nih=&nol=&noh=&v2l=&v2h=&oc2l=&oc2h=&sortcol=4&cnt=100&page=1" + + headers = { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36", + "Accept": "text/html", + } + + response = requests.get(url, headers=headers, timeout=60) + response.raise_for_status() + + soup = BeautifulSoup(response.text, "html.parser") + + # Find the main data table + table = soup.find("table", class_="tinytable") + if not table: + return f"No {filter_desc} data found on OpenInsider." + + tbody = table.find("tbody") + if not tbody: + return f"No {filter_desc} data found on OpenInsider." + + rows = tbody.find_all("tr") + + transactions = [] + + for row in rows: + cells = row.find_all("td") + if len(cells) < 12: + continue + + try: + # OpenInsider columns: + # 0: X (checkbox), 1: Filing Date, 2: Trade Date, 3: Ticker, 4: Company Name + # 5: Insider Name, 6: Title, 7: Trade Type, 8: Price, 9: Qty, 10: Owned, 11: ΔOwn, 12: Value + + ticker_cell = cells[3] + ticker_link = ticker_cell.find("a") + ticker = ticker_link.get_text(strip=True) if ticker_link else "" + + if not ticker or not re.match(r"^[A-Z]{1,5}$", ticker): + continue + + company = cells[4].get_text(strip=True)[:40] if len(cells) > 4 else "" + insider_name = cells[5].get_text(strip=True)[:25] if len(cells) > 5 else "" + title_raw = cells[6].get_text(strip=True) if len(cells) > 6 else "" + # "10%" means 10% beneficial owner - clarify for readability + title = "10% Owner" if title_raw == "10%" else title_raw[:20] + trade_type = cells[7].get_text(strip=True) if len(cells) > 7 else "" + price = cells[8].get_text(strip=True) if len(cells) > 8 else "" + qty = cells[9].get_text(strip=True) if len(cells) > 9 else "" + value_str = cells[12].get_text(strip=True) if len(cells) > 12 else "" + + # Filter by transaction type + trade_type_lower = trade_type.lower() + if ( + transaction_type == "buy" + and "buy" not in trade_type_lower + and "p -" not in trade_type_lower + ): + continue + if ( + transaction_type == "sell" + and "sale" not in trade_type_lower + and "s -" not in trade_type_lower + ): + continue + + # Parse value for sorting + value_num = 0 + if value_str: + # Remove $ and + signs, handle K/M suffixes + clean_value = ( + value_str.replace("$", "").replace("+", "").replace(",", "").strip() + ) + try: + if "M" in clean_value: + value_num = float(clean_value.replace("M", "")) * 1_000_000 + elif "K" in clean_value: + value_num = float(clean_value.replace("K", "")) * 1_000 + else: + value_num = float(clean_value) + except ValueError: + value_num = 0 + + transactions.append( + { + "ticker": ticker, + "company": company, + "insider": insider_name, + "title": title, + "trade_type": trade_type, + "price": price, + "qty": qty, + "value_str": value_str, + "value_num": value_num, + } + ) + + except Exception: + continue + + if not transactions: + if return_structured: + return [] + return f"No {filter_desc} transactions found in the last {lookback_days} days." + + # Sort by value (largest first) + transactions.sort(key=lambda x: x["value_num"], reverse=True) + + # Deduplicate by ticker, keeping the largest transaction per ticker + seen_tickers = set() + unique_transactions = [] + for t in transactions: + if t["ticker"] not in seen_tickers: + seen_tickers.add(t["ticker"]) + unique_transactions.append(t) + if len(unique_transactions) >= top_n: + break + + logger.info( + f"Discovered {len(unique_transactions)} tickers with {filter_desc} (sorted by value)" + ) + + # Return structured data if requested + if return_structured: + return unique_transactions + + # Format report + report_lines = [ + f"# Insider {'Buying' if transaction_type == 'buy' else 'Selling'} Report", + f"*Top {len(unique_transactions)} stocks by transaction value (last {lookback_days} days)*\n", + "| Ticker | Company | Insider | Title | Value | Price |", + "|--------|---------|---------|-------|-------|-------|", + ] + + for t in unique_transactions: + report_lines.append( + f"| {t['ticker']} | {t['company']} | {t['insider']} | {t['title']} | {t['value_str']} | {t['price']} |" + ) + + report_lines.append( + f"\n**Total: {len(unique_transactions)} stocks with significant {filter_desc}**" + ) + report_lines.append("*Sorted by transaction value (largest first)*") + + return "\n".join(report_lines) + + except requests.exceptions.RequestException as e: + if return_structured: + return [] + return f"Error fetching insider data from OpenInsider: {e}" + except Exception as e: + if return_structured: + return [] + return f"Error processing insider screener: {e}" + + +def get_finviz_insider_buying( + transaction_type: str = "buy", + lookback_days: int = 7, + min_value: int = 25000, + top_n: int = 20, +) -> str: + """Alias for get_insider_buying_screener to match registry naming convention""" + return get_insider_buying_screener( + transaction_type=transaction_type, + lookback_days=lookback_days, + min_value=min_value, + top_n=top_n, + ) diff --git a/tradingagents/dataflows/fmp_api.py b/tradingagents/dataflows/fmp_api.py index 028364d1..89a0e3bd 100644 --- a/tradingagents/dataflows/fmp_api.py +++ b/tradingagents/dataflows/fmp_api.py @@ -3,11 +3,14 @@ Yahoo Finance API - Short Interest Data using yfinance Identifies potential short squeeze candidates with high short interest """ -import os -import yfinance as yf -from typing import Annotated -import re from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Annotated + +from tradingagents.dataflows.market_data_utils import format_markdown_table, format_market_cap +from tradingagents.dataflows.y_finance import get_ticker_info +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) def get_short_interest( @@ -37,33 +40,70 @@ def get_short_interest( # In a production system, this would come from a screener API watchlist = [ # Meme stocks & high short interest candidates - "GME", "AMC", "BBBY", "BYND", "CLOV", "WISH", "PLTR", "SPCE", + "GME", + "AMC", + "BBBY", + "BYND", + "CLOV", + "WISH", + "PLTR", + "SPCE", # EV & Tech - "RIVN", "LCID", "NIO", "TSLA", "NKLA", "PLUG", "FCEL", + "RIVN", + "LCID", + "NIO", + "TSLA", + "NKLA", + "PLUG", + "FCEL", # Biotech (often heavily shorted) - "SAVA", "NVAX", "MRNA", "BNTX", "VXRT", "SESN", "OCGN", + "SAVA", + "NVAX", + "MRNA", + "BNTX", + "VXRT", + "SESN", + "OCGN", # Retail & Consumer - "PTON", "W", "CVNA", "DASH", "UBER", "LYFT", + "PTON", + "W", + "CVNA", + "DASH", + "UBER", + "LYFT", # Finance & REITs - "SOFI", "HOOD", "COIN", "SQ", "AFRM", + "SOFI", + "HOOD", + "COIN", + "SQ", + "AFRM", # Small caps with squeeze potential - "APRN", "ATER", "BBIG", "CEI", "PROG", "SNDL", + "APRN", + "ATER", + "BBIG", + "CEI", + "PROG", + "SNDL", # Others - "TDOC", "ZM", "PTON", "NFLX", "SNAP", "PINS", + "TDOC", + "ZM", + "PTON", + "NFLX", + "SNAP", + "PINS", ] - print(f" Checking short interest for {len(watchlist)} tickers...") + logger.info(f"Checking short interest for {len(watchlist)} tickers...") high_si_candidates = [] # Use threading to speed up API calls def fetch_short_data(ticker): try: - stock = yf.Ticker(ticker) - info = stock.info + info = get_ticker_info(ticker) # Get short interest data - short_pct = info.get('shortPercentOfFloat', info.get('sharesPercentSharesOut', 0)) + short_pct = info.get("shortPercentOfFloat", info.get("sharesPercentSharesOut", 0)) if short_pct and isinstance(short_pct, (int, float)): short_pct = short_pct * 100 # Convert to percentage else: @@ -72,9 +112,9 @@ def get_short_interest( # Only include if meets criteria if short_pct >= min_short_interest_pct: # Get other data - price = info.get('currentPrice', info.get('regularMarketPrice', 0)) - market_cap = info.get('marketCap', 0) - volume = info.get('volume', info.get('regularMarketVolume', 0)) + price = info.get("currentPrice", info.get("regularMarketPrice", 0)) + market_cap = info.get("marketCap", 0) + volume = info.get("volume", info.get("regularMarketVolume", 0)) # Categorize squeeze potential if short_pct >= 30: @@ -111,34 +151,40 @@ def get_short_interest( # Sort by short interest percentage (highest first) sorted_candidates = sorted( - high_si_candidates, - key=lambda x: x["short_interest_pct"], - reverse=True + high_si_candidates, key=lambda x: x["short_interest_pct"], reverse=True )[:top_n] # Format output - report = f"# High Short Interest Stocks (Yahoo Finance Data)\n\n" + report = "# High Short Interest Stocks (Yahoo Finance Data)\n\n" report += f"**Criteria**: Short Interest >{min_short_interest_pct}%\n" - report += f"**Data Source**: Yahoo Finance via yfinance\n" + report += "**Data Source**: Yahoo Finance via yfinance\n" report += f"**Checked**: {len(watchlist)} tickers from watchlist\n\n" report += f"**Found**: {len(sorted_candidates)} stocks with high short interest\n\n" + report += f"**Found**: {len(sorted_candidates)} stocks with high short interest\n\n" report += "## Potential Short Squeeze Candidates\n\n" - report += "| Ticker | Price | Market Cap | Volume | Short % | Signal |\n" - report += "|--------|-------|------------|--------|---------|--------|\n" + headers = ["Ticker", "Price", "Market Cap", "Volume", "Short %", "Signal"] + rows = [] for candidate in sorted_candidates: - market_cap_str = format_market_cap(candidate['market_cap']) - report += f"| {candidate['ticker']} | " - report += f"${candidate['price']:.2f} | " - report += f"{market_cap_str} | " - report += f"{candidate['volume']:,} | " - report += f"{candidate['short_interest_pct']:.1f}% | " - report += f"{candidate['signal']} |\n" + rows.append( + [ + candidate["ticker"], + f"${candidate['price']:.2f}", + format_market_cap(candidate["market_cap"]), + f"{candidate['volume']:,}", + f"{candidate['short_interest_pct']:.1f}%", + candidate["signal"], + ] + ) + + report += format_markdown_table(headers, rows) report += "\n\n## Signal Definitions\n\n" report += "- **extreme_squeeze_risk**: Short interest >30% - Very high squeeze potential\n" report += "- **high_squeeze_potential**: Short interest 20-30% - High squeeze risk\n" - report += "- **moderate_squeeze_potential**: Short interest 15-20% - Moderate squeeze risk\n" + report += ( + "- **moderate_squeeze_potential**: Short interest 15-20% - Moderate squeeze risk\n" + ) report += "- **low_squeeze_potential**: Short interest 10-15% - Lower squeeze risk\n\n" report += "**Note**: High short interest alone doesn't guarantee a squeeze. Look for positive catalysts.\n" report += "**Limitation**: This checks a curated watchlist. For comprehensive scanning, use a stock screener with short interest filters.\n" @@ -149,41 +195,6 @@ def get_short_interest( return f"Unexpected error in short interest detection: {str(e)}" -def parse_market_cap(market_cap_text: str) -> float: - """Parse market cap from Finviz format (e.g., '1.23B', '456M').""" - if not market_cap_text or market_cap_text == '-': - return 0.0 - - market_cap_text = market_cap_text.upper().strip() - - # Extract number and multiplier - match = re.match(r'([0-9.]+)([BMK])?', market_cap_text) - if not match: - return 0.0 - - number = float(match.group(1)) - multiplier = match.group(2) - - if multiplier == 'B': - return number * 1_000_000_000 - elif multiplier == 'M': - return number * 1_000_000 - elif multiplier == 'K': - return number * 1_000 - else: - return number - - -def format_market_cap(market_cap: float) -> str: - """Format market cap for display.""" - if market_cap >= 1_000_000_000: - return f"${market_cap / 1_000_000_000:.2f}B" - elif market_cap >= 1_000_000: - return f"${market_cap / 1_000_000:.2f}M" - else: - return f"${market_cap:,.0f}" - - def get_fmp_short_interest( min_short_interest_pct: float = 10.0, min_days_to_cover: float = 2.0, diff --git a/tradingagents/dataflows/google.py b/tradingagents/dataflows/google.py index 975b9f2f..8157758a 100644 --- a/tradingagents/dataflows/google.py +++ b/tradingagents/dataflows/google.py @@ -1,6 +1,8 @@ -from typing import Annotated from datetime import datetime +from typing import Annotated + from dateutil.relativedelta import relativedelta + from .googlenews_utils import getNewsData @@ -32,7 +34,9 @@ def get_google_news( start_dt = datetime.strptime(curr_date, "%Y-%m-%d") before = (start_dt - relativedelta(days=look_back_days)).strftime("%Y-%m-%d") else: - raise ValueError("Must provide either (start_date, end_date) or (curr_date, look_back_days)") + raise ValueError( + "Must provide either (start_date, end_date) or (curr_date, look_back_days)" + ) news_results = getNewsData(search_query, before, target_date) @@ -40,7 +44,9 @@ def get_google_news( for news in news_results: news_str += ( - f"### {news['title']} (source: {news['source']}) \n\n{news['snippet']}\n\n" + f"### {news['title']} (source: {news['source']}, date: {news['date']})\n" + f"Link: {news['link']}\n" + f"Snippet: {news['snippet']}\n\n" ) if len(news_results) == 0: @@ -49,24 +55,18 @@ def get_google_news( return f"## {search_query} Google News, from {before} to {target_date}:\n\n{news_str}" -def get_global_news_google( - date: str, - look_back_days: int = 3, - limit: int = 5 -) -> str: +def get_global_news_google(date: str, look_back_days: int = 3, limit: int = 5) -> str: """Retrieve global market news using Google News. - + Args: date: Date for news, yyyy-mm-dd look_back_days: Days to look back limit: Max number of articles (not strictly enforced by underlying function but good for interface) - + Returns: Global news report """ # Query for general market topics return get_google_news( - query="financial markets macroeconomics", - curr_date=date, - look_back_days=look_back_days - ) \ No newline at end of file + query="financial markets macroeconomics", curr_date=date, look_back_days=look_back_days + ) diff --git a/tradingagents/dataflows/googlenews_utils.py b/tradingagents/dataflows/googlenews_utils.py index bdc6124d..a311eebd 100644 --- a/tradingagents/dataflows/googlenews_utils.py +++ b/tradingagents/dataflows/googlenews_utils.py @@ -1,17 +1,20 @@ -import json +import random +import time +from datetime import datetime + import requests from bs4 import BeautifulSoup -from datetime import datetime -import time -import random from tenacity import ( retry, + retry_if_result, stop_after_attempt, wait_exponential, - retry_if_exception_type, - retry_if_result, ) +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + def is_rate_limited(response): """Check if the response indicates rate limiting (status code 429)""" @@ -88,7 +91,7 @@ def getNewsData(query, start_date, end_date): } ) except Exception as e: - print(f"Error processing result: {e}") + logger.error(f"Error processing result: {e}") # If one of the fields is not found, skip this result continue @@ -102,7 +105,7 @@ def getNewsData(query, start_date, end_date): page += 1 except Exception as e: - print(f"Failed after multiple retries: {e}") + logger.error(f"Failed after multiple retries: {e}") break return news_results diff --git a/tradingagents/dataflows/interface.py b/tradingagents/dataflows/interface.py index 108631fd..38083638 100644 --- a/tradingagents/dataflows/interface.py +++ b/tradingagents/dataflows/interface.py @@ -1,26 +1,4 @@ -from typing import Annotated - # Import from vendor-specific modules -from .local import get_YFin_data, get_finnhub_news, get_finnhub_company_insider_sentiment, get_finnhub_company_insider_transactions, get_simfin_balance_sheet, get_simfin_cashflow, get_simfin_income_statements, get_reddit_global_news, get_reddit_company_news -from .y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_technical_analysis, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions, validate_ticker as validate_ticker_yfinance -from .google import get_google_news, get_global_news_google -from .openai import get_stock_news_openai, get_global_news_openai, get_fundamentals_openai -from .alpha_vantage import ( - get_stock as get_alpha_vantage_stock, - get_top_gainers_losers as get_alpha_vantage_movers, - get_indicator as get_alpha_vantage_indicator, - get_fundamentals as get_alpha_vantage_fundamentals, - get_balance_sheet as get_alpha_vantage_balance_sheet, - get_cashflow as get_alpha_vantage_cashflow, - get_income_statement as get_alpha_vantage_income_statement, - get_insider_transactions as get_alpha_vantage_insider_transactions, - get_news as get_alpha_vantage_news, - get_global_news as get_alpha_vantage_global_news -) -from .alpha_vantage_common import AlphaVantageRateLimitError -from .reddit_api import get_reddit_news, get_reddit_global_news as get_reddit_api_global_news, get_reddit_trending_tickers, get_reddit_discussions -from .finnhub_api import get_recommendation_trends as get_finnhub_recommendation_trends -from .twitter_data import get_tweets as get_twitter_tweets, get_tweets_from_user as get_twitter_user_tweets # ============================================================================ # LEGACY COMPATIBILITY LAYER @@ -29,6 +7,7 @@ from .twitter_data import get_tweets as get_twitter_tweets, get_tweets_from_user # All new code should use tradingagents.tools.executor.execute_tool() directly. # ============================================================================ + def route_to_vendor(method: str, *args, **kwargs): """Route method calls to appropriate vendor implementation with fallback support. @@ -40,4 +19,4 @@ def route_to_vendor(method: str, *args, **kwargs): from tradingagents.tools.executor import execute_tool # Delegate to new system - return execute_tool(method, *args, **kwargs) \ No newline at end of file + return execute_tool(method, *args, **kwargs) diff --git a/tradingagents/dataflows/local.py b/tradingagents/dataflows/local.py index 502bc43a..9b3ac144 100644 --- a/tradingagents/dataflows/local.py +++ b/tradingagents/dataflows/local.py @@ -1,13 +1,20 @@ -from typing import Annotated -import pandas as pd -import os -from .config import DATA_DIR -from datetime import datetime -from dateutil.relativedelta import relativedelta import json -from .reddit_utils import fetch_top_from_category +import os +from datetime import datetime +from typing import Annotated + +import pandas as pd +from dateutil.relativedelta import relativedelta from tqdm import tqdm +from .config import DATA_DIR +from .reddit_utils import fetch_top_from_category + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + def get_YFin_data_window( symbol: Annotated[str, "ticker symbol of the company"], curr_date: Annotated[str, "Start date in yyyy-mm-dd format"], @@ -30,9 +37,7 @@ def get_YFin_data_window( data["DateOnly"] = data["Date"].str[:10] # Filter data between the start and end dates (inclusive) - filtered_data = data[ - (data["DateOnly"] >= start_date) & (data["DateOnly"] <= curr_date) - ] + filtered_data = data[(data["DateOnly"] >= start_date) & (data["DateOnly"] <= curr_date)] # Drop the temporary column we created filtered_data = filtered_data.drop("DateOnly", axis=1) @@ -43,10 +48,8 @@ def get_YFin_data_window( ): df_string = filtered_data.to_string() - return ( - f"## Raw Market Data for {symbol} from {start_date} to {curr_date}:\n\n" - + df_string - ) + return f"## Raw Market Data for {symbol} from {start_date} to {curr_date}:\n\n" + df_string + def get_YFin_data( symbol: Annotated[str, "ticker symbol of the company"], @@ -70,9 +73,7 @@ def get_YFin_data( data["DateOnly"] = data["Date"].str[:10] # Filter data between the start and end dates (inclusive) - filtered_data = data[ - (data["DateOnly"] >= start_date) & (data["DateOnly"] <= end_date) - ] + filtered_data = data[(data["DateOnly"] >= start_date) & (data["DateOnly"] <= end_date)] # Drop the temporary column we created filtered_data = filtered_data.drop("DateOnly", axis=1) @@ -82,6 +83,7 @@ def get_YFin_data( return filtered_data + def get_finnhub_news( query: Annotated[str, "Search query or ticker symbol"], start_date: Annotated[str, "Start date in yyyy-mm-dd format"], @@ -109,9 +111,7 @@ def get_finnhub_news( if len(data) == 0: continue for entry in data: - current_news = ( - "### " + entry["headline"] + f" ({day})" + "\n" + entry["summary"] - ) + current_news = "### " + entry["headline"] + f" ({day})" + "\n" + entry["summary"] combined_result += current_news + "\n\n" return f"## {query} News, from {start_date} to {end_date}:\n" + str(combined_result) @@ -191,6 +191,7 @@ def get_finnhub_company_insider_transactions( + "The change field reflects the variation in share count—here a negative number indicates a reduction in holdings—while share specifies the total number of shares involved. The transactionPrice denotes the per-share price at which the trade was executed, and transactionDate marks when the transaction occurred. The name field identifies the insider making the trade, and transactionCode (e.g., S for sale) clarifies the nature of the transaction. FilingDate records when the transaction was officially reported, and the unique id links to the specific SEC filing, as indicated by the source. Additionally, the symbol ties the transaction to a particular company, isDerivative flags whether the trade involves derivative securities, and currency notes the currency context of the transaction." ) + def get_data_in_range(ticker, start_date, end_date, data_type, data_dir, period=None): """ Gets finnhub data saved and processed on disk. @@ -224,6 +225,7 @@ def get_data_in_range(ticker, start_date, end_date, data_type, data_dir, period= filtered_data[key] = value return filtered_data + def get_simfin_balance_sheet( ticker: Annotated[str, "ticker symbol"], freq: Annotated[ @@ -255,7 +257,7 @@ def get_simfin_balance_sheet( # Check if there are any available reports; if not, return a notification if filtered_df.empty: - print("No balance sheet available before the given current date.") + logger.warning("No balance sheet available before the given current date.") return "" # Get the most recent balance sheet by selecting the row with the latest Publish Date @@ -302,7 +304,7 @@ def get_simfin_cashflow( # Check if there are any available reports; if not, return a notification if filtered_df.empty: - print("No cash flow statement available before the given current date.") + logger.warning("No cash flow statement available before the given current date.") return "" # Get the most recent cash flow statement by selecting the row with the latest Publish Date @@ -349,7 +351,7 @@ def get_simfin_income_statements( # Check if there are any available reports; if not, return a notification if filtered_df.empty: - print("No income statement available before the given current date.") + logger.warning("No income statement available before the given current date.") return "" # Get the most recent income statement by selecting the row with the latest Publish Date @@ -472,4 +474,4 @@ def get_reddit_company_news( else: news_str += f"### {post['title']}\n\n{post['content']}\n\n" - return f"##{query} News Reddit, from {start_date} to {end_date}:\n\n{news_str}" \ No newline at end of file + return f"##{query} News Reddit, from {start_date} to {end_date}:\n\n{news_str}" diff --git a/tradingagents/dataflows/market_data_utils.py b/tradingagents/dataflows/market_data_utils.py new file mode 100644 index 00000000..54f2fa0a --- /dev/null +++ b/tradingagents/dataflows/market_data_utils.py @@ -0,0 +1,73 @@ +import re +from typing import Any, List + + +def format_markdown_table(headers: List[str], rows: List[List[Any]]) -> str: + """ + Format a list of rows into a Markdown table. + + Args: + headers: List of column headers + rows: List of rows, where each row is a list of values + + Returns: + Formatted Markdown table string + """ + if not headers: + return "" + + # Create header row + header_str = "| " + " | ".join(headers) + " |\n" + + # Create separator row + separator_str = "| " + " | ".join(["---"] * len(headers)) + " |\n" + + # Create data rows + body_str = "" + for row in rows: + # Convert all values to string and handle None + formatted_row = [str(val) if val is not None else "" for val in row] + body_str += "| " + " | ".join(formatted_row) + " |\n" + + return header_str + separator_str + body_str + + +def parse_market_cap(market_cap_text: str) -> float: + """Parse market cap from string format (e.g., '1.23B', '456M').""" + if not market_cap_text or market_cap_text == "-": + return 0.0 + + market_cap_text = str(market_cap_text).upper().strip() + + # Extract number and multiplier + match = re.match(r"([0-9.]+)([BMK])?", market_cap_text) + if not match: + try: + return float(market_cap_text) + except ValueError: + return 0.0 + + number = float(match.group(1)) + multiplier = match.group(2) + + if multiplier == "B": + return number * 1_000_000_000 + elif multiplier == "M": + return number * 1_000_000 + elif multiplier == "K": + return number * 1_000 + else: + return number + + +def format_market_cap(market_cap: float) -> str: + """Format market cap for display (e.g. 1.5B, 200M).""" + if not isinstance(market_cap, (int, float)): + return str(market_cap) + + if market_cap >= 1_000_000_000: + return f"${market_cap / 1_000_000_000:.2f}B" + elif market_cap >= 1_000_000: + return f"${market_cap / 1_000_000:.2f}M" + else: + return f"${market_cap:,.0f}" diff --git a/tradingagents/dataflows/news_semantic_scanner.py b/tradingagents/dataflows/news_semantic_scanner.py new file mode 100644 index 00000000..2620f6d5 --- /dev/null +++ b/tradingagents/dataflows/news_semantic_scanner.py @@ -0,0 +1,960 @@ +""" +News Semantic Scanner +-------------------- +Scans news from multiple sources, summarizes key themes, and enables semantic +matching against ticker descriptions to find relevant investment opportunities. + +Sources: +- OpenAI web search (real-time market news) +- SEC EDGAR filings (regulatory news) +- Google News +- Alpha Vantage news +""" + +import json +import os +from datetime import datetime, timedelta +from typing import Any, Dict, List, Optional, Tuple + +import requests +from dotenv import load_dotenv +from langchain_google_genai import ChatGoogleGenerativeAI +from openai import OpenAI + +from tradingagents.dataflows.discovery.utils import build_llm_log_entry +from tradingagents.schemas import FilingsList, NewsList +from tradingagents.utils.logger import get_logger + +load_dotenv() + +logger = get_logger(__name__) + + +class NewsSemanticScanner: + """Scans and processes news for semantic ticker matching.""" + + def __init__(self, config: Dict[str, Any]): + """ + Initialize news scanner. + + Args: + config: Configuration dict with: + - openai_api_key: OpenAI API key + - news_sources: List of sources to use + - max_news_items: Maximum news items to process + - news_lookback_hours: How far back to look for news (default: 24 hours) + """ + self.config = config + openai_api_key = os.getenv("OPENAI_API_KEY") + if not openai_api_key: + raise ValueError("OPENAI_API_KEY not found in environment") + self.openai_client = OpenAI(api_key=openai_api_key) + self.news_sources = config.get("news_sources", ["openai", "google_news"]) + self.max_news_items = config.get("max_news_items", 20) + self.news_lookback_hours = config.get("news_lookback_hours", 24) + self.log_callback = config.get("log_callback") + + # Calculate time window + self.cutoff_time = datetime.now() - timedelta(hours=self.news_lookback_hours) + + def _emit_log(self, entry: Dict[str, Any]) -> None: + if self.log_callback: + try: + self.log_callback(entry) + except Exception: + pass + + def _log_llm( + self, + step: str, + model: str, + prompt: Any, + output: Any, + error: str = "", + ) -> None: + entry = build_llm_log_entry( + node="semantic_news", + step=step, + model=model, + prompt=prompt, + output=output, + error=error, + ) + self._emit_log(entry) + + def _get_time_phrase(self) -> str: + """Generate human-readable time phrase for queries.""" + if self.news_lookback_hours <= 1: + return "from the last hour" + elif self.news_lookback_hours <= 6: + return f"from the last {self.news_lookback_hours} hours" + elif self.news_lookback_hours <= 24: + return "from today" + elif self.news_lookback_hours <= 48: + return "from the last 2 days" + else: + days = int(self.news_lookback_hours / 24) + return f"from the last {days} days" + + def _deduplicate_news( + self, news_items: List[Dict[str, Any]], similarity_threshold: float = 0.85 + ) -> List[Dict[str, Any]]: + """ + Deduplicate news items using semantic similarity (embeddings + cosine similarity). + + Two-pass approach: + 1. Fast hash-based pass for exact/near-exact duplicates + 2. Embedding-based cosine similarity for semantically similar stories + + Args: + news_items: List of news items from various sources + similarity_threshold: Cosine similarity threshold (0.85 = very similar) + + Returns: + Deduplicated list, keeping highest importance version of each story + """ + import hashlib + import re + + import numpy as np + + if not news_items: + return [] + + def normalize_text(text: str) -> str: + """Normalize text for comparison.""" + if not text: + return "" + text = text.lower() + text = re.sub(r"[^\w\s]", "", text) + text = re.sub(r"\s+", " ", text).strip() + return text + + def get_content_hash(item: Dict[str, Any]) -> str: + """Generate hash from normalized title + summary.""" + title = normalize_text(item.get("title", "")) + summary = normalize_text(item.get("summary", ""))[:100] + content = title + " " + summary + return hashlib.md5(content.encode()).hexdigest() + + def get_news_text(item: Dict[str, Any]) -> str: + """Get combined text for embedding.""" + title = item.get("title", "") + summary = item.get("summary", "") + return f"{title}. {summary}"[:500] # Limit length for efficiency + + def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float: + """Compute cosine similarity between two vectors.""" + norm_a = np.linalg.norm(a) + norm_b = np.linalg.norm(b) + if norm_a == 0 or norm_b == 0: + return 0.0 + return float(np.dot(a, b) / (norm_a * norm_b)) + + # === PASS 1: Hash-based deduplication (fast, exact matches) === + seen_hashes: Dict[str, Dict[str, Any]] = {} + hash_duplicates = 0 + + for item in news_items: + content_hash = get_content_hash(item) + if content_hash not in seen_hashes: + seen_hashes[content_hash] = item + else: + existing = seen_hashes[content_hash] + if (item.get("importance", 0) or 0) > (existing.get("importance", 0) or 0): + seen_hashes[content_hash] = item + hash_duplicates += 1 + + after_hash = list(seen_hashes.values()) + logger.info( + f"Hash dedup: {len(news_items)} → {len(after_hash)} ({hash_duplicates} exact duplicates)" + ) + + # === PASS 2: Embedding-based semantic similarity === + # Only run if we have enough items to justify the cost + if len(after_hash) <= 3: + return after_hash + + try: + # Generate embeddings for all remaining items + texts = [get_news_text(item) for item in after_hash] + + # Use OpenAI embeddings (same as ticker_semantic_db) + response = self.openai_client.embeddings.create( + model="text-embedding-3-small", + input=texts, + ) + embeddings = np.array([e.embedding for e in response.data]) + + # Find semantic duplicates using cosine similarity + unique_indices = [] + semantic_duplicates = 0 + + for i in range(len(after_hash)): + is_duplicate = False + + for j in unique_indices: + sim = cosine_similarity(embeddings[i], embeddings[j]) + if sim >= similarity_threshold: + # This is a semantic duplicate + is_duplicate = True + semantic_duplicates += 1 + + # Keep higher importance version + existing_item = after_hash[j] + new_item = after_hash[i] + if (new_item.get("importance", 0) or 0) > ( + existing_item.get("importance", 0) or 0 + ): + # Replace with higher importance + unique_indices.remove(j) + unique_indices.append(i) + + logger.debug( + f"Semantic duplicate (sim={sim:.2f}): " + f"'{new_item.get('title', '')[:40]}' vs " + f"'{existing_item.get('title', '')[:40]}'" + ) + break + + if not is_duplicate: + unique_indices.append(i) + + final_items = [after_hash[i] for i in unique_indices] + logger.info( + f"Semantic dedup: {len(after_hash)} → {len(final_items)} " + f"({semantic_duplicates} similar stories merged)" + ) + + return final_items + + except Exception as e: + logger.warning(f"Embedding-based dedup failed, using hash-only results: {e}") + return after_hash + + def _filter_by_time(self, news_items: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Filter news items by timestamp to respect lookback window. + + Args: + news_items: List of news items with 'published_at' or 'timestamp' field + + Returns: + Filtered list of news items within time window + """ + filtered = [] + filtered_out_count = 0 + + for item in news_items: + timestamp_str = item.get("published_at") or item.get("timestamp") + title_preview = item.get("title", "")[:60] + + if not timestamp_str: + # No timestamp, keep it (assume recent) + logger.debug(f"No timestamp for '{title_preview}', keeping") + filtered.append(item) + continue + + item_time = self._parse_timestamp(timestamp_str, date_only_end=True) + if not item_time: + # If parsing fails, keep it + logger.debug(f"Parse failed for '{timestamp_str}' on '{title_preview}', keeping") + filtered.append(item) + continue + + if item_time >= self.cutoff_time: + filtered.append(item) + else: + filtered_out_count += 1 + logger.debug( + f"FILTERED OUT: '{title_preview}' | " + f"published_at='{item.get('published_at')}' | " + f"parsed={item_time.strftime('%Y-%m-%d %H:%M')} | " + f"cutoff={self.cutoff_time.strftime('%Y-%m-%d %H:%M')}" + ) + + if filtered_out_count > 0: + logger.info( + f"Time filter removed {filtered_out_count} items with timestamps before cutoff" + ) + + return filtered + + def _parse_timestamp(self, timestamp_str: str, date_only_end: bool) -> Optional[datetime]: + """Parse a timestamp string into a naive datetime, or return None if invalid.""" + try: + # Handle date-only strings + if len(timestamp_str) == 10 and timestamp_str[4] == "-" and timestamp_str[7] == "-": + base_time = datetime.fromisoformat(timestamp_str) + if date_only_end: + return base_time.replace(hour=23, minute=59, second=59) + return base_time + + # Parse ISO timestamp + parsed_time = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00")) + if parsed_time.tzinfo: + parsed_time = parsed_time.astimezone().replace(tzinfo=None) + return parsed_time + except Exception: + return None + + def _publish_date_range( + self, news_items: List[Dict[str, Any]] + ) -> Tuple[Optional[datetime], Optional[datetime]]: + """Get the earliest and latest publish timestamps from a list of news items.""" + min_time = None + max_time = None + for item in news_items: + timestamp_str = item.get("published_at") or item.get("timestamp") + if not timestamp_str: + continue + item_time = self._parse_timestamp(timestamp_str, date_only_end=False) + if not item_time: + continue + if min_time is None or item_time < min_time: + min_time = item_time + if max_time is None or item_time > max_time: + max_time = item_time + return min_time, max_time + + def _build_web_search_prompt(self, query: str = "breaking stock market news today") -> str: + """ + Build unified web search prompt for both OpenAI and Gemini. + + Args: + query: Search query for news + + Returns: + Formatted search prompt string + """ + time_phrase = self._get_time_phrase() + time_query = f"{query} {time_phrase}" + current_datetime = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") + cutoff_datetime = self.cutoff_time.strftime("%Y-%m-%dT%H:%M:%S") + + return f"""Search the web for: {time_query} + +CRITICAL TIME CONSTRAINT: +- Current time: {current_datetime} +- Only include news published AFTER: {cutoff_datetime} +- Skip any articles older than {self.news_lookback_hours} hours + +Find the top {self.max_news_items} most important market-moving news stories from the last {self.news_lookback_hours} hours. + +Prefer company-specific or single-catalyst stories that are likely to impact only one company or a small number of companies. Avoid broad market, index, or macroeconomic headlines unless they have a clear company-specific catalyst. + +Focus on: +- Earnings reports and guidance +- FDA approvals / regulatory decisions +- Mergers, acquisitions, partnerships +- Product launches +- Executive changes +- Legal/regulatory actions +- Analyst upgrades/downgrades + +For each news item, extract: +- title: Headline +- summary: 2-3 sentence summary of key points +- published_at: ISO-8601 timestamp (REQUIRED - convert relative times like "2 hours ago" to full timestamp using current time {current_datetime}) +- companies_mentioned: List of ticker symbols or company names mentioned +- themes: List of key themes (e.g., "earnings beat", "FDA approval", "merger") +- sentiment: one of positive, negative, neutral +- importance: 1-10 score (10 = highly market-moving) +""" + + def _build_openai_input(self, system_text: str, user_text: str) -> str: + """Build Responses API input as a single prompt string.""" + if system_text: + return f"{system_text}\n\n{user_text}" + return user_text + + def _fetch_openai_news( + self, query: str = "breaking stock market news today" + ) -> List[Dict[str, Any]]: + """ + Fetch news using OpenAI's web search capability. + + Args: + query: Search query for news + + Returns: + List of news items with title, summary, published_at, timestamp + """ + try: + # Build search prompt + search_prompt = self._build_web_search_prompt(query) + + # Use OpenAI web search tool for real-time news + response = self.openai_client.responses.parse( + model="gpt-4o", + tools=[{"type": "web_search"}], + input=self._build_openai_input( + "You are a financial news analyst. Search the web for the latest market news " + "and return structured summaries.", + search_prompt, + ), + text_format=NewsList, + ) + + news_list = response.output_parsed + news_items = [item.model_dump() for item in news_list.news] + + self._log_llm( + step="OpenAI web search", + model="gpt-4o", + prompt=search_prompt, + output=news_items, + ) + + # Add metadata + for item in news_items: + item["source"] = "openai_search" + item["timestamp"] = datetime.now().isoformat() + + return news_items[: self.max_news_items] + + except Exception as e: + self._log_llm( + step="OpenAI web search", + model="gpt-4o", + prompt=search_prompt if "search_prompt" in locals() else "", + output="", + error=str(e), + ) + logger.error(f"Error fetching OpenAI news: {e}") + return [] + + def _fetch_google_news(self, query: str = "stock market") -> List[Dict[str, Any]]: + """ + Fetch news from Google News RSS. + + Args: + query: Search query + + Returns: + List of news items + """ + try: + # Use Google News helper + from tradingagents.dataflows.google import get_google_news + + # Convert hours to days (round up) + lookback_days = max(1, int((self.news_lookback_hours + 23) / 24)) + + news_report = get_google_news( + query=query, + curr_date=datetime.now().strftime("%Y-%m-%d"), + look_back_days=lookback_days, + ) + + # Parse the report using LLM to extract structured data + current_datetime = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") + cutoff_datetime = self.cutoff_time.strftime("%Y-%m-%dT%H:%M:%S") + parse_prompt = f"""Parse this news report and extract individual news items. + +CRITICAL TIME CONSTRAINT: +- Current time: {current_datetime} +- Only include news published AFTER: {cutoff_datetime} +- Skip any articles older than {self.news_lookback_hours} hours + +Prefer company-specific or single-catalyst stories that are likely to impact only one company or a small number of companies. Avoid broad market, index, or macroeconomic headlines unless they have a clear company-specific catalyst. If a story is broad or sector-wide without a specific company catalyst, skip it. + +{news_report} + +For each news item, extract: +- title: Headline +- summary: Brief summary +- published_at: ISO-8601 timestamp (REQUIRED - convert relative times like "2 hours ago" to full timestamp using current time {current_datetime}) +- companies_mentioned: Companies or tickers mentioned +- themes: Key themes +- sentiment: one of positive, negative, neutral +- importance: 1-10 score + +Return as JSON array with key "news".""" + response = self.openai_client.responses.parse( + model="gpt-4o-mini", + input=self._build_openai_input( + "Extract news items from this report into structured JSON format.", + parse_prompt, + ), + text_format=NewsList, + ) + + news_list = response.output_parsed + news_items = [item.model_dump() for item in news_list.news] + + self._log_llm( + step="Parse Google News", + model="gpt-4o-mini", + prompt=parse_prompt, + output=news_items, + ) + + # Add metadata + for item in news_items: + item["source"] = "google_news" + item["timestamp"] = datetime.now().isoformat() + + return news_items[: self.max_news_items] + + except Exception as e: + self._log_llm( + step="Parse Google News", + model="gpt-4o-mini", + prompt=parse_prompt if "parse_prompt" in locals() else "", + output="", + error=str(e), + ) + logger.error(f"Error fetching Google News: {e}") + return [] + + def _fetch_sec_filings(self) -> List[Dict[str, Any]]: + """ + Fetch recent SEC filings (8-K, 13D, 13G - market-moving events). + + Returns: + List of filing summaries + """ + try: + # SEC EDGAR API endpoint + # Get recent 8-K filings (material events) + url = "https://www.sec.gov/cgi-bin/browse-edgar" + params = {"action": "getcurrent", "type": "8-K", "output": "atom", "count": 20} + headers = {"User-Agent": "TradingAgents/1.0 (contact@example.com)"} + + response = requests.get(url, params=params, headers=headers, timeout=10) + + if response.status_code != 200: + return [] + + # Parse SEC filings using LLM + # (SEC returns XML/Atom feed, we'll parse with LLM for simplicity) + current_datetime = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") + cutoff_datetime = self.cutoff_time.strftime("%Y-%m-%dT%H:%M:%S") + filings_prompt = f"""Parse these SEC filings and extract the most important ones. + +CRITICAL TIME CONSTRAINT: +- Current time: {current_datetime} +- Only include filings submitted AFTER: {cutoff_datetime} +- Skip any filings older than {self.news_lookback_hours} hours + +Prefer company-specific filings and material events; skip broad market commentary. + +{response.text} # Limit to avoid token limits + +For each important filing, extract: +- title: Company name and filing type +- summary: What the material event is about +- published_at: ISO-8601 timestamp (REQUIRED - extract from filing date/time) +- companies_mentioned: [company name and ticker if available] +- themes: Type of event (e.g., "acquisition", "earnings guidance", "executive change") +- sentiment: one of positive, negative, neutral +- importance: 1-10 score + +Return as JSON array with key "filings".""" + llm_response = self.openai_client.responses.parse( + model="gpt-4o-mini", + input=self._build_openai_input( + "Extract important SEC 8-K filings from this data and summarize the market-moving events.", + filings_prompt, + ), + text_format=FilingsList, + ) + + filings_list = llm_response.output_parsed + filings = [item.model_dump() for item in filings_list.filings] + + self._log_llm( + step="Parse SEC filings", + model="gpt-4o-mini", + prompt=filings_prompt, + output=filings, + ) + + # Add metadata + for filing in filings: + filing["source"] = "sec_edgar" + filing["timestamp"] = datetime.now().isoformat() + + return filings[: self.max_news_items] + + except Exception as e: + self._log_llm( + step="Parse SEC filings", + model="gpt-4o-mini", + prompt=filings_prompt if "filings_prompt" in locals() else "", + output="", + error=str(e), + ) + logger.error(f"Error fetching SEC filings: {e}") + return [] + + def _fetch_alpha_vantage_news( + self, topics: str = "earnings,technology" + ) -> List[Dict[str, Any]]: + """ + Fetch news from Alpha Vantage. + + Args: + topics: News topics to filter + + Returns: + List of news items + """ + try: + from tradingagents.dataflows.alpha_vantage_news import get_alpha_vantage_news_feed + + # Use cutoff time for Alpha Vantage + time_from = self.cutoff_time.strftime("%Y%m%dT%H%M") + + news_report = get_alpha_vantage_news_feed(topics=topics, time_from=time_from, limit=50) + + # Parse with LLM + current_datetime = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") + cutoff_datetime = self.cutoff_time.strftime("%Y-%m-%dT%H:%M:%S") + parse_prompt = f"""Parse this news feed and extract the most important market-moving stories. + +CRITICAL TIME CONSTRAINT: +- Current time: {current_datetime} +- Only include news published AFTER: {cutoff_datetime} +- Skip any articles older than {self.news_lookback_hours} hours + +Prefer company-specific or single-catalyst stories that are likely to impact only one company or a small number of companies. Avoid broad market, index, or macroeconomic headlines unless they have a clear company-specific catalyst. If a story is broad or sector-wide without a specific company catalyst, skip it. + +{news_report} + +For each news item, extract: +- title: Headline +- summary: Key points +- published_at: ISO-8601 timestamp (REQUIRED - extract from the data or convert relative times using current time {current_datetime}) +- companies_mentioned: Tickers/companies mentioned +- themes: Key themes +- sentiment: one of positive, negative, neutral +- importance: 1-10 score (10 = highly market-moving) + +Return as JSON array with key "news".""" + response = self.openai_client.responses.parse( + model="gpt-4o-mini", + input=self._build_openai_input( + "Extract and summarize important market news.", + parse_prompt, + ), + text_format=NewsList, + ) + + news_list = response.output_parsed + news_items = [item.model_dump() for item in news_list.news] + + self._log_llm( + step="Parse Alpha Vantage news", + model="gpt-4o-mini", + prompt=parse_prompt, + output=news_items, + ) + + # Add metadata + for item in news_items: + item["source"] = "alpha_vantage" + item["timestamp"] = datetime.now().isoformat() + + return news_items[: self.max_news_items] + + except Exception as e: + self._log_llm( + step="Parse Alpha Vantage news", + model="gpt-4o-mini", + prompt=parse_prompt if "parse_prompt" in locals() else "", + output="", + error=str(e), + ) + logger.error(f"Error fetching Alpha Vantage news: {e}") + return [] + + def _fetch_gemini_search_news( + self, query: str = "breaking stock market news today" + ) -> List[Dict[str, Any]]: + """ + Fetch news using Google Gemini's native web search (grounding) capability. + + This uses Gemini's built-in web search tool for real-time market news, + which may provide different results than OpenAI's web search. + + Args: + query: Search query for news + + Returns: + List of news items with title, summary, published_at, timestamp + """ + try: + import os + + # Get API key + google_api_key = os.getenv("GOOGLE_API_KEY") + if not google_api_key: + logger.error("GOOGLE_API_KEY not set, skipping Gemini search") + return [] + + # Build search prompt + search_prompt = self._build_web_search_prompt(query) + + # Step 1: Execute web search using Gemini with google_search tool + search_llm = ChatGoogleGenerativeAI( + model="gemini-2.5-flash-lite", # Fast model for search + api_key=google_api_key, + temperature=1.0, # Higher temperature for diverse results + ).bind_tools([{"google_search": {}}]) + + # Execute search + raw_response = search_llm.invoke(search_prompt) + self._log_llm( + step="Gemini search", + model="gemini-2.5-flash-lite", + prompt=search_prompt, + output=raw_response.content if hasattr(raw_response, "content") else raw_response, + ) + + # Step 2: Structure the results using Gemini with JSON schema + structured_llm = ChatGoogleGenerativeAI( + model="gemini-2.5-flash-lite", api_key=google_api_key + ).with_structured_output(NewsList, method="json_schema") + + current_datetime = datetime.now().strftime("%Y-%m-%dT%H:%M:%S") + cutoff_datetime = self.cutoff_time.strftime("%Y-%m-%dT%H:%M:%S") + + structure_prompt = f"""Parse the following web search results into structured news items. + +CRITICAL TIME CONSTRAINT: +- Current time: {current_datetime} +- Only include news published AFTER: {cutoff_datetime} +- Skip any articles older than {self.news_lookback_hours} hours + +For each news item, extract: +- title: Headline +- summary: 2-3 sentence summary of key points +- published_at: ISO-8601 timestamp (REQUIRED - convert "X hours ago" to full timestamp using current time {current_datetime}) +- companies_mentioned: List of ticker symbols or company names +- themes: List of key themes (e.g., "earnings beat", "FDA approval", "merger") +- sentiment: one of positive, negative, neutral +- importance: 1-10 score (10 = highly market-moving) + +Web search results: +{raw_response.content} + +Return as JSON with "news" array.""" + + structured_response = structured_llm.invoke(structure_prompt) + self._log_llm( + step="Gemini search structuring", + model="gemini-2.5-flash-lite", + prompt=structure_prompt, + output=structured_response, + ) + + # Extract news items + news_items = [item.model_dump() for item in structured_response.news] + + # Add metadata + for item in news_items: + item["source"] = "gemini_search" + item["timestamp"] = datetime.now().isoformat() + + return news_items[: self.max_news_items] + + except Exception as e: + self._log_llm( + step="Gemini search", + model="gemini-2.5-flash-lite", + prompt=search_prompt if "search_prompt" in locals() else "", + output="", + error=str(e), + ) + logger.error(f"Error fetching Gemini search news: {e}") + return [] + + def scan_news(self) -> List[Dict[str, Any]]: + """ + Scan news from all enabled sources. + + Returns: + Aggregated list of news items sorted by importance + """ + all_news = [] + + logger.info("Scanning news sources...") + logger.info(f"Time window: {self._get_time_phrase()} (last {self.news_lookback_hours}h)") + logger.info(f"Cutoff: {self.cutoff_time.strftime('%Y-%m-%d %H:%M')}") + + # Fetch from each enabled source + if "openai" in self.news_sources: + logger.info("Fetching OpenAI web search...") + openai_news = self._fetch_openai_news() + all_news.extend(openai_news) + logger.info(f"Found {len(openai_news)} items from OpenAI") + min_date, max_date = self._publish_date_range(openai_news) + if min_date: + logger.debug(f"Min publish date (OpenAI): {min_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Min publish date (OpenAI): N/A") + if max_date: + logger.debug(f"Max publish date (OpenAI): {max_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Max publish date (OpenAI): N/A") + + if "google_news" in self.news_sources: + logger.info("Fetching Google News...") + google_news = self._fetch_google_news() + all_news.extend(google_news) + logger.info(f"Found {len(google_news)} items from Google News") + min_date, max_date = self._publish_date_range(google_news) + if min_date: + logger.debug(f"Min publish date (Google News): {min_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Min publish date (Google News): N/A") + if max_date: + logger.debug(f"Max publish date (Google News): {max_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Max publish date (Google News): N/A") + + if "sec_filings" in self.news_sources: + logger.info("Fetching SEC filings...") + sec_filings = self._fetch_sec_filings() + all_news.extend(sec_filings) + logger.info(f"Found {len(sec_filings)} items from SEC") + min_date, max_date = self._publish_date_range(sec_filings) + if min_date: + logger.debug(f"Min publish date (SEC): {min_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Min publish date (SEC): N/A") + if max_date: + logger.debug(f"Max publish date (SEC): {max_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Max publish date (SEC): N/A") + + if "alpha_vantage" in self.news_sources: + logger.info("Fetching Alpha Vantage news...") + av_news = self._fetch_alpha_vantage_news() + all_news.extend(av_news) + logger.info(f"Found {len(av_news)} items from Alpha Vantage") + min_date, max_date = self._publish_date_range(av_news) + if min_date: + logger.debug(f"Min publish date (Alpha Vantage): {min_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Min publish date (Alpha Vantage): N/A") + if max_date: + logger.debug(f"Max publish date (Alpha Vantage): {max_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Max publish date (Alpha Vantage): N/A") + + if "gemini_search" in self.news_sources: + logger.info("Fetching Google Gemini search...") + gemini_news = self._fetch_gemini_search_news() + all_news.extend(gemini_news) + logger.info(f"Found {len(gemini_news)} items from Gemini search") + min_date, max_date = self._publish_date_range(gemini_news) + if min_date: + logger.debug(f"Min publish date (Gemini): {min_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Min publish date (Gemini): N/A") + if max_date: + logger.debug(f"Max publish date (Gemini): {max_date.strftime('%Y-%m-%d %H:%M')}") + else: + logger.debug("Max publish date (Gemini): N/A") + + # Apply time filtering + logger.info(f"Collected {len(all_news)} raw news items") + all_news = self._filter_by_time(all_news) + logger.info(f"After time filtering: {len(all_news)} items") + + # Deduplicate news from multiple sources (same story = same hash) + all_news = self._deduplicate_news(all_news) + logger.info(f"After deduplication: {len(all_news)} items") + + # Sort by importance + all_news.sort(key=lambda x: x.get("importance", 0), reverse=True) + + logger.info(f"Total news items collected: {len(all_news)}") + + return all_news[: self.max_news_items] + + def generate_news_summary(self, news_item: Dict[str, Any]) -> str: + """ + Generate a semantic search-optimized summary for a news item. + + Args: + news_item: News item dict + + Returns: + Optimized summary text for embedding/matching + """ + title = news_item.get("title", "") + summary = news_item.get("summary", "") + themes = news_item.get("themes", []) + companies = news_item.get("companies_mentioned", []) + + # Create rich text for semantic matching + search_text = f""" + {title} + + {summary} + + Key themes: {', '.join(themes) if themes else 'General market news'} + Companies mentioned: {', '.join(companies) if companies else 'Broad market'} + """.strip() + + return search_text + + +def main(): + """CLI for testing news scanner.""" + import argparse + + parser = argparse.ArgumentParser(description="Scan news for semantic ticker matching") + parser.add_argument( + "--sources", + nargs="+", + default=["openai"], + choices=["openai", "google_news", "sec_filings", "alpha_vantage", "gemini_search"], + help="News sources to use", + ) + parser.add_argument("--max-items", type=int, default=10, help="Maximum news items to fetch") + parser.add_argument( + "--lookback-hours", + type=int, + default=24, + help="How far back to look for news (in hours). Examples: 1 (last hour), 6 (last 6 hours), 24 (last day), 168 (last week)", + ) + parser.add_argument("--output", type=str, help="Output file for news JSON") + + args = parser.parse_args() + + config = { + "news_sources": args.sources, + "max_news_items": args.max_items, + "news_lookback_hours": args.lookback_hours, + } + + scanner = NewsSemanticScanner(config) + news_items = scanner.scan_news() + + # Display results + logger.info("\n" + "=" * 60) + logger.info(f"Top {min(5, len(news_items))} Most Important News Items:") + logger.info("=" * 60 + "\n") + + for i, item in enumerate(news_items[:5], 1): + logger.info(f"{i}. {item.get('title', 'Untitled')}") + logger.info(f" Source: {item.get('source', 'unknown')}") + logger.info(f" Importance: {item.get('importance', 'N/A')}/10") + logger.info(f" Summary: {item.get('summary', '')[:150]}...") + logger.info(f" Themes: {', '.join(item.get('themes', []))}") + logger.info("") + + # Save to file if specified + if args.output: + with open(args.output, "w") as f: + json.dump(news_items, f, indent=2) + logger.info(f"✅ Saved {len(news_items)} news items to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/tradingagents/dataflows/openai.py b/tradingagents/dataflows/openai.py index 68802893..d287efbf 100644 --- a/tradingagents/dataflows/openai.py +++ b/tradingagents/dataflows/openai.py @@ -1,6 +1,15 @@ import os +import warnings + from openai import OpenAI -from .config import get_config + +from tradingagents.config import config +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + +# Suppress Pydantic serialization warnings from OpenAI web search +warnings.filterwarnings("ignore", category=UserWarning, module="pydantic.main") _OPENAI_CLIENT = None @@ -8,7 +17,7 @@ _OPENAI_CLIENT = None def _get_openai_client() -> OpenAI: global _OPENAI_CLIENT if _OPENAI_CLIENT is None: - _OPENAI_CLIENT = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + _OPENAI_CLIENT = OpenAI(api_key=config.validate_key("openai_api_key", "OpenAI")) return _OPENAI_CLIENT @@ -36,7 +45,7 @@ def get_stock_news_openai(query=None, ticker=None, start_date=None, end_date=Non response = client.responses.create( model="gpt-4o-mini", tools=[{"type": "web_search_preview"}], - input=f"Search Social Media and news sources for {search_query} from {start_date} to {end_date}. Make sure you only get the data posted during that period." + input=f"Search Social Media and news sources for {search_query} from {start_date} to {end_date}. Make sure you only get the data posted during that period.", ) return response.output_text except Exception as e: @@ -50,7 +59,7 @@ def get_global_news_openai(date, look_back_days=7, limit=5): response = client.responses.create( model="gpt-4o-mini", tools=[{"type": "web_search_preview"}], - input=f"Search global or macroeconomics news from {look_back_days} days before {date} that would be informative for trading purposes. Make sure you only get the data posted during that period. Limit the results to {limit} articles." + input=f"Search global or macroeconomics news from {look_back_days} days before {date} that would be informative for trading purposes. Make sure you only get the data posted during that period. Limit the results to {limit} articles.", ) return response.output_text except Exception as e: @@ -64,8 +73,197 @@ def get_fundamentals_openai(ticker, curr_date): response = client.responses.create( model="gpt-4o-mini", tools=[{"type": "web_search_preview"}], - input=f"Search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc" + input=f"Search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc", ) return response.output_text except Exception as e: return f"Error fetching fundamentals from OpenAI: {str(e)}" + + +def get_batch_stock_news_openai( + tickers: list[str], + start_date: str, + end_date: str, + batch_size: int = 10, +) -> dict[str, str]: + """Fetch news for multiple tickers in batched OpenAI calls. + + Instead of making one API call per ticker, this batches tickers together + to significantly reduce API costs (~90% savings for 50 tickers). + + Args: + tickers: List of ticker symbols + start_date: Start date yyyy-mm-dd + end_date: End date yyyy-mm-dd + batch_size: Max tickers per API call (default 10 to avoid output truncation) + + Returns: + dict: {ticker: "news summary text", ...} + """ + from typing import List + + from pydantic import BaseModel + + # Define structured output schema (matching working snippet) + class TickerNews(BaseModel): + ticker: str + news_summary: str + date: str + + class PortfolioUpdate(BaseModel): + items: List[TickerNews] + + from tqdm import tqdm + + client = _get_openai_client() + results = {} + + # Process in batches to avoid output token limits + with tqdm(total=len(tickers), desc="📰 OpenAI batch news", unit="ticker") as pbar: + for i in range(0, len(tickers), batch_size): + batch = tickers[i : i + batch_size] + + # Request comprehensive news summaries for better ranker LLM context + prompt = f"""Find the most significant news stories for {batch} from {start_date} to {end_date}. + +Focus on business catalysts: earnings, product launches, partnerships, analyst changes, regulatory news. + +For each ticker, provide a comprehensive summary (5-8 sentences) covering: +- What happened (the catalyst/event) +- Key numbers/metrics if applicable (revenue, earnings, deal size, etc.) +- Why it matters for investors +- Market reaction or implications +- Any forward-looking statements or guidance""" + + try: + completion = client.responses.parse( + model="gpt-5-nano", + tools=[{"type": "web_search"}], + input=prompt, + text_format=PortfolioUpdate, + ) + + # Extract structured output + if completion.output_parsed: + for item in completion.output_parsed.items: + results[item.ticker.upper()] = item.news_summary + else: + # Fallback if parsing failed + logger.warning(f"Structured parsing returned None for batch: {batch}") + for ticker in batch: + results[ticker.upper()] = "" + + except Exception as e: + logger.error(f"Error fetching batch news for {batch}: {e}") + # On error, set empty string for all tickers in batch + for ticker in batch: + results[ticker.upper()] = "" + + # Update progress bar + pbar.update(len(batch)) + + return results + + +def get_batch_stock_news_google( + tickers: list[str], + start_date: str, + end_date: str, + batch_size: int = 10, + model: str = "gemini-3-flash-preview", +) -> dict[str, str]: + """Fetch news for multiple tickers using Google Search (Gemini). + + Two-step approach: + 1. Use Gemini with google_search tool to gather grounded news + 2. Use structured output to format into JSON + + Args: + tickers: List of ticker symbols + start_date: Start date yyyy-mm-dd + end_date: End date yyyy-mm-dd + batch_size: Max tickers per API call (default 10) + model: Gemini model name (default: gemini-3-flash-preview) + + Returns: + dict: {ticker: "news summary text", ...} + """ + # Create LLMs with specified model (don't use cached version) + from typing import List + + from langchain_google_genai import ChatGoogleGenerativeAI + from pydantic import BaseModel + + google_api_key = os.getenv("GOOGLE_API_KEY") + if not google_api_key: + raise ValueError("GOOGLE_API_KEY not set in environment") + + # Define schema for structured output + class TickerNews(BaseModel): + ticker: str + news_summary: str + date: str + + class PortfolioUpdate(BaseModel): + items: List[TickerNews] + + # Searcher: Enable web search tool + search_llm = ChatGoogleGenerativeAI( + model=model, api_key=google_api_key, temperature=1.0 + ).bind_tools([{"google_search": {}}]) + + # Formatter: Native JSON mode + structured_llm = ChatGoogleGenerativeAI( + model=model, api_key=google_api_key + ).with_structured_output(PortfolioUpdate, method="json_schema") + results = {} + + from tqdm import tqdm + + # Process in batches + with tqdm(total=len(tickers), desc="📰 Google batch news", unit="ticker") as pbar: + for i in range(0, len(tickers), batch_size): + batch = tickers[i : i + batch_size] + + # Request comprehensive news summaries for better ranker LLM context + prompt = f"""Find the most significant news stories for {batch} from {start_date} to {end_date}. + +Focus on business catalysts: earnings, product launches, partnerships, analyst changes, regulatory news. + +For each ticker, provide a comprehensive summary (5-8 sentences) covering: +- What happened (the catalyst/event) +- Key numbers/metrics if applicable (revenue, earnings, deal size, etc.) +- Why it matters for investors +- Market reaction or implications +- Any forward-looking statements or guidance""" + + try: + # Step 1: Perform Google search (grounded response) + raw_news = search_llm.invoke(prompt) + + # Step 2: Structure the grounded results + structured_result = structured_llm.invoke( + f"Using this verified news data: {raw_news.content}\n\n" + f"Format the news for these tickers into the JSON structure: {batch}\n" + f"Include all tickers from the list, even if no news was found." + ) + + # Extract results + if structured_result and hasattr(structured_result, "items"): + for item in structured_result.items: + results[item.ticker.upper()] = item.news_summary + else: + logger.warning(f"Structured output invalid for batch: {batch}") + for ticker in batch: + results[ticker.upper()] = "" + + except Exception as e: + logger.error(f"Error fetching Google batch news for {batch}: {e}") + # On error, set empty string for all tickers in batch + for ticker in batch: + results[ticker.upper()] = "" + + # Update progress bar + pbar.update(len(batch)) + + return results diff --git a/tradingagents/dataflows/reddit_api.py b/tradingagents/dataflows/reddit_api.py index 3ce14f19..9b7c71b8 100644 --- a/tradingagents/dataflows/reddit_api.py +++ b/tradingagents/dataflows/reddit_api.py @@ -1,22 +1,22 @@ -import os -import praw from datetime import datetime, timedelta from typing import Annotated +import praw + +from tradingagents.config import config +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + def get_reddit_client(): """Initialize and return a PRAW Reddit instance.""" - client_id = os.getenv("REDDIT_CLIENT_ID") - client_secret = os.getenv("REDDIT_CLIENT_SECRET") - user_agent = os.getenv("REDDIT_USER_AGENT", "trading_agents_bot/1.0") + client_id = config.validate_key("reddit_client_id", "Reddit Client ID") + client_secret = config.validate_key("reddit_client_secret", "Reddit Client Secret") + user_agent = config.reddit_user_agent - if not client_id or not client_secret: - raise ValueError("REDDIT_CLIENT_ID and REDDIT_CLIENT_SECRET must be set in environment variables.") + return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent) - return praw.Reddit( - client_id=client_id, - client_secret=client_secret, - user_agent=user_agent - ) def get_reddit_news( ticker: Annotated[str, "Ticker symbol"] = None, @@ -33,133 +33,163 @@ def get_reddit_news( try: reddit = get_reddit_client() - + start_dt = datetime.strptime(start_date, "%Y-%m-%d") end_dt = datetime.strptime(end_date, "%Y-%m-%d") # Add one day to end_date to include the full day end_dt = end_dt + timedelta(days=1) - + # Subreddits to search subreddits = "stocks+investing+wallstreetbets+stockmarket" - + # Search queries - try multiple variations queries = [ target_query, f"${target_query}", # Common format on WSB target_query.lower(), ] - + posts = [] seen_ids = set() # Avoid duplicates subreddit = reddit.subreddit(subreddits) - + # Try multiple search strategies for q in queries: # Strategy 1: Search by relevance - for submission in subreddit.search(q, sort='relevance', time_filter='all', limit=50): + for submission in subreddit.search(q, sort="relevance", time_filter="all", limit=50): if submission.id in seen_ids: continue - + post_date = datetime.fromtimestamp(submission.created_utc) - + if start_dt <= post_date <= end_dt: seen_ids.add(submission.id) - + # Fetch top comments for this post - submission.comment_sort = 'top' + submission.comment_sort = "top" submission.comments.replace_more(limit=0) - + top_comments = [] for comment in submission.comments[:5]: # Top 5 comments - if hasattr(comment, 'body') and hasattr(comment, 'score'): - top_comments.append({ - 'body': comment.body[:300] + "..." if len(comment.body) > 300 else comment.body, - 'score': comment.score, - 'author': str(comment.author) if comment.author else '[deleted]' - }) - - posts.append({ - "title": submission.title, - "score": submission.score, - "num_comments": submission.num_comments, - "date": post_date.strftime("%Y-%m-%d"), - "url": submission.url, - "text": submission.selftext[:500] + "..." if len(submission.selftext) > 500 else submission.selftext, - "subreddit": submission.subreddit.display_name, - "top_comments": top_comments - }) - + if hasattr(comment, "body") and hasattr(comment, "score"): + top_comments.append( + { + "body": ( + comment.body[:300] + "..." + if len(comment.body) > 300 + else comment.body + ), + "score": comment.score, + "author": ( + str(comment.author) if comment.author else "[deleted]" + ), + } + ) + + posts.append( + { + "title": submission.title, + "score": submission.score, + "num_comments": submission.num_comments, + "date": post_date.strftime("%Y-%m-%d"), + "url": submission.url, + "text": ( + submission.selftext[:500] + "..." + if len(submission.selftext) > 500 + else submission.selftext + ), + "subreddit": submission.subreddit.display_name, + "top_comments": top_comments, + } + ) + # Strategy 2: Search by new (for recent posts) - for submission in subreddit.search(q, sort='new', time_filter='week', limit=50): + for submission in subreddit.search(q, sort="new", time_filter="week", limit=50): if submission.id in seen_ids: continue - + post_date = datetime.fromtimestamp(submission.created_utc) - + if start_dt <= post_date <= end_dt: seen_ids.add(submission.id) - - submission.comment_sort = 'top' + + submission.comment_sort = "top" submission.comments.replace_more(limit=0) - + top_comments = [] for comment in submission.comments[:5]: - if hasattr(comment, 'body') and hasattr(comment, 'score'): - top_comments.append({ - 'body': comment.body[:300] + "..." if len(comment.body) > 300 else comment.body, - 'score': comment.score, - 'author': str(comment.author) if comment.author else '[deleted]' - }) - - posts.append({ - "title": submission.title, - "score": submission.score, - "num_comments": submission.num_comments, - "date": post_date.strftime("%Y-%m-%d"), - "url": submission.url, - "text": submission.selftext[:500] + "..." if len(submission.selftext) > 500 else submission.selftext, - "subreddit": submission.subreddit.display_name, - "top_comments": top_comments - }) - + if hasattr(comment, "body") and hasattr(comment, "score"): + top_comments.append( + { + "body": ( + comment.body[:300] + "..." + if len(comment.body) > 300 + else comment.body + ), + "score": comment.score, + "author": ( + str(comment.author) if comment.author else "[deleted]" + ), + } + ) + + posts.append( + { + "title": submission.title, + "score": submission.score, + "num_comments": submission.num_comments, + "date": post_date.strftime("%Y-%m-%d"), + "url": submission.url, + "text": ( + submission.selftext[:500] + "..." + if len(submission.selftext) > 500 + else submission.selftext + ), + "subreddit": submission.subreddit.display_name, + "top_comments": top_comments, + } + ) + if not posts: return f"No Reddit posts found for {target_query} between {start_date} and {end_date}." - + # Format output report = f"## Reddit Discussions for {target_query} ({start_date} to {end_date})\n\n" report += f"**Total Posts Found:** {len(posts)}\n\n" - + # Sort by score (popularity) posts.sort(key=lambda x: x["score"], reverse=True) - + # Detailed view of top posts report += "### Top Posts with Community Reactions\n\n" for i, post in enumerate(posts[:10], 1): # Top 10 posts report += f"#### {i}. [{post['subreddit']}] {post['title']}\n" report += f"**Score:** {post['score']} | **Comments:** {post['num_comments']} | **Date:** {post['date']}\n\n" - - if post['text']: + + if post["text"]: report += f"**Post Content:**\n{post['text']}\n\n" - - if post['top_comments']: + + if post["top_comments"]: report += f"**Top Community Reactions ({len(post['top_comments'])} comments):**\n" - for j, comment in enumerate(post['top_comments'], 1): + for j, comment in enumerate(post["top_comments"], 1): report += f"{j}. *[{comment['score']} upvotes]* u/{comment['author']}: {comment['body']}\n" report += "\n" - + report += f"**Link:** {post['url']}\n\n" report += "---\n\n" - + # Summary statistics - total_engagement = sum(p['score'] + p['num_comments'] for p in posts) - avg_score = sum(p['score'] for p in posts) / len(posts) if posts else 0 - + total_engagement = sum(p["score"] + p["num_comments"] for p in posts) + avg_score = sum(p["score"] for p in posts) / len(posts) if posts else 0 + report += "### Summary Statistics\n" report += f"- **Total Posts:** {len(posts)}\n" report += f"- **Average Score:** {avg_score:.1f}\n" report += f"- **Total Engagement:** {total_engagement:,} (upvotes + comments)\n" - report += f"- **Most Active Subreddit:** {max(posts, key=lambda x: x['score'])['subreddit']}\n" - + report += ( + f"- **Most Active Subreddit:** {max(posts, key=lambda x: x['score'])['subreddit']}\n" + ) + return report except Exception as e: @@ -181,43 +211,45 @@ def get_reddit_global_news( try: reddit = get_reddit_client() - + curr_dt = datetime.strptime(target_date, "%Y-%m-%d") start_dt = curr_dt - timedelta(days=look_back_days) - + # Subreddits for global news subreddits = "financenews+finance+economics+stockmarket" - + posts = [] subreddit = reddit.subreddit(subreddits) - + # For global news, we just want top posts from the period # We can use 'top' with time_filter, but 'week' is a fixed window. # Better to iterate top of 'week' and filter by date. - - for submission in subreddit.top(time_filter='week', limit=50): + + for submission in subreddit.top(time_filter="week", limit=50): post_date = datetime.fromtimestamp(submission.created_utc) - + if start_dt <= post_date <= curr_dt + timedelta(days=1): - posts.append({ - "title": submission.title, - "score": submission.score, - "date": post_date.strftime("%Y-%m-%d"), - "subreddit": submission.subreddit.display_name - }) - + posts.append( + { + "title": submission.title, + "score": submission.score, + "date": post_date.strftime("%Y-%m-%d"), + "subreddit": submission.subreddit.display_name, + } + ) + if not posts: return f"No global news found on Reddit for the past {look_back_days} days." - + # Format output report = f"## Global News from Reddit (Last {look_back_days} days)\n\n" - + posts.sort(key=lambda x: x["score"], reverse=True) - + for post in posts[:limit]: report += f"### [{post['subreddit']}] {post['title']} (Score: {post['score']})\n" report += f"**Date:** {post['date']}\n\n" - + return report except Exception as e: @@ -234,58 +266,65 @@ def get_reddit_trending_tickers( """ try: reddit = get_reddit_client() - + # Subreddits to scan subreddits = "wallstreetbets+stocks+investing+stockmarket" subreddit = reddit.subreddit(subreddits) - + posts = [] - + # Scan hot posts - for submission in subreddit.hot(limit=limit * 2): # Fetch more to filter by date + for submission in subreddit.hot(limit=limit * 2): # Fetch more to filter by date # Check date post_date = datetime.fromtimestamp(submission.created_utc) if (datetime.now() - post_date).days > look_back_days: continue - + # Fetch top comments - submission.comment_sort = 'top' + submission.comment_sort = "top" submission.comments.replace_more(limit=0) - + top_comments = [] for comment in submission.comments[:3]: - if hasattr(comment, 'body'): + if hasattr(comment, "body"): top_comments.append(f"- {comment.body[:200]}...") - - posts.append({ - "title": submission.title, - "score": submission.score, - "subreddit": submission.subreddit.display_name, - "text": submission.selftext[:500] + "..." if len(submission.selftext) > 500 else submission.selftext, - "comments": top_comments - }) - + + posts.append( + { + "title": submission.title, + "score": submission.score, + "subreddit": submission.subreddit.display_name, + "text": ( + submission.selftext[:500] + "..." + if len(submission.selftext) > 500 + else submission.selftext + ), + "comments": top_comments, + } + ) + if len(posts) >= limit: break - + if not posts: return "No trending discussions found." - + # Format report for LLM report = "## Trending Reddit Discussions\n\n" for i, post in enumerate(posts, 1): report += f"### {i}. [{post['subreddit']}] {post['title']} (Score: {post['score']})\n" - if post['text']: + if post["text"]: report += f"**Content:** {post['text']}\n" - if post['comments']: - report += "**Top Comments:**\n" + "\n".join(post['comments']) + "\n" + if post["comments"]: + report += "**Top Comments:**\n" + "\n".join(post["comments"]) + "\n" report += "\n---\n" - + return report except Exception as e: return f"Error fetching trending tickers: {str(e)}" + def get_reddit_discussions( symbol: Annotated[str, "Ticker symbol"], from_date: Annotated[str, "Start date in yyyy-mm-dd format"], @@ -302,7 +341,7 @@ def get_reddit_undiscovered_dd( scan_limit: Annotated[int, "Number of new posts to scan"] = 100, top_n: Annotated[int, "Number of top DD posts to return"] = 10, num_comments: Annotated[int, "Number of top comments to include"] = 10, - llm_evaluator = None, # Will be passed from discovery graph + llm_evaluator=None, # Will be passed from discovery graph ) -> str: """ Find high-quality undiscovered DD using LLM evaluation. @@ -345,47 +384,77 @@ def get_reddit_undiscovered_dd( continue # Get top comments for community validation - submission.comment_sort = 'top' + submission.comment_sort = "top" submission.comments.replace_more(limit=0) top_comments = [] for comment in submission.comments[:num_comments]: - if hasattr(comment, 'body') and hasattr(comment, 'score'): - top_comments.append({ - 'body': comment.body[:500], # Include more of each comment - 'score': comment.score, - }) + if hasattr(comment, "body") and hasattr(comment, "score"): + top_comments.append( + { + "body": comment.body[:1000], # Include more of each comment + "score": comment.score, + } + ) - candidate_posts.append({ - "title": submission.title, - "author": str(submission.author) if submission.author else '[deleted]', - "score": submission.score, - "num_comments": submission.num_comments, - "subreddit": submission.subreddit.display_name, - "flair": submission.link_flair_text or "None", - "date": post_date.strftime("%Y-%m-%d %H:%M"), - "url": f"https://reddit.com{submission.permalink}", - "text": submission.selftext[:1500], # First 1500 chars for LLM - "full_length": len(submission.selftext), - "hours_ago": int((datetime.now() - post_date).total_seconds() / 3600), - "top_comments": top_comments, - }) + candidate_posts.append( + { + "title": submission.title, + "author": str(submission.author) if submission.author else "[deleted]", + "score": submission.score, + "num_comments": submission.num_comments, + "subreddit": submission.subreddit.display_name, + "flair": submission.link_flair_text or "None", + "date": post_date.strftime("%Y-%m-%d %H:%M"), + "url": f"https://reddit.com{submission.permalink}", + "text": submission.selftext[:1500], # First 1500 chars for LLM + "full_length": len(submission.selftext), + "hours_ago": int((datetime.now() - post_date).total_seconds() / 3600), + "top_comments": top_comments, + } + ) if not candidate_posts: return f"# Undiscovered DD\n\nNo posts found in last {lookback_hours}h." - print(f" Scanning {len(candidate_posts)} Reddit posts with LLM...") + logger.info(f"Scanning {len(candidate_posts)} Reddit posts with LLM...") # LLM evaluation (parallel) if llm_evaluator: from concurrent.futures import ThreadPoolExecutor, as_completed + from typing import List + from pydantic import BaseModel, Field - from typing import List, Optional # Define structured output schema class DDEvaluation(BaseModel): score: int = Field(description="Quality score 0-100") reason: str = Field(description="Brief reasoning for the score") - tickers: List[str] = Field(default_factory=list, description="List of stock ticker symbols mentioned (empty list if none)") + tickers: List[str] = Field( + default_factory=list, + description="List of stock ticker symbols mentioned (empty list if none)", + ) + + # Configure LLM for Reddit content (adjust safety settings if using Gemini) + try: + # Check if using Google Gemini and configure safety settings + if ( + hasattr(llm_evaluator, "model_name") + and "gemini" in llm_evaluator.model_name.lower() + ): + from langchain_google_genai import HarmBlockThreshold, HarmCategory + + # More permissive safety settings for financial content analysis + llm_evaluator.safety_settings = { + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, + } + logger.info( + "⚙️ Configured Gemini with permissive safety settings for financial content" + ) + except Exception as e: + logger.warning(f"Could not configure safety settings: {e}") # Create structured LLM structured_llm = llm_evaluator.with_structured_output(DDEvaluation) @@ -394,10 +463,12 @@ def get_reddit_undiscovered_dd( try: # Build prompt with comments if available comments_section = "" - if post.get('top_comments') and len(post['top_comments']) > 0: + if post.get("top_comments") and len(post["top_comments"]) > 0: comments_section = "\n\nTop Community Comments (for validation):\n" - for i, comment in enumerate(post['top_comments'], 1): - comments_section += f"{i}. [{comment['score']} upvotes] {comment['body']}\n" + for i, comment in enumerate(post["top_comments"], 1): + comments_section += ( + f"{i}. [{comment['score']} upvotes] {comment['body']}\n" + ) prompt = f"""Evaluate this Reddit post for investment Due Diligence quality. @@ -420,22 +491,34 @@ Extract all stock ticker symbols mentioned in the post or comments.""" result = structured_llm.invoke(prompt) + # Handle None result (Gemini blocked content despite safety settings) + if result is None: + logger.warning(f"⚠️ Content blocked for '{post['title'][:50]}...' - Skipping") + post["quality_score"] = 0 + post["quality_reason"] = ( + "Content blocked by LLM safety filter. " + "Consider using OpenAI/Anthropic for Reddit content." + ) + post["tickers"] = [] + return post + # Extract values from structured response - post['quality_score'] = result.score - post['quality_reason'] = result.reason - post['tickers'] = result.tickers # Now a list + post["quality_score"] = result.score + post["quality_reason"] = result.reason + post["tickers"] = result.tickers # Now a list except Exception as e: - print(f" Error evaluating '{post['title'][:50]}': {str(e)}") - post['quality_score'] = 0 - post['quality_reason'] = f'Error: {str(e)}' - post['tickers'] = [] + logger.error(f"Error evaluating '{post['title'][:50]}': {str(e)}") + post["quality_score"] = 0 + post["quality_reason"] = f"Error: {str(e)}" + post["tickers"] = [] return post # Parallel evaluation with progress tracking try: from tqdm import tqdm + use_tqdm = True except ImportError: use_tqdm = False @@ -446,48 +529,49 @@ Extract all stock ticker symbols mentioned in the post or comments.""" if use_tqdm: # With progress bar evaluated = [] - for future in tqdm(as_completed(futures), total=len(futures), desc=" Evaluating posts"): + for future in tqdm( + as_completed(futures), total=len(futures), desc=" Evaluating posts" + ): evaluated.append(future.result()) else: # Without progress bar (fallback) evaluated = [f.result() for f in as_completed(futures)] # Filter quality threshold (55+ = decent DD) - quality_dd = [p for p in evaluated if p['quality_score'] >= 55] - quality_dd.sort(key=lambda x: x['quality_score'], reverse=True) + quality_dd = [p for p in evaluated if p["quality_score"] >= 55] + quality_dd.sort(key=lambda x: x["quality_score"], reverse=True) # Debug: show score distribution - all_scores = [p['quality_score'] for p in evaluated if p['quality_score'] > 0] + all_scores = [p["quality_score"] for p in evaluated if p["quality_score"] > 0] if all_scores: avg_score = sum(all_scores) / len(all_scores) max_score = max(all_scores) - print(f" Score distribution: avg={avg_score:.1f}, max={max_score}, quality_posts={len(quality_dd)}") + logger.info( + f"Score distribution: avg={avg_score:.1f}, max={max_score}, quality_posts={len(quality_dd)}" + ) top_dd = quality_dd[:top_n] else: # No LLM - sort by length + engagement - candidate_posts.sort( - key=lambda x: x['full_length'] + (x['score'] * 10), - reverse=True - ) + candidate_posts.sort(key=lambda x: x["full_length"] + (x["score"] * 10), reverse=True) top_dd = candidate_posts[:top_n] if not top_dd: return f"# Undiscovered DD\n\nNo high-quality DD found (scanned {len(candidate_posts)} posts)." # Build report - report = f"# 💎 Undiscovered DD (LLM-Filtered Quality)\n\n" + report = "# 💎 Undiscovered DD (LLM-Filtered Quality)\n\n" report += f"**Scanned:** {len(candidate_posts)} posts\n" report += f"**High Quality:** {len(top_dd)} DD posts (score ≥60)\n\n" for i, post in enumerate(top_dd, 1): report += f"## {i}. {post['title']}\n\n" - if 'quality_score' in post: + if "quality_score" in post: report += f"**Quality:** {post['quality_score']}/100 - {post['quality_reason']}\n" - if post.get('tickers') and len(post['tickers']) > 0: - tickers_str = ', '.join([f'${t}' for t in post['tickers']]) + if post.get("tickers") and len(post["tickers"]) > 0: + tickers_str = ", ".join([f"${t}" for t in post["tickers"]]) report += f"**Tickers:** {tickers_str}\n" report += f"**r/{post['subreddit']}** | {post['hours_ago']}h ago | " @@ -500,4 +584,5 @@ Extract all stock ticker symbols mentioned in the post or comments.""" except Exception as e: import traceback + return f"# Undiscovered DD\n\nError: {str(e)}\n{traceback.format_exc()}" diff --git a/tradingagents/dataflows/reddit_utils.py b/tradingagents/dataflows/reddit_utils.py index 2532f0d1..5232d79f 100644 --- a/tradingagents/dataflows/reddit_utils.py +++ b/tradingagents/dataflows/reddit_utils.py @@ -1,11 +1,8 @@ -import requests -import time import json -from datetime import datetime, timedelta -from contextlib import contextmanager -from typing import Annotated import os import re +from datetime import datetime +from typing import Annotated ticker_to_company = { "AAPL": "Apple", @@ -50,9 +47,7 @@ ticker_to_company = { def fetch_top_from_category( - category: Annotated[ - str, "Category to fetch top post from. Collection of subreddits." - ], + category: Annotated[str, "Category to fetch top post from. Collection of subreddits."], date: Annotated[str, "Date to fetch top posts from."], max_limit: Annotated[int, "Maximum number of posts to fetch."], query: Annotated[str, "Optional query to search for in the subreddit."] = None, @@ -70,9 +65,7 @@ def fetch_top_from_category( "REDDIT FETCHING ERROR: max limit is less than the number of files in the category. Will not be able to fetch any posts" ) - limit_per_subreddit = max_limit // len( - os.listdir(os.path.join(base_path, category)) - ) + limit_per_subreddit = max_limit // len(os.listdir(os.path.join(base_path, category))) for data_file in os.listdir(os.path.join(base_path, category)): # check if data_file is a .jsonl file @@ -90,9 +83,9 @@ def fetch_top_from_category( parsed_line = json.loads(line) # select only lines that are from the date - post_date = datetime.utcfromtimestamp( - parsed_line["created_utc"] - ).strftime("%Y-%m-%d") + post_date = datetime.utcfromtimestamp(parsed_line["created_utc"]).strftime( + "%Y-%m-%d" + ) if post_date != date: continue @@ -108,9 +101,9 @@ def fetch_top_from_category( found = False for term in search_terms: - if re.search( - term, parsed_line["title"], re.IGNORECASE - ) or re.search(term, parsed_line["selftext"], re.IGNORECASE): + if re.search(term, parsed_line["title"], re.IGNORECASE) or re.search( + term, parsed_line["selftext"], re.IGNORECASE + ): found = True break diff --git a/tradingagents/dataflows/semantic_discovery.py b/tradingagents/dataflows/semantic_discovery.py new file mode 100644 index 00000000..bae2e4b3 --- /dev/null +++ b/tradingagents/dataflows/semantic_discovery.py @@ -0,0 +1,575 @@ +""" +Semantic Discovery System +------------------------ +Combines news scanning with ticker semantic matching to discover +investment opportunities based on breaking news before they show up +in social media or price action. + +Flow: +1. Scan news from multiple sources +2. Generate embeddings for each news item +3. Match news against ticker descriptions semantically +4. Filter and rank opportunities +5. Return actionable ticker candidates +""" + +import re +from datetime import datetime +from typing import Any, Dict, List + +from dotenv import load_dotenv + +from tradingagents.dataflows.news_semantic_scanner import NewsSemanticScanner +from tradingagents.dataflows.ticker_semantic_db import TickerSemanticDB +from tradingagents.utils.logger import get_logger + +load_dotenv() + +logger = get_logger(__name__) + + +class SemanticDiscovery: + """Discovers investment opportunities through news-ticker semantic matching.""" + + def __init__(self, config: Dict[str, Any]): + """ + Initialize semantic discovery system. + + Args: + config: Configuration dict with settings for both + ticker DB and news scanner + """ + self.config = config + + # Initialize ticker database + self.ticker_db = TickerSemanticDB(config) + + # Initialize news scanner + self.news_scanner = NewsSemanticScanner(config) + + # Discovery settings + self.min_similarity_threshold = config.get("min_similarity_threshold", 0.3) + self.min_news_importance = config.get("min_news_importance", 5) + self.max_tickers_per_news = config.get("max_tickers_per_news", 5) + self.max_total_candidates = config.get("max_total_candidates", 20) + self.news_sentiment_filter = config.get("news_sentiment_filter", "positive") + self.group_by_news = config.get("group_by_news", False) + + def _extract_tickers(self, mentions: List[str]) -> List[str]: + from tradingagents.dataflows.discovery.utils import is_valid_ticker + + tickers = set() + for mention in mentions or []: + for match in re.findall(r"\b[A-Z]{1,5}\b", str(mention)): + # APPLY VALIDATION IMMEDIATELY + if is_valid_ticker(match): + tickers.add(match) + return sorted(tickers) + + def get_directly_mentioned_tickers(self) -> List[Dict[str, Any]]: + """ + Get tickers that are directly mentioned in news (highest signal). + + This extracts tickers from the 'companies_mentioned' field of news items, + which represents explicit company references rather than semantic matches. + + Returns: + List of ticker info dicts with news context + """ + # Scan news if not already done + news_items = self.news_scanner.scan_news() + + # Filter by importance + important_news = [ + item for item in news_items if item.get("importance", 0) >= self.min_news_importance + ] + + # Extract directly mentioned tickers + mentioned_tickers = {} # ticker -> list of news items + + # Common words to exclude (not tickers) + exclude_words = { + "A", + "I", + "AN", + "AI", + "CEO", + "CFO", + "CTO", + "FDA", + "SEC", + "IPO", + "ETF", + "GDP", + "CPI", + "FED", + "NYSE", + "Q1", + "Q2", + "Q3", + "Q4", + "US", + "UK", + "EU", + "AT", + "BE", + "BY", + "DO", + "GO", + "IF", + "IN", + "IS", + "IT", + "ME", + "MY", + "NO", + "OF", + "ON", + "OR", + "SO", + "TO", + "UP", + "WE", + "ALL", + "ARE", + "FOR", + "HAS", + "NEW", + "NOW", + "OLD", + "OUR", + "OUT", + "THE", + "TOP", + "TWO", + "WAS", + "WHO", + "WHY", + "WIN", + "BUY", + "COO", + "EPS", + "P/E", + "ROE", + "ROI", + # Common business abbreviations that aren't tickers + "INC", + "CO", + "LLC", + "LTD", + "CORP", + "PLC", + "AG", + "SA", + "SE", + "NV", + "GAS", + "OIL", + "MGE", + "LG", # Common words/abbreviations from logs + # Single/two-letter words often false positives + "AM", + "AS", + } + + for news_item in important_news: + companies = news_item.get("companies_mentioned", []) + extracted = self._extract_tickers(companies) + + for ticker in extracted: + if ticker in exclude_words: + continue + if len(ticker) < 2: + continue + + if ticker not in mentioned_tickers: + mentioned_tickers[ticker] = [] + + mentioned_tickers[ticker].append( + { + "news_title": news_item.get("title", ""), + "news_summary": news_item.get("summary", ""), + "sentiment": news_item.get("sentiment", "neutral"), + "importance": news_item.get("importance", 5), + "themes": news_item.get("themes", []), + "source": news_item.get("source", "unknown"), + } + ) + + # Convert to list format, prioritizing by news importance + result = [] + for ticker, news_list in mentioned_tickers.items(): + # Use the most important news item as primary + best_news = max(news_list, key=lambda x: x["importance"]) + result.append( + { + "ticker": ticker, + "news_title": best_news["news_title"], + "news_summary": best_news["news_summary"], + "sentiment": best_news["sentiment"], + "importance": best_news["importance"], + "themes": best_news["themes"], + "source": best_news["source"], + "mention_count": len(news_list), + } + ) + + # Sort by importance and mention count + result.sort(key=lambda x: (x["importance"], x["mention_count"]), reverse=True) + + logger.info(f"📌 Found {len(result)} directly mentioned tickers in news") + + return result[: self.max_total_candidates] + + def discover(self) -> List[Dict[str, Any]]: + """ + Run semantic discovery to find ticker opportunities. + + Returns: + List of ticker candidates with news context and relevance scores + """ + logger.info("=" * 60) + logger.info("🚀 SEMANTIC DISCOVERY") + logger.info("=" * 60) + + # Step 1: Scan news + news_items = self.news_scanner.scan_news() + + if not news_items: + logger.info("No news items found.") + return [] + + # Filter news by importance threshold + important_news = [ + item for item in news_items if item.get("importance", 0) >= self.min_news_importance + ] + + logger.info(f"📰 Processing {len(important_news)} high-importance news items...") + logger.info(f"(Filtered from {len(news_items)} total items)") + + if self.news_sentiment_filter: + before_count = len(important_news) + important_news = [ + item + for item in important_news + if item.get("sentiment", "").lower() == self.news_sentiment_filter + ] + logger.info( + f"Sentiment filter: {self.news_sentiment_filter} " + f"({len(important_news)}/{before_count} kept)" + ) + + # Step 2: For each news item, find matching tickers + all_candidates = [] + news_ticker_map = {} # Track which news items match which tickers + news_groups = {} # Track which tickers match each news item + + for i, news_item in enumerate(important_news, 1): + title = news_item.get("title", "Untitled") + logger.info(f"{i}. {title}") + logger.debug(f"Importance: {news_item.get('importance', 0)}/10") + mentioned_tickers = self._extract_tickers(news_item.get("companies_mentioned", [])) + + # Generate search query from news + search_text = self.news_scanner.generate_news_summary(news_item) + + # Search ticker database + matches = self.ticker_db.search_by_text( + query_text=search_text, top_k=self.max_tickers_per_news + ) + + # Filter by similarity threshold + relevant_matches = [ + match + for match in matches + if match["similarity_score"] >= self.min_similarity_threshold + ] + + if relevant_matches: + logger.info(f"Found {len(relevant_matches)} relevant tickers:") + news_key = ( + f"{title}|{news_item.get('source', '')}|" + f"{news_item.get('published_at') or news_item.get('timestamp', '')}" + ) + if news_key not in news_groups: + news_groups[news_key] = { + "news_title": title, + "news_summary": news_item.get("summary", ""), + "news_importance": news_item.get("importance", 0), + "news_themes": news_item.get("themes", []), + "news_sentiment": news_item.get("sentiment"), + "news_source": news_item.get("source"), + "published_at": news_item.get("published_at"), + "timestamp": news_item.get("timestamp"), + "mentioned_tickers": mentioned_tickers, + "tickers": [], + } + for match in relevant_matches: + symbol = match["symbol"] + score = match["similarity_score"] + logger.debug(f"{symbol} (similarity: {score:.3f})") + + # Track news-ticker mapping + if symbol not in news_ticker_map: + news_ticker_map[symbol] = [] + news_ticker_map[symbol].append( + { + "news_title": title, + "news_summary": news_item.get("summary", ""), + "news_importance": news_item.get("importance", 0), + "news_themes": news_item.get("themes", []), + "news_sentiment": news_item.get("sentiment"), + "news_tickers_mentioned": mentioned_tickers, + "similarity_score": score, + "timestamp": news_item.get("timestamp"), + "source": news_item.get("source"), + } + ) + + if symbol not in {t["ticker"] for t in news_groups[news_key]["tickers"]}: + news_groups[news_key]["tickers"].append( + { + "ticker": symbol, + "similarity_score": score, + "ticker_name": match["metadata"]["name"], + "ticker_sector": match["metadata"]["sector"], + "ticker_industry": match["metadata"]["industry"], + } + ) + + # Add to candidates + all_candidates.append( + { + "ticker": symbol, + "ticker_name": match["metadata"]["name"], + "ticker_sector": match["metadata"]["sector"], + "ticker_industry": match["metadata"]["industry"], + "news_title": title, + "news_summary": news_item.get("summary", ""), + "news_importance": news_item.get("importance", 0), + "news_themes": news_item.get("themes", []), + "news_sentiment": news_item.get("sentiment"), + "news_tickers_mentioned": mentioned_tickers, + "similarity_score": score, + "news_source": news_item.get("source"), + "discovery_timestamp": datetime.now().isoformat(), + } + ) + else: + logger.debug("No relevant tickers found (below threshold)") + + if self.group_by_news: + grouped_candidates = [] + for news_entry in news_groups.values(): + tickers = news_entry["tickers"] + if not tickers: + continue + avg_similarity = sum(t["similarity_score"] for t in tickers) / len(tickers) + aggregate_score = ( + (news_entry["news_importance"] * 1.5) + + (avg_similarity * 3.0) + + (len(tickers) * 0.5) + ) + grouped_candidates.append( + { + **news_entry, + "num_tickers": len(tickers), + "avg_similarity": round(avg_similarity, 3), + "aggregate_score": round(aggregate_score, 2), + } + ) + + grouped_candidates.sort(key=lambda x: x["aggregate_score"], reverse=True) + grouped_candidates = grouped_candidates[: self.max_total_candidates] + logger.info("📊 Aggregating and ranking news items...") + logger.info(f"Identified {len(grouped_candidates)} news items with tickers") + return grouped_candidates + + # Step 3: Aggregate and rank candidates + logger.info("📊 Aggregating and ranking candidates...") + + # Group by ticker and calculate aggregate scores + ticker_aggregates = {} + for ticker, news_matches in news_ticker_map.items(): + # Calculate aggregate score + # Factors: number of news matches, importance, similarity + num_matches = len(news_matches) + avg_importance = sum(n["news_importance"] for n in news_matches) / num_matches + avg_similarity = sum(n["similarity_score"] for n in news_matches) / num_matches + max_importance = max(n["news_importance"] for n in news_matches) + + # Weighted score + aggregate_score = ( + (num_matches * 2.0) # More news = higher score + + (avg_importance * 1.5) # Average importance + + (avg_similarity * 3.0) # Similarity strength + + (max_importance * 1.0) # Bonus for having one very important match + ) + + ticker_aggregates[ticker] = { + "ticker": ticker, + "num_news_matches": num_matches, + "avg_importance": round(avg_importance, 2), + "avg_similarity": round(avg_similarity, 3), + "max_importance": max_importance, + "aggregate_score": round(aggregate_score, 2), + "news_matches": news_matches, + } + + # Sort by aggregate score + ranked_candidates = sorted( + ticker_aggregates.values(), key=lambda x: x["aggregate_score"], reverse=True + ) + + # Limit to max candidates + ranked_candidates = ranked_candidates[: self.max_total_candidates] + + logger.info(f"Identified {len(ranked_candidates)} unique ticker candidates") + + return ranked_candidates + + def format_discovery_report(self, candidates: List[Dict[str, Any]]) -> str: + """ + Format discovery results as a readable report. + + Args: + candidates: List of ranked candidates + + Returns: + Formatted text report + """ + if not candidates: + return "No opportunities discovered." + + if "tickers" in candidates[0]: + report = "\n" + "=" * 60 + report += "\n📰 NEWS-DRIVEN RESULTS" + report += "\n" + "=" * 60 + "\n" + + for i, news in enumerate(candidates, 1): + title = news["news_title"] + score = news["aggregate_score"] + num_tickers = news["num_tickers"] + importance = news["news_importance"] + + report += f"\n{i}. {title}" + report += f"\n Score: {score:.2f} | Tickers: {num_tickers} | Importance: {importance}/10" + report += f"\n Source: {news.get('news_source', 'unknown')}" + if news.get("news_themes"): + report += f"\n Themes: {', '.join(news['news_themes'])}" + if news.get("news_summary"): + report += f"\n Summary: {news['news_summary']}" + if news.get("mentioned_tickers"): + report += f"\n Mentioned Tickers: {', '.join(news['mentioned_tickers'])}" + + tickers = sorted(news["tickers"], key=lambda x: x["similarity_score"], reverse=True) + report += "\n Related Tickers:" + for j, ticker_info in enumerate(tickers[:5], 1): + report += ( + f"\n {j}. {ticker_info['ticker']} " + f"(similarity: {ticker_info['similarity_score']:.3f})" + ) + + if len(tickers) > 5: + report += f"\n ... and {len(tickers) - 5} more" + + report += "\n" + + return report + + report = "\n" + "=" * 60 + report += "\n🎯 SEMANTIC DISCOVERY RESULTS" + report += "\n" + "=" * 60 + "\n" + + for i, candidate in enumerate(candidates, 1): + ticker = candidate["ticker"] + score = candidate["aggregate_score"] + num_matches = candidate["num_news_matches"] + avg_importance = candidate["avg_importance"] + + report += f"\n{i}. {ticker}" + report += f"\n Score: {score:.2f} | Matches: {num_matches} | Avg Importance: {avg_importance}/10" + report += "\n Related News:" + + for j, news in enumerate(candidate["news_matches"][:3], 1): # Show top 3 news + report += f"\n {j}. {news['news_title']}" + report += f"\n Similarity: {news['similarity_score']:.3f} | Importance: {news['news_importance']}/10" + if news.get("news_themes"): + report += f"\n Themes: {', '.join(news['news_themes'])}" + + if len(candidate["news_matches"]) > 3: + report += f"\n ... and {len(candidate['news_matches']) - 3} more" + + report += "\n" + + return report + + +def main(): + """CLI for running semantic discovery.""" + import argparse + import json + + parser = argparse.ArgumentParser(description="Run semantic discovery") + parser.add_argument( + "--news-sources", + nargs="+", + default=["openai"], + choices=["openai", "google_news", "sec_filings", "alpha_vantage", "gemini_search"], + help="News sources to use", + ) + parser.add_argument( + "--min-importance", type=int, default=5, help="Minimum news importance (1-10)" + ) + parser.add_argument( + "--min-similarity", type=float, default=0.2, help="Minimum similarity threshold (0-1)" + ) + parser.add_argument( + "--max-candidates", type=int, default=15, help="Maximum ticker candidates to return" + ) + parser.add_argument( + "--lookback-hours", + type=int, + default=24, + help="How far back to look for news (in hours). Examples: 1, 6, 24, 168", + ) + parser.add_argument("--output", type=str, help="Output file for results JSON") + parser.add_argument( + "--group-by-news", action="store_true", help="Group results by news item instead of ticker" + ) + + args = parser.parse_args() + + # Load project config + from tradingagents.default_config import DEFAULT_CONFIG + + config = { + "project_dir": DEFAULT_CONFIG["project_dir"], + "use_openai_embeddings": True, + "news_sources": args.news_sources, + "news_lookback_hours": args.lookback_hours, + "min_news_importance": args.min_importance, + "min_similarity_threshold": args.min_similarity, + "max_tickers_per_news": 5, + "max_total_candidates": args.max_candidates, + "news_sentiment_filter": "positive", + "group_by_news": args.group_by_news, + } + + # Run discovery + discovery = SemanticDiscovery(config) + candidates = discovery.discover() + + # Display report + report = discovery.format_discovery_report(candidates) + logger.info(report) + + # Save to file if specified + if args.output: + with open(args.output, "w") as f: + json.dump(candidates, f, indent=2) + logger.info(f"✅ Saved {len(candidates)} candidates to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/tradingagents/dataflows/stockstats_utils.py b/tradingagents/dataflows/stockstats_utils.py index e81684e0..70bc6386 100644 --- a/tradingagents/dataflows/stockstats_utils.py +++ b/tradingagents/dataflows/stockstats_utils.py @@ -1,9 +1,10 @@ -import pandas as pd -import yfinance as yf -from stockstats import wrap -from typing import Annotated import os -from .config import get_config, DATA_DIR +from typing import Annotated + +import pandas as pd +from stockstats import wrap + +from .config import DATA_DIR, get_config class StockstatsUtils: @@ -13,9 +14,7 @@ class StockstatsUtils: indicator: Annotated[ str, "quantitative indicators based off of the stock data for the company" ], - curr_date: Annotated[ - str, "curr date for retrieving stock price data, YYYY-mm-dd" - ], + curr_date: Annotated[str, "curr date for retrieving stock price data, YYYY-mm-dd"], ): # Get config and set up data directory path config = get_config() @@ -57,7 +56,9 @@ class StockstatsUtils: data = pd.read_csv(data_file) data["Date"] = pd.to_datetime(data["Date"]) else: - data = yf.download( + from .y_finance import download_history + + data = download_history( symbol, start=start_date, end=end_date, diff --git a/tradingagents/dataflows/technical_analyst.py b/tradingagents/dataflows/technical_analyst.py new file mode 100644 index 00000000..5c90c86f --- /dev/null +++ b/tradingagents/dataflows/technical_analyst.py @@ -0,0 +1,476 @@ +from typing import List + +import pandas as pd + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +class TechnicalAnalyst: + """ + Performs comprehensive technical analysis on stock data. + """ + + def __init__(self, df: pd.DataFrame, current_price: float): + """ + Initialize with stock dataframe and current price. + + Args: + df: DataFrame with stock data (must contain 'close', 'high', 'low', 'volume') + current_price: The latest price of the stock + """ + self.df = df + self.current_price = current_price + self.analysis_report = [] + + def add_section(self, title: str, content: List[str]): + """Add a formatted section to the report.""" + self.analysis_report.append(f"## {title}") + self.analysis_report.extend(content) + self.analysis_report.append("") + + def analyze_price_action(self): + """Analyze recent price movements.""" + latest = self.df.iloc[-1] + prev = self.df.iloc[-2] if len(self.df) > 1 else latest + prev_5 = self.df.iloc[-5] if len(self.df) > 5 else latest + + daily_change = ((self.current_price - float(prev["close"])) / float(prev["close"])) * 100 + weekly_change = ( + (self.current_price - float(prev_5["close"])) / float(prev_5["close"]) + ) * 100 + + self.add_section( + "Price Action", + [ + f"- **Daily Change:** {daily_change:+.2f}%", + f"- **5-Day Change:** {weekly_change:+.2f}%", + ], + ) + + def analyze_rsi(self): + """Analyze Relative Strength Index.""" + try: + self.df["rsi"] # Trigger calculation + rsi = float(self.df.iloc[-1]["rsi"]) + rsi_prev = float(self.df.iloc[-5]["rsi"]) if len(self.df) > 5 else rsi + + if rsi > 70: + rsi_signal = "OVERBOUGHT ⚠️" + elif rsi < 30: + rsi_signal = "OVERSOLD ⚡" + elif rsi > 50: + rsi_signal = "Bullish" + else: + rsi_signal = "Bearish" + + rsi_trend = "↑" if rsi > rsi_prev else "↓" + + self.add_section( + "RSI (14)", [f"- **Value:** {rsi:.1f} {rsi_trend}", f"- **Signal:** {rsi_signal}"] + ) + except Exception as e: + logger.warning(f"RSI analysis failed: {e}") + + def analyze_macd(self): + """Analyze MACD.""" + try: + self.df["macd"] + self.df["macds"] + self.df["macdh"] + macd = float(self.df.iloc[-1]["macd"]) + signal = float(self.df.iloc[-1]["macds"]) + histogram = float(self.df.iloc[-1]["macdh"]) + hist_prev = float(self.df.iloc[-2]["macdh"]) if len(self.df) > 1 else histogram + + if macd > signal and histogram > 0: + macd_signal = "BULLISH CROSSOVER ⚡" if histogram > hist_prev else "Bullish" + elif macd < signal and histogram < 0: + macd_signal = "BEARISH CROSSOVER ⚠️" if histogram < hist_prev else "Bearish" + else: + macd_signal = "Neutral" + + momentum = "Strengthening ↑" if abs(histogram) > abs(hist_prev) else "Weakening ↓" + + self.add_section( + "MACD", + [ + f"- **MACD Line:** {macd:.3f}", + f"- **Signal Line:** {signal:.3f}", + f"- **Histogram:** {histogram:.3f} ({momentum})", + f"- **Signal:** {macd_signal}", + ], + ) + except Exception as e: + logger.warning(f"MACD analysis failed: {e}") + + def analyze_moving_averages(self): + """Analyze Moving Averages.""" + try: + self.df["close_50_sma"] + self.df["close_200_sma"] + sma_50 = float(self.df.iloc[-1]["close_50_sma"]) + sma_200 = float(self.df.iloc[-1]["close_200_sma"]) + + # Trend determination + if self.current_price > sma_50 > sma_200: + trend = "STRONG UPTREND ⚡" + elif self.current_price > sma_50: + trend = "Uptrend" + elif self.current_price < sma_50 < sma_200: + trend = "STRONG DOWNTREND ⚠️" + elif self.current_price < sma_50: + trend = "Downtrend" + else: + trend = "Sideways" + + # Golden/Death cross detection + sma_50_prev = float(self.df.iloc[-5]["close_50_sma"]) if len(self.df) > 5 else sma_50 + sma_200_prev = float(self.df.iloc[-5]["close_200_sma"]) if len(self.df) > 5 else sma_200 + + cross = "" + if sma_50 > sma_200 and sma_50_prev < sma_200_prev: + cross = " (GOLDEN CROSS ⚡)" + elif sma_50 < sma_200 and sma_50_prev > sma_200_prev: + cross = " (DEATH CROSS ⚠️)" + + self.add_section( + "Moving Averages", + [ + f"- **50 SMA:** ${sma_50:.2f} ({'+' if self.current_price > sma_50 else ''}{((self.current_price - sma_50) / sma_50 * 100):.1f}% from price)", + f"- **200 SMA:** ${sma_200:.2f} ({'+' if self.current_price > sma_200 else ''}{((self.current_price - sma_200) / sma_200 * 100):.1f}% from price)", + f"- **Trend:** {trend}{cross}", + ], + ) + except Exception as e: + logger.warning(f"Moving averages analysis failed: {e}") + + def analyze_bollinger_bands(self): + """Analyze Bollinger Bands.""" + try: + self.df["boll"] + self.df["boll_ub"] + self.df["boll_lb"] + middle = float(self.df.iloc[-1]["boll"]) + upper = float(self.df.iloc[-1]["boll_ub"]) + lower = float(self.df.iloc[-1]["boll_lb"]) + + band_position = ( + (self.current_price - lower) / (upper - lower) if upper != lower else 0.5 + ) + + if band_position > 0.95: + bb_signal = "AT UPPER BAND - Potential reversal ⚠️" + elif band_position < 0.05: + bb_signal = "AT LOWER BAND - Potential bounce ⚡" + elif band_position > 0.8: + bb_signal = "Near upper band" + elif band_position < 0.2: + bb_signal = "Near lower band" + else: + bb_signal = "Within bands" + + bandwidth = ((upper - lower) / middle) * 100 + + self.add_section( + "Bollinger Bands (20,2)", + [ + f"- **Upper:** ${upper:.2f}", + f"- **Middle:** ${middle:.2f}", + f"- **Lower:** ${lower:.2f}", + f"- **Band Position:** {band_position:.0%}", + f"- **Bandwidth:** {bandwidth:.1f}% (volatility indicator)", + f"- **Signal:** {bb_signal}", + ], + ) + except Exception as e: + logger.warning(f"Bollinger bands analysis failed: {e}") + + def analyze_atr(self): + """Analyze ATR (Volatility).""" + try: + self.df["atr"] + atr = float(self.df.iloc[-1]["atr"]) + atr_pct = (atr / self.current_price) * 100 + + if atr_pct > 5: + vol_level = "HIGH VOLATILITY ⚠️" + elif atr_pct > 2: + vol_level = "Moderate volatility" + else: + vol_level = "Low volatility" + + self.add_section( + "ATR (Volatility)", + [ + f"- **ATR:** ${atr:.2f} ({atr_pct:.1f}% of price)", + f"- **Level:** {vol_level}", + f"- **Suggested Stop-Loss:** ${self.current_price - (1.5 * atr):.2f} (1.5x ATR)", + ], + ) + except Exception as e: + logger.warning(f"ATR analysis failed: {e}") + + def analyze_stochastic(self): + """Analyze Stochastic Oscillator.""" + try: + self.df["kdjk"] + self.df["kdjd"] + stoch_k = float(self.df.iloc[-1]["kdjk"]) + stoch_d = float(self.df.iloc[-1]["kdjd"]) + stoch_k_prev = float(self.df.iloc[-2]["kdjk"]) if len(self.df) > 1 else stoch_k + + if stoch_k > 80 and stoch_d > 80: + stoch_signal = "OVERBOUGHT ⚠️" + elif stoch_k < 20 and stoch_d < 20: + stoch_signal = "OVERSOLD ⚡" + elif stoch_k > stoch_d and stoch_k_prev < stoch_d: + stoch_signal = "Bullish crossover ⚡" + elif stoch_k < stoch_d and stoch_k_prev > stoch_d: + stoch_signal = "Bearish crossover ⚠️" + elif stoch_k > 50: + stoch_signal = "Bullish" + else: + stoch_signal = "Bearish" + + self.add_section( + "Stochastic (14,3,3)", + [ + f"- **%K:** {stoch_k:.1f}", + f"- **%D:** {stoch_d:.1f}", + f"- **Signal:** {stoch_signal}", + ], + ) + except Exception as e: + logger.warning(f"Stochastic analysis failed: {e}") + + def analyze_adx(self): + """Analyze ADX (Trend Strength).""" + try: + self.df["adx"] + adx = float(self.df.iloc[-1]["adx"]) + adx_prev = float(self.df.iloc[-5]["adx"]) if len(self.df) > 5 else adx + + if adx > 50: + trend_strength = "VERY STRONG TREND ⚡" + elif adx > 25: + trend_strength = "Strong trend" + elif adx > 20: + trend_strength = "Trending" + else: + trend_strength = "WEAK/NO TREND (range-bound) ⚠️" + + adx_direction = "Strengthening ↑" if adx > adx_prev else "Weakening ↓" + + self.add_section( + "ADX (Trend Strength)", + [ + f"- **ADX:** {adx:.1f} ({adx_direction})", + f"- **Interpretation:** {trend_strength}", + ], + ) + except Exception as e: + logger.warning(f"ADX analysis failed: {e}") + + def analyze_ema(self): + """Analyze 20 EMA.""" + try: + self.df["close_20_ema"] + ema_20 = float(self.df.iloc[-1]["close_20_ema"]) + + pct_from_ema = ((self.current_price - ema_20) / ema_20) * 100 + if self.current_price > ema_20: + ema_signal = "Price ABOVE 20 EMA (short-term bullish)" + else: + ema_signal = "Price BELOW 20 EMA (short-term bearish)" + + self.add_section( + "20 EMA", + [ + f"- **Value:** ${ema_20:.2f} ({pct_from_ema:+.1f}% from price)", + f"- **Signal:** {ema_signal}", + ], + ) + except Exception as e: + logger.warning(f"EMA analysis failed: {e}") + + def analyze_obv(self): + """Analyze On-Balance Volume.""" + try: + # Check if we have enough data + if len(self.df) < 2: + logger.warning("Insufficient data for OBV analysis (need at least 2 days)") + return + + obv = 0 + obv_values = [0] + for i in range(1, len(self.df)): + if float(self.df.iloc[i]["close"]) > float(self.df.iloc[i - 1]["close"]): + obv += float(self.df.iloc[i]["volume"]) + elif float(self.df.iloc[i]["close"]) < float(self.df.iloc[i - 1]["close"]): + obv -= float(self.df.iloc[i]["volume"]) + obv_values.append(obv) + + current_obv = obv_values[-1] + obv_5_ago = obv_values[-5] if len(obv_values) > 5 else obv_values[0] + + # Check if we have enough data for price comparison + if len(self.df) >= 5: + price_5_ago = float(self.df.iloc[-5]["close"]) + else: + price_5_ago = float(self.df.iloc[0]["close"]) + + if current_obv > obv_5_ago and self.current_price > price_5_ago: + obv_signal = "Confirmed uptrend (price & volume rising)" + elif current_obv < obv_5_ago and self.current_price < price_5_ago: + obv_signal = "Confirmed downtrend (price & volume falling)" + elif current_obv > obv_5_ago and self.current_price < price_5_ago: + obv_signal = "BULLISH DIVERGENCE ⚡ (accumulation)" + elif current_obv < obv_5_ago and self.current_price > price_5_ago: + obv_signal = "BEARISH DIVERGENCE ⚠️ (distribution)" + else: + obv_signal = "Neutral" + + obv_formatted = ( + f"{current_obv/1e6:.1f}M" if abs(current_obv) > 1e6 else f"{current_obv/1e3:.1f}K" + ) + + self.add_section( + "OBV (On-Balance Volume)", + [ + f"- **Value:** {obv_formatted}", + f"- **5-Day Trend:** {'Rising ↑' if current_obv > obv_5_ago else 'Falling ↓'}", + f"- **Signal:** {obv_signal}", + ], + ) + except Exception as e: + logger.warning(f"OBV analysis failed: {e}") + + def analyze_vwap(self): + """Analyze VWAP.""" + try: + # Calculate VWAP for today (simplified - using recent data) + # Calculate cumulative VWAP (last 20 periods approximation) + recent_df = self.df.tail(20) + tp_vol = ((recent_df["high"] + recent_df["low"] + recent_df["close"]) / 3) * recent_df[ + "volume" + ] + vwap = float(tp_vol.sum() / recent_df["volume"].sum()) + + pct_from_vwap = ((self.current_price - vwap) / vwap) * 100 + if self.current_price > vwap: + vwap_signal = "Price ABOVE VWAP (institutional buying)" + else: + vwap_signal = "Price BELOW VWAP (institutional selling)" + + self.add_section( + "VWAP (20-period)", + [ + f"- **VWAP:** ${vwap:.2f}", + f"- **Current vs VWAP:** {pct_from_vwap:+.1f}%", + f"- **Signal:** {vwap_signal}", + ], + ) + except Exception as e: + logger.warning(f"VWAP analysis failed: {e}") + + def analyze_fibonacci(self): + """Analyze Fibonacci Retracement.""" + try: + # Get high and low from last 50 periods + recent_high = float(self.df.tail(50)["high"].max()) + recent_low = float(self.df.tail(50)["low"].min()) + diff = recent_high - recent_low + + fib_levels = { + "0.0% (High)": recent_high, + "23.6%": recent_high - (diff * 0.236), + "38.2%": recent_high - (diff * 0.382), + "50.0%": recent_high - (diff * 0.5), + "61.8%": recent_high - (diff * 0.618), + "78.6%": recent_high - (diff * 0.786), + "100% (Low)": recent_low, + } + + # Find nearest support and resistance + support = None + resistance = None + for level_name, level_price in fib_levels.items(): + if level_price < self.current_price and ( + support is None or level_price > support[1] + ): + support = (level_name, level_price) + if level_price > self.current_price and ( + resistance is None or level_price < resistance[1] + ): + resistance = (level_name, level_price) + + content = [ + f"- **Recent High:** ${recent_high:.2f}", + f"- **Recent Low:** ${recent_low:.2f}", + ] + if resistance: + content.append(f"- **Next Resistance:** ${resistance[1]:.2f} ({resistance[0]})") + if support: + content.append(f"- **Next Support:** ${support[1]:.2f} ({support[0]})") + + self.add_section("Fibonacci Levels (50-period)", content) + + except Exception as e: + logger.warning(f"Fibonacci analysis failed: {e}") + + def generate_summary(self): + """Generate final summary section.""" + signals = [] + try: + rsi = float(self.df.iloc[-1]["rsi"]) + if rsi > 70: + signals.append("RSI overbought") + elif rsi < 30: + signals.append("RSI oversold") + except Exception: + pass + + try: + if self.current_price > float(self.df.iloc[-1]["close_50_sma"]): + signals.append("Above 50 SMA") + else: + signals.append("Below 50 SMA") + except Exception: + pass + + content = [] + if signals: + content.append(f"- **Key Signals:** {', '.join(signals)}") + + self.add_section("Summary", content) + + def generate_report(self, symbol: str, date: str) -> str: + """Run all analyses and generate the markdown report.""" + self.df = self.df.copy() # Avoid modifying original + + # Header + self.analysis_report = [ + f"# Technical Analysis for {symbol.upper()}", + f"**Date:** {date}", + f"**Current Price:** ${self.current_price:.2f}", + "", + ] + + # Run analyses + self.analyze_price_action() + self.analyze_rsi() + self.analyze_macd() + self.analyze_moving_averages() + self.analyze_bollinger_bands() + self.analyze_atr() + self.analyze_stochastic() + self.analyze_adx() + self.analyze_ema() + self.analyze_obv() + self.analyze_vwap() + self.analyze_fibonacci() + self.generate_summary() + + return "\n".join(self.analysis_report) diff --git a/tradingagents/dataflows/ticker_semantic_db.py b/tradingagents/dataflows/ticker_semantic_db.py new file mode 100644 index 00000000..89c68d22 --- /dev/null +++ b/tradingagents/dataflows/ticker_semantic_db.py @@ -0,0 +1,395 @@ +""" +Ticker Semantic Database +------------------------ +Creates and maintains a database of ticker descriptions with embeddings +for semantic matching against news events. + +This enables news-driven discovery by finding tickers semantically related +to breaking news, rather than waiting for social media buzz or price action. +""" + +import json +import os +from datetime import datetime +from typing import Any, Dict, List, Optional + +import chromadb +from dotenv import load_dotenv +from openai import OpenAI +from tqdm import tqdm + +from tradingagents.dataflows.y_finance import get_ticker_info +from tradingagents.utils.logger import get_logger + +# Load environment variables +load_dotenv() + +logger = get_logger(__name__) + + +class TickerSemanticDB: + """Manages ticker descriptions and embeddings for semantic search.""" + + def __init__(self, config: Dict[str, Any]): + """ + Initialize the ticker semantic database. + + Args: + config: Configuration dict with: + - project_dir: Base directory for storage + - use_openai_embeddings: If True, use OpenAI; else use local HF model + - embedding_model: Model name (default: text-embedding-3-small) + """ + self.config = config + self.use_openai = config.get("use_openai_embeddings", True) + + # Setup embedding backend + if self.use_openai: + self.embedding_model = config.get("embedding_model", "text-embedding-3-small") + openai_api_key = os.getenv("OPENAI_API_KEY") + if not openai_api_key: + raise ValueError("OPENAI_API_KEY not found in environment") + self.openai_client = OpenAI(api_key=openai_api_key) + self.embedding_dim = 1536 # OpenAI text-embedding-3-small dimension + else: + # TODO: Add local HuggingFace model support + # Use sentence-transformers with a good MTEB-ranked model + from sentence_transformers import SentenceTransformer + + self.embedding_model = config.get("embedding_model", "BAAI/bge-small-en-v1.5") + self.local_model = SentenceTransformer(self.embedding_model) + self.embedding_dim = self.local_model.get_sentence_embedding_dimension() + + # Setup ChromaDB for persistent storage + project_dir = config.get("project_dir", ".") + embedding_model_safe = self.embedding_model.replace("/", "_").replace(" ", "_") + db_dir = os.path.join(project_dir, "ticker_semantic_db", embedding_model_safe) + os.makedirs(db_dir, exist_ok=True) + + self.chroma_client = chromadb.PersistentClient(path=db_dir) + + # Get or create collection + collection_name = "ticker_descriptions" + try: + self.collection = self.chroma_client.get_collection(name=collection_name) + logger.info(f"Loaded existing ticker database: {self.collection.count()} tickers") + except Exception: + self.collection = self.chroma_client.create_collection( + name=collection_name, + metadata={"description": "Ticker descriptions with metadata for semantic search"}, + ) + logger.info("Created new ticker database collection") + + def get_embedding(self, text: str) -> List[float]: + """Generate embedding for text using configured backend.""" + if self.use_openai: + response = self.openai_client.embeddings.create(model=self.embedding_model, input=text) + return response.data[0].embedding + else: + # Local HuggingFace model + embedding = self.local_model.encode(text, convert_to_numpy=True) + return embedding.tolist() + + def fetch_ticker_info(self, symbol: str) -> Optional[Dict[str, Any]]: + """ + Fetch ticker information from Yahoo Finance. + + Args: + symbol: Stock ticker symbol + + Returns: + Dict with ticker metadata or None if fetch fails + """ + try: + info = get_ticker_info(symbol) + + # Extract relevant fields + description = info.get("longBusinessSummary", "") + if not description: + # Fallback to shorter description if available + description = info.get("description", f"{symbol} - No description available") + + # Build metadata dict + ticker_data = { + "symbol": symbol.upper(), + "name": info.get("longName", info.get("shortName", symbol)), + "description": description, + "industry": info.get("industry", "Unknown"), + "sector": info.get("sector", "Unknown"), + "market_cap": info.get("marketCap", 0), + "revenue": info.get("totalRevenue", 0), + "country": info.get("country", "US"), + "website": info.get("website", ""), + "employees": info.get("fullTimeEmployees", 0), + "last_updated": datetime.now().isoformat(), + } + + return ticker_data + + except Exception as e: + logger.warning(f"Error fetching {symbol}: {e}") + return None + + def add_ticker(self, symbol: str, force_refresh: bool = False) -> bool: + """ + Add a single ticker to the database. + + Args: + symbol: Stock ticker symbol + force_refresh: If True, refresh even if ticker exists + + Returns: + True if added successfully, False otherwise + """ + # Check if already exists + if not force_refresh: + try: + existing = self.collection.get(ids=[symbol.upper()]) + if existing and existing["ids"]: + return True # Already exists + except Exception: + pass + + # Fetch ticker info + ticker_data = self.fetch_ticker_info(symbol) + if not ticker_data: + return False + + # Generate embedding from description + try: + embedding = self.get_embedding(ticker_data["description"]) + except Exception as e: + logger.error(f"Error generating embedding for {symbol}: {e}") + return False + + # Store in ChromaDB + try: + # Store description as document, metadata as metadata, embedding as embedding + self.collection.upsert( + ids=[symbol.upper()], + documents=[ticker_data["description"]], + embeddings=[embedding], + metadatas=[ + { + "symbol": ticker_data["symbol"], + "name": ticker_data["name"], + "industry": ticker_data["industry"], + "sector": ticker_data["sector"], + "market_cap": ticker_data["market_cap"], + "revenue": ticker_data["revenue"], + "country": ticker_data["country"], + "website": ticker_data["website"], + "employees": ticker_data["employees"], + "last_updated": ticker_data["last_updated"], + } + ], + ) + return True + except Exception as e: + logger.error(f"Error storing {symbol}: {e}") + return False + + def build_database( + self, + ticker_file: str, + max_tickers: Optional[int] = None, + skip_existing: bool = True, + batch_size: int = 100, + ): + """ + Build the ticker database from a file. + + Args: + ticker_file: Path to file with ticker symbols (one per line) + max_tickers: Maximum number of tickers to process (None = all) + skip_existing: If True, skip tickers already in DB + batch_size: Number of tickers to process before showing progress + """ + # Read ticker file + with open(ticker_file, "r") as f: + tickers = [line.strip().upper() for line in f if line.strip()] + + if max_tickers: + tickers = tickers[:max_tickers] + + logger.info("Building ticker semantic database...") + logger.info(f"Source: {ticker_file}") + logger.info(f"Total tickers: {len(tickers)}") + logger.info(f"Embedding model: {self.embedding_model}") + + # Get existing tickers if skipping + existing_tickers = set() + if skip_existing: + try: + existing = self.collection.get(include=[]) + existing_tickers = set(existing["ids"]) + logger.info(f"Existing tickers in DB: {len(existing_tickers)}") + except Exception: + pass + + # Process tickers + success_count = 0 + skip_count = 0 + fail_count = 0 + + for i, symbol in enumerate(tqdm(tickers, desc="Processing tickers")): + # Skip if exists + if skip_existing and symbol in existing_tickers: + skip_count += 1 + continue + + # Add ticker + if self.add_ticker(symbol, force_refresh=not skip_existing): + success_count += 1 + else: + fail_count += 1 + + logger.info("Database build complete!") + logger.info(f"Success: {success_count}") + logger.info(f"Skipped: {skip_count}") + logger.info(f"Failed: {fail_count}") + logger.info(f"Total in DB: {self.collection.count()}") + + def search_by_text( + self, query_text: str, top_k: int = 10, filters: Optional[Dict[str, Any]] = None + ) -> List[Dict[str, Any]]: + """ + Search for tickers semantically related to query text. + + Args: + query_text: Text to search for (e.g., news summary) + top_k: Number of top matches to return + filters: Optional metadata filters (e.g., {"sector": "Technology"}) + + Returns: + List of ticker matches with metadata and similarity scores + """ + # Generate embedding for query + query_embedding = self.get_embedding(query_text) + + # Search ChromaDB + results = self.collection.query( + query_embeddings=[query_embedding], + n_results=top_k, + where=filters, # Apply metadata filters if provided + include=["documents", "metadatas", "distances"], + ) + + # Format results + matches = [] + for i in range(len(results["ids"][0])): + distance = results["distances"][0][i] + similarity = 1 / (1 + distance) + match = { + "symbol": results["ids"][0][i], + "description": results["documents"][0][i], + "metadata": results["metadatas"][0][i], + "similarity_score": similarity, # Normalize distance to (0, 1] + } + matches.append(match) + + return matches + + def get_ticker_info(self, symbol: str) -> Optional[Dict[str, Any]]: + """Get stored information for a specific ticker.""" + try: + result = self.collection.get(ids=[symbol.upper()], include=["documents", "metadatas"]) + + if not result["ids"]: + return None + + return { + "symbol": result["ids"][0], + "description": result["documents"][0], + "metadata": result["metadatas"][0], + } + except Exception: + return None + + def get_stats(self) -> Dict[str, Any]: + """Get database statistics.""" + try: + count = self.collection.count() + + # Get sector breakdown + all_data = self.collection.get(include=["metadatas"]) + sectors = {} + industries = {} + + for metadata in all_data["metadatas"]: + sector = metadata.get("sector", "Unknown") + industry = metadata.get("industry", "Unknown") + sectors[sector] = sectors.get(sector, 0) + 1 + industries[industry] = industries.get(industry, 0) + 1 + + return { + "total_tickers": count, + "sectors": sectors, + "industries": industries, + "embedding_model": self.embedding_model, + "embedding_dimension": self.embedding_dim, + } + except Exception as e: + return {"error": str(e)} + + +def main(): + """CLI for building/managing the ticker database.""" + import argparse + + parser = argparse.ArgumentParser(description="Build ticker semantic database") + parser.add_argument("--ticker-file", default="data/tickers.txt", help="Path to ticker file") + parser.add_argument( + "--max-tickers", type=int, default=None, help="Maximum tickers to process (default: all)" + ) + parser.add_argument( + "--use-local", + action="store_true", + help="Use local HuggingFace embeddings instead of OpenAI", + ) + parser.add_argument( + "--force-refresh", action="store_true", help="Refresh all tickers even if they exist" + ) + parser.add_argument("--stats", action="store_true", help="Show database statistics") + parser.add_argument("--search", type=str, help="Search for tickers by text query") + + args = parser.parse_args() + + # Load config + from tradingagents.default_config import DEFAULT_CONFIG + + config = { + "project_dir": DEFAULT_CONFIG["project_dir"], + "use_openai_embeddings": not args.use_local, + } + + # Initialize database + db = TickerSemanticDB(config) + + # Execute command + if args.stats: + stats = db.get_stats() + logger.info("📊 Database Statistics:") + logger.info(json.dumps(stats, indent=2)) + + elif args.search: + logger.info(f"🔍 Searching for: {args.search}") + matches = db.search_by_text(args.search, top_k=10) + logger.info("Top matches:") + for i, match in enumerate(matches, 1): + logger.info(f"{i}. {match['symbol']} - {match['metadata']['name']}") + logger.debug(f" Sector: {match['metadata']['sector']}") + logger.debug(f" Similarity: {match['similarity_score']:.3f}") + logger.debug(f" Description: {match['description'][:150]}...") + + else: + # Build database + db.build_database( + ticker_file=args.ticker_file, + max_tickers=args.max_tickers, + skip_existing=not args.force_refresh, + ) + + +if __name__ == "__main__": + main() diff --git a/tradingagents/dataflows/tradier_api.py b/tradingagents/dataflows/tradier_api.py index 7d284dc1..8f71be9b 100644 --- a/tradingagents/dataflows/tradier_api.py +++ b/tradingagents/dataflows/tradier_api.py @@ -4,10 +4,13 @@ Detects unusual options activity indicating smart money positioning """ import os -import requests -from datetime import datetime from typing import Annotated, List +import requests + +from tradingagents.config import config +from tradingagents.dataflows.market_data_utils import format_markdown_table + def get_unusual_options_activity( tickers: Annotated[List[str], "List of ticker symbols to analyze"] = None, @@ -33,9 +36,10 @@ def get_unusual_options_activity( Returns: Formatted markdown report of unusual options activity """ - api_key = os.getenv("TRADIER_API_KEY") - if not api_key: - return "Error: TRADIER_API_KEY not set in environment variables. Get a free key at https://tradier.com" + try: + api_key = config.validate_key("tradier_api_key", "Tradier") + except ValueError as e: + return f"Error: {str(e)}" if not tickers or len(tickers) == 0: return "Error: No tickers provided. This function analyzes options activity for specific tickers found by other discovery methods." @@ -45,10 +49,7 @@ def get_unusual_options_activity( # Use production: https://api.tradier.com base_url = os.getenv("TRADIER_BASE_URL", "https://sandbox.tradier.com") - headers = { - "Authorization": f"Bearer {api_key}", - "Accept": "application/json" - } + headers = {"Authorization": f"Bearer {api_key}", "Accept": "application/json"} try: # Strategy: Analyze options activity for provided tickers @@ -63,7 +64,7 @@ def get_unusual_options_activity( params = { "symbol": ticker, "expiration": "", # Will get nearest expiration - "greeks": "true" + "greeks": "true", } response = requests.get(options_url, headers=headers, params=params, timeout=10) @@ -96,7 +97,9 @@ def get_unusual_options_activity( total_volume = total_call_volume + total_put_volume if total_volume > 10000: # Significant volume threshold - put_call_ratio = total_put_volume / total_call_volume if total_call_volume > 0 else 0 + put_call_ratio = ( + total_put_volume / total_call_volume if total_call_volume > 0 else 0 + ) # Unusual signals: # - Very low P/C ratio (<0.7) = Bullish (heavy call buying) @@ -111,46 +114,52 @@ def get_unusual_options_activity( elif total_volume > 50000: signal = "high_volume" - unusual_activity.append({ - "ticker": ticker, - "total_volume": total_volume, - "call_volume": total_call_volume, - "put_volume": total_put_volume, - "put_call_ratio": put_call_ratio, - "signal": signal, - "call_oi": total_call_oi, - "put_oi": total_put_oi, - }) + unusual_activity.append( + { + "ticker": ticker, + "total_volume": total_volume, + "call_volume": total_call_volume, + "put_volume": total_put_volume, + "put_call_ratio": put_call_ratio, + "signal": signal, + "call_oi": total_call_oi, + "put_oi": total_put_oi, + } + ) - except Exception as e: + except Exception: # Skip this ticker if there's an error continue # Sort by total volume (highest first) - sorted_activity = sorted( - unusual_activity, - key=lambda x: x["total_volume"], - reverse=True - )[:top_n] + sorted_activity = sorted(unusual_activity, key=lambda x: x["total_volume"], reverse=True)[ + :top_n + ] # Format output if not sorted_activity: return "No unusual options activity detected" report = f"# Unusual Options Activity - {date or 'Latest'}\n\n" - report += f"**Criteria**: P/C Ratio extremes (<0.7 bullish, >1.5 bearish), High volume (>50k)\n\n" + report += ( + "**Criteria**: P/C Ratio extremes (<0.7 bullish, >1.5 bearish), High volume (>50k)\n\n" + ) report += f"**Found**: {len(sorted_activity)} stocks with notable options activity\n\n" report += "## Top Options Activity\n\n" - report += "| Ticker | Total Volume | Call Vol | Put Vol | P/C Ratio | Signal |\n" - report += "|--------|--------------|----------|---------|-----------|--------|\n" - - for activity in sorted_activity: - report += f"| {activity['ticker']} | " - report += f"{activity['total_volume']:,} | " - report += f"{activity['call_volume']:,} | " - report += f"{activity['put_volume']:,} | " - report += f"{activity['put_call_ratio']:.2f} | " - report += f"{activity['signal']} |\n" + report += format_markdown_table( + ["Ticker", "Total Volume", "Call Vol", "Put Vol", "P/C Ratio", "Signal"], + [ + [ + a["ticker"], + f"{a['total_volume']:,}", + f"{a['call_volume']:,}", + f"{a['put_volume']:,}", + f"{a['put_call_ratio']:.2f}", + a["signal"], + ] + for a in sorted_activity + ], + ) report += "\n\n## Signal Definitions\n\n" report += "- **bullish_calls**: P/C ratio <0.7 - Heavy call buying, bullish positioning\n" diff --git a/tradingagents/dataflows/twitter_data.py b/tradingagents/dataflows/twitter_data.py index c026fe66..d9eae3ee 100644 --- a/tradingagents/dataflows/twitter_data.py +++ b/tradingagents/dataflows/twitter_data.py @@ -1,11 +1,16 @@ -import os -import tweepy import json import time from datetime import datetime from pathlib import Path + +import tweepy from dotenv import load_dotenv +from tradingagents.config import config +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + # Load environment variables load_dotenv() @@ -16,10 +21,12 @@ USAGE_FILE = DATA_DIR / ".twitter_usage.json" MONTHLY_LIMIT = 200 CACHE_DURATION_HOURS = 4 + def _ensure_data_dir(): """Ensure the data directory exists.""" DATA_DIR.mkdir(exist_ok=True) + def _load_json(file_path: Path) -> dict: """Load JSON data from a file, returning empty dict if not found.""" if not file_path.exists(): @@ -30,6 +37,7 @@ def _load_json(file_path: Path) -> dict: except (json.JSONDecodeError, IOError): return {} + def _save_json(file_path: Path, data: dict): """Save dictionary to a JSON file.""" _ensure_data_dir() @@ -37,57 +45,62 @@ def _save_json(file_path: Path, data: dict): with open(file_path, "w") as f: json.dump(data, f, indent=2) except IOError as e: - print(f"Warning: Could not save to {file_path}: {e}") + logger.warning(f"Could not save to {file_path}: {e}") + def _get_cache_key(prefix: str, identifier: str) -> str: """Generate a cache key.""" return f"{prefix}:{identifier}" + def _is_cache_valid(timestamp: float) -> bool: """Check if the cached entry is still valid.""" age_hours = (time.time() - timestamp) / 3600 return age_hours < CACHE_DURATION_HOURS + def _check_usage_limit() -> bool: """Check if the monthly usage limit has been reached.""" usage_data = _load_json(USAGE_FILE) current_month = datetime.now().strftime("%Y-%m") - + # Reset usage if it's a new month if usage_data.get("month") != current_month: usage_data = {"month": current_month, "count": 0} _save_json(USAGE_FILE, usage_data) return True - + return usage_data.get("count", 0) < MONTHLY_LIMIT + def _increment_usage(): """Increment the usage counter.""" usage_data = _load_json(USAGE_FILE) current_month = datetime.now().strftime("%Y-%m") - + if usage_data.get("month") != current_month: usage_data = {"month": current_month, "count": 0} - + usage_data["count"] = usage_data.get("count", 0) + 1 _save_json(USAGE_FILE, usage_data) + def get_tweets(query: str, count: int = 10) -> str: """ Fetches recent tweets matching the query using Twitter API v2. Includes caching and rate limiting. - + Args: query (str): The search query (e.g., "AAPL", "Bitcoin"). count (int): Number of tweets to retrieve (default 10). - + Returns: str: A formatted string containing the tweets or an error message. """ # 1. Check Cache cache_key = _get_cache_key("search", query) cache = _load_json(CACHE_FILE) - + if cache_key in cache: entry = cache[cache_key] if _is_cache_valid(entry["timestamp"]): @@ -97,26 +110,23 @@ def get_tweets(query: str, count: int = 10) -> str: if not _check_usage_limit(): return "Error: Monthly Twitter API usage limit (200 calls) reached." - bearer_token = os.getenv("TWITTER_BEARER_TOKEN") - - if not bearer_token: - return "Error: TWITTER_BEARER_TOKEN not found in environment variables." + bearer_token = config.validate_key("twitter_bearer_token", "Twitter") try: client = tweepy.Client(bearer_token=bearer_token) - + # Search for recent tweets safe_count = max(10, min(count, 100)) - + response = client.search_recent_tweets( - query=query, + query=query, max_results=safe_count, - tweet_fields=['created_at', 'author_id', 'public_metrics'] + tweet_fields=["created_at", "author_id", "public_metrics"], ) - + # 3. Increment Usage _increment_usage() - + if not response.data: result = f"No tweets found for query: {query}" else: @@ -130,33 +140,31 @@ def get_tweets(query: str, count: int = 10) -> str: result = formatted_tweets # 4. Save to Cache - cache[cache_key] = { - "timestamp": time.time(), - "data": result - } + cache[cache_key] = {"timestamp": time.time(), "data": result} _save_json(CACHE_FILE, cache) - + return result except Exception as e: return f"Error fetching tweets: {str(e)}" + def get_tweets_from_user(username: str, count: int = 10) -> str: """ Fetches recent tweets from a specific user using Twitter API v2. Includes caching and rate limiting. - + Args: username (str): The Twitter username (without @). count (int): Number of tweets to retrieve (default 10). - + Returns: str: A formatted string containing the tweets or an error message. """ # 1. Check Cache cache_key = _get_cache_key("user", username) cache = _load_json(CACHE_FILE) - + if cache_key in cache: entry = cache[cache_key] if _is_cache_valid(entry["timestamp"]): @@ -166,33 +174,28 @@ def get_tweets_from_user(username: str, count: int = 10) -> str: if not _check_usage_limit(): return "Error: Monthly Twitter API usage limit (200 calls) reached." - bearer_token = os.getenv("TWITTER_BEARER_TOKEN") - - if not bearer_token: - return "Error: TWITTER_BEARER_TOKEN not found in environment variables." + bearer_token = config.validate_key("twitter_bearer_token", "Twitter") try: client = tweepy.Client(bearer_token=bearer_token) - + # First, get the user ID user = client.get_user(username=username) if not user.data: return f"Error: User '@{username}' not found." - + user_id = user.data.id - + # max_results must be between 5 and 100 for get_users_tweets safe_count = max(5, min(count, 100)) - + response = client.get_users_tweets( - id=user_id, - max_results=safe_count, - tweet_fields=['created_at', 'public_metrics'] + id=user_id, max_results=safe_count, tweet_fields=["created_at", "public_metrics"] ) - + # 3. Increment Usage _increment_usage() - + if not response.data: result = f"No recent tweets found for user: @{username}" else: @@ -204,16 +207,12 @@ def get_tweets_from_user(username: str, count: int = 10) -> str: formatted_tweets += f" (Likes: {metrics.get('like_count', 0)}, Retweets: {metrics.get('retweet_count', 0)})\n" formatted_tweets += "\n" result = formatted_tweets - + # 4. Save to Cache - cache[cache_key] = { - "timestamp": time.time(), - "data": result - } + cache[cache_key] = {"timestamp": time.time(), "data": result} _save_json(CACHE_FILE, cache) - + return result except Exception as e: return f"Error fetching tweets from user @{username}: {str(e)}" - diff --git a/tradingagents/dataflows/utils.py b/tradingagents/dataflows/utils.py index 4523de19..0526f8ec 100644 --- a/tradingagents/dataflows/utils.py +++ b/tradingagents/dataflows/utils.py @@ -1,15 +1,19 @@ -import os -import json -import pandas as pd -from datetime import date, timedelta, datetime +from datetime import date, datetime, timedelta from typing import Annotated +import pandas as pd + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + SavePathType = Annotated[str, "File path to save data. If None, data is not saved."] + def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None: if save_path: data.to_csv(save_path) - print(f"{tag} saved to {save_path}") + logger.info(f"{tag} saved to {save_path}") def get_current_date(): diff --git a/tradingagents/dataflows/y_finance.py b/tradingagents/dataflows/y_finance.py index c6730856..de50cceb 100644 --- a/tradingagents/dataflows/y_finance.py +++ b/tradingagents/dataflows/y_finance.py @@ -1,14 +1,83 @@ -from typing import Annotated, List, Optional, Union -from datetime import datetime -from dateutil.relativedelta import relativedelta -import yfinance as yf -import pandas as pd import os -import requests -from concurrent.futures import ThreadPoolExecutor, as_completed +import sys +import warnings +from contextlib import contextmanager +from datetime import datetime, timedelta from pathlib import Path +from typing import Annotated, Any, Dict, List, Optional, Union + +import pandas as pd +import yfinance as yf +from dateutil.relativedelta import relativedelta + +from tradingagents.dataflows.technical_analyst import TechnicalAnalyst +from tradingagents.utils.logger import get_logger + from .stockstats_utils import StockstatsUtils +logger = get_logger(__name__) + + +@contextmanager +def suppress_yfinance_warnings(): + """Suppress yfinance stderr warnings about delisted tickers.""" + with warnings.catch_warnings(): + warnings.filterwarnings("ignore") + # Redirect stderr to devnull temporarily + old_stderr = sys.stderr + sys.stderr = open(os.devnull, "w") + try: + yield + finally: + sys.stderr.close() + sys.stderr = old_stderr + + +def get_ticker_info(symbol: str) -> dict: + """Get ticker info dict with warning suppression. Returns {} on error.""" + with suppress_yfinance_warnings(): + try: + return yf.Ticker(symbol.upper()).info or {} + except Exception: + return {} + + +def download_history(symbol: str, **kwargs) -> pd.DataFrame: + """Download historical data via yf.download() with warning suppression.""" + with suppress_yfinance_warnings(): + try: + return yf.download(symbol.upper(), **kwargs) + except Exception: + return pd.DataFrame() + + +def get_ticker_history(symbol: str, **kwargs) -> pd.DataFrame: + """Get ticker history via Ticker.history() with warning suppression.""" + with suppress_yfinance_warnings(): + try: + return yf.Ticker(symbol.upper()).history(**kwargs) + except Exception: + return pd.DataFrame() + + +def get_ticker_options(symbol: str) -> tuple: + """Get available option expiration dates. Returns () on error.""" + with suppress_yfinance_warnings(): + try: + return yf.Ticker(symbol.upper()).options + except Exception: + return () + + +def get_option_chain(symbol: str, expiration: str): + """Get option chain for a specific expiration. Returns None on error.""" + with suppress_yfinance_warnings(): + try: + return yf.Ticker(symbol.upper()).option_chain(expiration) + except Exception: + return None + + def get_YFin_data_online( symbol: Annotated[str, "ticker symbol of the company"], start_date: Annotated[str, "Start date in yyyy-mm-dd format"], @@ -26,9 +95,7 @@ def get_YFin_data_online( # Check if data is empty if data.empty: - return ( - f"No data found for symbol '{symbol}' between {start_date} and {end_date}" - ) + return f"No data found for symbol '{symbol}' between {start_date} and {end_date}" # Remove timezone info from index for cleaner output if data.index.tz is not None: @@ -50,12 +117,72 @@ def get_YFin_data_online( return header + csv_string + +def get_average_volume( + symbol: Annotated[str, "ticker symbol of the company"], + lookback_days: Annotated[int, "number of trading days to average"] = 20, + curr_date: Annotated[str, "current date (YYYY-mm-dd) for reference"] = None, +) -> dict: + """Get average volume over a recent window for liquidity filtering.""" + try: + if curr_date: + end_dt = datetime.strptime(curr_date, "%Y-%m-%d") + else: + end_dt = datetime.now() + start_dt = end_dt - timedelta(days=lookback_days * 2) + + with suppress_yfinance_warnings(): + data = yf.download( + symbol, + start=start_dt.strftime("%Y-%m-%d"), + end=end_dt.strftime("%Y-%m-%d"), + multi_level_index=False, + progress=False, + auto_adjust=True, + ) + + if data.empty or "Volume" not in data.columns: + return { + "symbol": symbol.upper(), + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "error": "No volume data found", + } + + volume_series = data["Volume"].dropna() + if volume_series.empty: + return { + "symbol": symbol.upper(), + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "error": "No volume data found", + } + + average_volume = float(volume_series.tail(lookback_days).mean()) + latest_volume = float(volume_series.iloc[-1]) + + return { + "symbol": symbol.upper(), + "average_volume": average_volume, + "latest_volume": latest_volume, + "lookback_days": lookback_days, + } + except Exception as e: + return { + "symbol": symbol.upper(), + "average_volume": None, + "latest_volume": None, + "lookback_days": lookback_days, + "error": f"{e}", + } + + def get_stock_stats_indicators_window( symbol: Annotated[str, "ticker symbol of the company"], indicator: Annotated[str, "technical indicator to get the analysis and report of"], - curr_date: Annotated[ - str, "The current trading date you are trading on, YYYY-mm-dd" - ], + curr_date: Annotated[str, "The current trading date you are trading on, YYYY-mm-dd"], look_back_days: Annotated[int, "how many days to look back"], ) -> str: @@ -144,30 +271,30 @@ def get_stock_stats_indicators_window( # Optimized: Get stock data once and calculate indicators for all dates try: indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date) - + # Generate the date range we need current_dt = curr_date_dt date_values = [] - + while current_dt >= before: - date_str = current_dt.strftime('%Y-%m-%d') - + date_str = current_dt.strftime("%Y-%m-%d") + # Look up the indicator value for this date if date_str in indicator_data: indicator_value = indicator_data[date_str] else: indicator_value = "N/A: Not a trading day (weekend or holiday)" - + date_values.append((date_str, indicator_value)) current_dt = current_dt - relativedelta(days=1) - + # Build the result string ind_string = "" for date_str, value in date_values: ind_string += f"{date_str}: {value}\n" - + except Exception as e: - print(f"Error getting bulk stockstats data: {e}") + logger.error(f"Error getting bulk stockstats data: {e}") # Fallback to original implementation if bulk method fails ind_string = "" curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") @@ -191,21 +318,21 @@ def get_stock_stats_indicators_window( def _get_stock_stats_bulk( symbol: Annotated[str, "ticker symbol of the company"], indicator: Annotated[str, "technical indicator to calculate"], - curr_date: Annotated[str, "current date for reference"] + curr_date: Annotated[str, "current date for reference"], ) -> dict: """ Optimized bulk calculation of stock stats indicators. Fetches data once and calculates indicator for all available dates. Returns dict mapping date strings to indicator values. """ - from .config import get_config import pandas as pd from stockstats import wrap - import os - + + from .config import get_config + config = get_config() online = config["data_vendors"]["technical_indicators"] != "local" - + if not online: # Local data path try: @@ -222,19 +349,19 @@ def _get_stock_stats_bulk( # Online data fetching with caching today_date = pd.Timestamp.today() curr_date_dt = pd.to_datetime(curr_date) - + end_date = today_date start_date = today_date - pd.DateOffset(years=2) start_date_str = start_date.strftime("%Y-%m-%d") end_date_str = end_date.strftime("%Y-%m-%d") - + os.makedirs(config["data_cache_dir"], exist_ok=True) - + data_file = os.path.join( config["data_cache_dir"], f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv", ) - + if os.path.exists(data_file): data = pd.read_csv(data_file) data["Date"] = pd.to_datetime(data["Date"]) @@ -249,34 +376,32 @@ def _get_stock_stats_bulk( ) data = data.reset_index() data.to_csv(data_file, index=False) - + df = wrap(data) df["Date"] = df["Date"].dt.strftime("%Y-%m-%d") - + # Calculate the indicator for all rows at once df[indicator] # This triggers stockstats to calculate the indicator - + # Create a dictionary mapping date strings to indicator values result_dict = {} for _, row in df.iterrows(): date_str = row["Date"] indicator_value = row[indicator] - + # Handle NaN/None values if pd.isna(indicator_value): result_dict[date_str] = "N/A" else: result_dict[date_str] = str(indicator_value) - + return result_dict def get_stockstats_indicator( symbol: Annotated[str, "ticker symbol of the company"], indicator: Annotated[str, "technical indicator to get the analysis and report of"], - curr_date: Annotated[ - str, "The current trading date you are trading on, YYYY-mm-dd" - ], + curr_date: Annotated[str, "The current trading date you are trading on, YYYY-mm-dd"], ) -> str: curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") @@ -289,7 +414,7 @@ def get_stockstats_indicator( curr_date, ) except Exception as e: - print( + logger.error( f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}" ) return "" @@ -303,543 +428,177 @@ def get_technical_analysis( ) -> str: """ Get a concise technical analysis summary with key indicators, signals, and trend interpretation. - + Returns analysis-ready output instead of verbose day-by-day data. """ - from .config import get_config from stockstats import wrap - - # Default indicators to analyze - indicators = ["rsi", "stoch", "macd", "adx", "close_20_ema", "close_50_sma", "close_200_sma", "boll", "atr", "obv", "vwap", "fib"] - - # Fetch price data (last 60 days for indicator calculation) + + # Fetch price data (last 200 days for indicator calculation) curr_date_dt = pd.to_datetime(curr_date) - start_date = curr_date_dt - pd.DateOffset(days=200) # Need enough history for 200 SMA - + start_date = curr_date_dt - pd.DateOffset(days=300) # Need enough history for 200 SMA + try: - data = yf.download( - symbol, - start=start_date.strftime("%Y-%m-%d"), - end=curr_date_dt.strftime("%Y-%m-%d"), - multi_level_index=False, - progress=False, - auto_adjust=True, - ) - + with suppress_yfinance_warnings(): + data = yf.download( + symbol, + start=start_date.strftime("%Y-%m-%d"), + end=curr_date_dt.strftime("%Y-%m-%d"), + multi_level_index=False, + progress=False, + auto_adjust=True, + ) + if data.empty: return f"No data found for {symbol}" - + data = data.reset_index() df = wrap(data) - + # Get latest values latest = df.iloc[-1] - prev = df.iloc[-2] if len(df) > 1 else latest - prev_5 = df.iloc[-5] if len(df) > 5 else latest - - current_price = float(latest['close']) - - # Build analysis - analysis = [] - analysis.append(f"# Technical Analysis for {symbol.upper()}") - analysis.append(f"**Date:** {curr_date}") - analysis.append(f"**Current Price:** ${current_price:.2f}") - analysis.append("") - - # Price action summary - daily_change = ((current_price - float(prev['close'])) / float(prev['close'])) * 100 - weekly_change = ((current_price - float(prev_5['close'])) / float(prev_5['close'])) * 100 - analysis.append(f"## Price Action") - analysis.append(f"- **Daily Change:** {daily_change:+.2f}%") - analysis.append(f"- **5-Day Change:** {weekly_change:+.2f}%") - analysis.append("") - - # RSI Analysis - if 'rsi' in indicators: - try: - df['rsi'] # Trigger calculation - rsi = float(df.iloc[-1]['rsi']) - rsi_prev = float(df.iloc[-5]['rsi']) if len(df) > 5 else rsi - - if rsi > 70: - rsi_signal = "OVERBOUGHT ⚠️" - elif rsi < 30: - rsi_signal = "OVERSOLD ⚡" - elif rsi > 50: - rsi_signal = "Bullish" - else: - rsi_signal = "Bearish" - - rsi_trend = "↑" if rsi > rsi_prev else "↓" - analysis.append(f"## RSI (14)") - analysis.append(f"- **Value:** {rsi:.1f} {rsi_trend}") - analysis.append(f"- **Signal:** {rsi_signal}") - analysis.append("") - except Exception as e: - pass - - # MACD Analysis - if 'macd' in indicators: - try: - df['macd'] - df['macds'] - df['macdh'] - macd = float(df.iloc[-1]['macd']) - signal = float(df.iloc[-1]['macds']) - histogram = float(df.iloc[-1]['macdh']) - hist_prev = float(df.iloc[-2]['macdh']) if len(df) > 1 else histogram - - if macd > signal and histogram > 0: - macd_signal = "BULLISH CROSSOVER ⚡" if histogram > hist_prev else "Bullish" - elif macd < signal and histogram < 0: - macd_signal = "BEARISH CROSSOVER ⚠️" if histogram < hist_prev else "Bearish" - else: - macd_signal = "Neutral" - - momentum = "Strengthening ↑" if abs(histogram) > abs(hist_prev) else "Weakening ↓" - analysis.append(f"## MACD") - analysis.append(f"- **MACD Line:** {macd:.3f}") - analysis.append(f"- **Signal Line:** {signal:.3f}") - analysis.append(f"- **Histogram:** {histogram:.3f} ({momentum})") - analysis.append(f"- **Signal:** {macd_signal}") - analysis.append("") - except Exception as e: - pass - - # Moving Averages - if 'close_50_sma' in indicators or 'close_200_sma' in indicators: - try: - df['close_50_sma'] - df['close_200_sma'] - sma_50 = float(df.iloc[-1]['close_50_sma']) - sma_200 = float(df.iloc[-1]['close_200_sma']) - - # Trend determination - if current_price > sma_50 > sma_200: - trend = "STRONG UPTREND ⚡" - elif current_price > sma_50: - trend = "Uptrend" - elif current_price < sma_50 < sma_200: - trend = "STRONG DOWNTREND ⚠️" - elif current_price < sma_50: - trend = "Downtrend" - else: - trend = "Sideways" - - # Golden/Death cross detection - sma_50_prev = float(df.iloc[-5]['close_50_sma']) if len(df) > 5 else sma_50 - sma_200_prev = float(df.iloc[-5]['close_200_sma']) if len(df) > 5 else sma_200 - - cross = "" - if sma_50 > sma_200 and sma_50_prev < sma_200_prev: - cross = " (GOLDEN CROSS ⚡)" - elif sma_50 < sma_200 and sma_50_prev > sma_200_prev: - cross = " (DEATH CROSS ⚠️)" - - analysis.append(f"## Moving Averages") - analysis.append(f"- **50 SMA:** ${sma_50:.2f} ({'+' if current_price > sma_50 else ''}{((current_price - sma_50) / sma_50 * 100):.1f}% from price)") - analysis.append(f"- **200 SMA:** ${sma_200:.2f} ({'+' if current_price > sma_200 else ''}{((current_price - sma_200) / sma_200 * 100):.1f}% from price)") - analysis.append(f"- **Trend:** {trend}{cross}") - analysis.append("") - except Exception as e: - pass - - # Bollinger Bands - if 'boll' in indicators: - try: - df['boll'] - df['boll_ub'] - df['boll_lb'] - middle = float(df.iloc[-1]['boll']) - upper = float(df.iloc[-1]['boll_ub']) - lower = float(df.iloc[-1]['boll_lb']) - - # Position within bands (0 = lower, 1 = upper) - band_position = (current_price - lower) / (upper - lower) if upper != lower else 0.5 - - if band_position > 0.95: - bb_signal = "AT UPPER BAND - Potential reversal ⚠️" - elif band_position < 0.05: - bb_signal = "AT LOWER BAND - Potential bounce ⚡" - elif band_position > 0.8: - bb_signal = "Near upper band" - elif band_position < 0.2: - bb_signal = "Near lower band" - else: - bb_signal = "Within bands" - - bandwidth = ((upper - lower) / middle) * 100 - analysis.append(f"## Bollinger Bands (20,2)") - analysis.append(f"- **Upper:** ${upper:.2f}") - analysis.append(f"- **Middle:** ${middle:.2f}") - analysis.append(f"- **Lower:** ${lower:.2f}") - analysis.append(f"- **Band Position:** {band_position:.0%}") - analysis.append(f"- **Bandwidth:** {bandwidth:.1f}% (volatility indicator)") - analysis.append(f"- **Signal:** {bb_signal}") - analysis.append("") - except Exception as e: - pass - - # ATR (Volatility) - if 'atr' in indicators: - try: - df['atr'] - atr = float(df.iloc[-1]['atr']) - atr_pct = (atr / current_price) * 100 - - if atr_pct > 5: - vol_level = "HIGH VOLATILITY ⚠️" - elif atr_pct > 2: - vol_level = "Moderate volatility" - else: - vol_level = "Low volatility" - - analysis.append(f"## ATR (Volatility)") - analysis.append(f"- **ATR:** ${atr:.2f} ({atr_pct:.1f}% of price)") - analysis.append(f"- **Level:** {vol_level}") - analysis.append(f"- **Suggested Stop-Loss:** ${current_price - (1.5 * atr):.2f} (1.5x ATR)") - analysis.append("") - except Exception as e: - pass - - # Stochastic Oscillator - if 'stoch' in indicators: - try: - df['kdjk'] # Stochastic %K - df['kdjd'] # Stochastic %D - stoch_k = float(df.iloc[-1]['kdjk']) - stoch_d = float(df.iloc[-1]['kdjd']) - stoch_k_prev = float(df.iloc[-2]['kdjk']) if len(df) > 1 else stoch_k - - if stoch_k > 80 and stoch_d > 80: - stoch_signal = "OVERBOUGHT ⚠️" - elif stoch_k < 20 and stoch_d < 20: - stoch_signal = "OVERSOLD ⚡" - elif stoch_k > stoch_d and stoch_k_prev < stoch_d: - stoch_signal = "Bullish crossover ⚡" - elif stoch_k < stoch_d and stoch_k_prev > stoch_d: - stoch_signal = "Bearish crossover ⚠️" - elif stoch_k > 50: - stoch_signal = "Bullish" - else: - stoch_signal = "Bearish" - - analysis.append(f"## Stochastic (14,3,3)") - analysis.append(f"- **%K:** {stoch_k:.1f}") - analysis.append(f"- **%D:** {stoch_d:.1f}") - analysis.append(f"- **Signal:** {stoch_signal}") - analysis.append("") - except Exception as e: - pass - - # ADX (Trend Strength) - if 'adx' in indicators: - try: - df['adx'] - df['dx'] - adx = float(df.iloc[-1]['adx']) - adx_prev = float(df.iloc[-5]['adx']) if len(df) > 5 else adx - - if adx > 50: - trend_strength = "VERY STRONG TREND ⚡" - elif adx > 25: - trend_strength = "Strong trend" - elif adx > 20: - trend_strength = "Trending" - else: - trend_strength = "WEAK/NO TREND (range-bound) ⚠️" - - adx_direction = "Strengthening ↑" if adx > adx_prev else "Weakening ↓" - analysis.append(f"## ADX (Trend Strength)") - analysis.append(f"- **ADX:** {adx:.1f} ({adx_direction})") - analysis.append(f"- **Interpretation:** {trend_strength}") - analysis.append("") - except Exception as e: - pass - - # 20 EMA (Short-term trend) - if 'close_20_ema' in indicators: - try: - df['close_20_ema'] - ema_20 = float(df.iloc[-1]['close_20_ema']) - - pct_from_ema = ((current_price - ema_20) / ema_20) * 100 - if current_price > ema_20: - ema_signal = "Price ABOVE 20 EMA (short-term bullish)" - else: - ema_signal = "Price BELOW 20 EMA (short-term bearish)" - - analysis.append(f"## 20 EMA") - analysis.append(f"- **Value:** ${ema_20:.2f} ({pct_from_ema:+.1f}% from price)") - analysis.append(f"- **Signal:** {ema_signal}") - analysis.append("") - except Exception as e: - pass - - # OBV (On-Balance Volume) - if 'obv' in indicators: - try: - # Calculate OBV manually since stockstats may not have it - obv = 0 - obv_values = [0] - for i in range(1, len(df)): - if float(df.iloc[i]['close']) > float(df.iloc[i-1]['close']): - obv += float(df.iloc[i]['volume']) - elif float(df.iloc[i]['close']) < float(df.iloc[i-1]['close']): - obv -= float(df.iloc[i]['volume']) - obv_values.append(obv) - - current_obv = obv_values[-1] - obv_5_ago = obv_values[-5] if len(obv_values) > 5 else obv_values[0] - - if current_obv > obv_5_ago and current_price > float(df.iloc[-5]['close']): - obv_signal = "Confirmed uptrend (price & volume rising)" - elif current_obv < obv_5_ago and current_price < float(df.iloc[-5]['close']): - obv_signal = "Confirmed downtrend (price & volume falling)" - elif current_obv > obv_5_ago and current_price < float(df.iloc[-5]['close']): - obv_signal = "BULLISH DIVERGENCE ⚡ (accumulation)" - elif current_obv < obv_5_ago and current_price > float(df.iloc[-5]['close']): - obv_signal = "BEARISH DIVERGENCE ⚠️ (distribution)" - else: - obv_signal = "Neutral" - - obv_formatted = f"{current_obv/1e6:.1f}M" if abs(current_obv) > 1e6 else f"{current_obv/1e3:.1f}K" - analysis.append(f"## OBV (On-Balance Volume)") - analysis.append(f"- **Value:** {obv_formatted}") - analysis.append(f"- **5-Day Trend:** {'Rising ↑' if current_obv > obv_5_ago else 'Falling ↓'}") - analysis.append(f"- **Signal:** {obv_signal}") - analysis.append("") - except Exception as e: - pass - - # VWAP (Volume Weighted Average Price) - if 'vwap' in indicators: - try: - # Calculate VWAP for today (simplified - using recent data) - typical_price = (float(df.iloc[-1]['high']) + float(df.iloc[-1]['low']) + float(df.iloc[-1]['close'])) / 3 - - # Calculate cumulative VWAP (last 20 periods approximation) - recent_df = df.tail(20) - tp_vol = ((recent_df['high'] + recent_df['low'] + recent_df['close']) / 3) * recent_df['volume'] - vwap = float(tp_vol.sum() / recent_df['volume'].sum()) - - pct_from_vwap = ((current_price - vwap) / vwap) * 100 - if current_price > vwap: - vwap_signal = "Price ABOVE VWAP (institutional buying)" - else: - vwap_signal = "Price BELOW VWAP (institutional selling)" - - analysis.append(f"## VWAP (20-period)") - analysis.append(f"- **VWAP:** ${vwap:.2f}") - analysis.append(f"- **Current vs VWAP:** {pct_from_vwap:+.1f}%") - analysis.append(f"- **Signal:** {vwap_signal}") - analysis.append("") - except Exception as e: - pass - - # Fibonacci Retracement Levels - if 'fib' in indicators: - try: - # Get high and low from last 50 periods - recent_high = float(df.tail(50)['high'].max()) - recent_low = float(df.tail(50)['low'].min()) - diff = recent_high - recent_low - - fib_levels = { - "0.0% (High)": recent_high, - "23.6%": recent_high - (diff * 0.236), - "38.2%": recent_high - (diff * 0.382), - "50.0%": recent_high - (diff * 0.5), - "61.8%": recent_high - (diff * 0.618), - "78.6%": recent_high - (diff * 0.786), - "100% (Low)": recent_low, - } - - # Find nearest support and resistance - support = None - resistance = None - for level_name, level_price in fib_levels.items(): - if level_price < current_price and (support is None or level_price > support[1]): - support = (level_name, level_price) - if level_price > current_price and (resistance is None or level_price < resistance[1]): - resistance = (level_name, level_price) - - analysis.append(f"## Fibonacci Levels (50-period)") - analysis.append(f"- **Recent High:** ${recent_high:.2f}") - analysis.append(f"- **Recent Low:** ${recent_low:.2f}") - if resistance: - analysis.append(f"- **Next Resistance:** ${resistance[1]:.2f} ({resistance[0]})") - if support: - analysis.append(f"- **Next Support:** ${support[1]:.2f} ({support[0]})") - analysis.append("") - except Exception as e: - pass - - # Overall Summary - analysis.append("## Summary") - signals = [] - - # Collect all signals for summary - try: - rsi = float(df.iloc[-1]['rsi']) - if rsi > 70: - signals.append("RSI overbought") - elif rsi < 30: - signals.append("RSI oversold") - except: - pass - - try: - if current_price > float(df.iloc[-1]['close_50_sma']): - signals.append("Above 50 SMA") - else: - signals.append("Below 50 SMA") - except: - pass - - if signals: - analysis.append(f"- **Key Signals:** {', '.join(signals)}") - - return "\n".join(analysis) - + current_price = float(latest["close"]) + + # Instantiate analyst and generate report + analyst = TechnicalAnalyst(df, current_price) + return analyst.generate_report(symbol, curr_date) + except Exception as e: + logger.error(f"Error analyzing {symbol}: {str(e)}") return f"Error analyzing {symbol}: {str(e)}" +def _get_financial_statement(ticker: str, statement_type: str, freq: str) -> str: + """Helper to retrieve financial statements from yfinance.""" + try: + ticker_obj = yf.Ticker(ticker.upper()) + + if statement_type == "balance_sheet": + data = ( + ticker_obj.quarterly_balance_sheet + if freq.lower() == "quarterly" + else ticker_obj.balance_sheet + ) + name = "Balance Sheet" + elif statement_type == "cashflow": + data = ( + ticker_obj.quarterly_cashflow + if freq.lower() == "quarterly" + else ticker_obj.cashflow + ) + name = "Cash Flow" + elif statement_type == "income_statement": + data = ( + ticker_obj.quarterly_income_stmt + if freq.lower() == "quarterly" + else ticker_obj.income_stmt + ) + name = "Income Statement" + else: + return f"Error: Unknown statement type '{statement_type}'" + + if data.empty: + return f"No {name.lower()} data found for symbol '{ticker}'" + + # Convert to CSV string for consistency with other functions + csv_string = data.to_csv() + + # Add header information + header = f"# {name} data for {ticker.upper()} ({freq})\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + csv_string + + except Exception as e: + return f"Error retrieving {name.lower()} for {ticker}: {str(e)}" + + def get_balance_sheet( ticker: Annotated[str, "ticker symbol of the company"], freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", - curr_date: Annotated[str, "current date (not used for yfinance)"] = None + curr_date: Annotated[str, "current date (not used for yfinance)"] = None, ): """Get balance sheet data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - - if freq.lower() == "quarterly": - data = ticker_obj.quarterly_balance_sheet - else: - data = ticker_obj.balance_sheet - - if data.empty: - return f"No balance sheet data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Balance Sheet data for {ticker.upper()} ({freq})\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving balance sheet for {ticker}: {str(e)}" + return _get_financial_statement(ticker, "balance_sheet", freq) def get_cashflow( ticker: Annotated[str, "ticker symbol of the company"], freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", - curr_date: Annotated[str, "current date (not used for yfinance)"] = None + curr_date: Annotated[str, "current date (not used for yfinance)"] = None, ): """Get cash flow data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - - if freq.lower() == "quarterly": - data = ticker_obj.quarterly_cashflow - else: - data = ticker_obj.cashflow - - if data.empty: - return f"No cash flow data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Cash Flow data for {ticker.upper()} ({freq})\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving cash flow for {ticker}: {str(e)}" + return _get_financial_statement(ticker, "cashflow", freq) def get_income_statement( ticker: Annotated[str, "ticker symbol of the company"], freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", - curr_date: Annotated[str, "current date (not used for yfinance)"] = None + curr_date: Annotated[str, "current date (not used for yfinance)"] = None, ): """Get income statement data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - - if freq.lower() == "quarterly": - data = ticker_obj.quarterly_income_stmt - else: - data = ticker_obj.income_stmt - - if data.empty: - return f"No income statement data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Income Statement data for {ticker.upper()} ({freq})\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving income statement for {ticker}: {str(e)}" + return _get_financial_statement(ticker, "income_statement", freq) def get_insider_transactions( ticker: Annotated[str, "ticker symbol of the company"], - curr_date: Annotated[str, "current date (not used for yfinance)"] = None + curr_date: Annotated[str, "current date (not used for yfinance)"] = None, ): """Get insider transactions data from yfinance with parsed transaction types.""" try: ticker_obj = yf.Ticker(ticker.upper()) data = ticker_obj.insider_transactions - + if data is None or data.empty: return f"No insider transactions data found for symbol '{ticker}'" - + # Parse the Text column to populate Transaction type def classify_transaction(text): - if pd.isna(text) or text == '': - return 'Unknown' + if pd.isna(text) or text == "": + return "Unknown" text_lower = str(text).lower() - if 'sale' in text_lower: - return 'Sale' - elif 'purchase' in text_lower or 'buy' in text_lower: - return 'Purchase' - elif 'gift' in text_lower: - return 'Gift' - elif 'exercise' in text_lower or 'option' in text_lower: - return 'Option Exercise' - elif 'award' in text_lower or 'grant' in text_lower: - return 'Award/Grant' - elif 'conversion' in text_lower: - return 'Conversion' + if "sale" in text_lower: + return "Sale" + elif "purchase" in text_lower or "buy" in text_lower: + return "Purchase" + elif "gift" in text_lower: + return "Gift" + elif "exercise" in text_lower or "option" in text_lower: + return "Option Exercise" + elif "award" in text_lower or "grant" in text_lower: + return "Award/Grant" + elif "conversion" in text_lower: + return "Conversion" else: - return 'Other' - + return "Other" + # Apply classification - data['Transaction'] = data['Text'].apply(classify_transaction) - + data["Transaction"] = data["Text"].apply(classify_transaction) + + # Limit to the last 3 months to keep output focused and small + if curr_date: + curr_dt = datetime.strptime(curr_date, "%Y-%m-%d") + else: + curr_dt = datetime.now() + cutoff_dt = curr_dt - relativedelta(months=1) + + if "Start Date" in data.columns: + data["Start Date"] = pd.to_datetime(data["Start Date"], errors="coerce") + data = data[data["Start Date"].notna()] + data = data[data["Start Date"] >= cutoff_dt] + data = data.sort_values(by="Start Date", ascending=False) + + if data.empty: + return f"No insider transactions found for {ticker.upper()} in the last 3 months." + # Calculate summary statistics - transaction_counts = data['Transaction'].value_counts().to_dict() - total_sales_value = data[data['Transaction'] == 'Sale']['Value'].sum() - total_purchases_value = data[data['Transaction'] == 'Purchase']['Value'].sum() - + transaction_counts = data["Transaction"].value_counts().to_dict() + total_sales_value = data[data["Transaction"] == "Sale"]["Value"].sum() + total_purchases_value = data[data["Transaction"] == "Purchase"]["Value"].sum() + # Determine insider sentiment - sales_count = transaction_counts.get('Sale', 0) - purchases_count = transaction_counts.get('Purchase', 0) - + sales_count = transaction_counts.get("Sale", 0) + purchases_count = transaction_counts.get("Purchase", 0) + if purchases_count > sales_count: sentiment = "BULLISH ⚡ (more buying than selling)" elif sales_count > purchases_count * 2: @@ -848,7 +607,7 @@ def get_insider_transactions( sentiment = "Slightly bearish (more selling than buying)" else: sentiment = "Neutral" - + # Build summary header header = f"# Insider Transactions for {ticker.upper()}\n" header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" @@ -860,19 +619,96 @@ def get_insider_transactions( header += f"- **Total Sales Value:** ${total_sales_value:,.0f}\n" if total_purchases_value > 0: header += f"- **Total Purchases Value:** ${total_purchases_value:,.0f}\n" + + def _coerce_numeric(series: pd.Series) -> pd.Series: + return pd.to_numeric( + series.astype(str).str.replace(r"[^0-9.\\-]", "", regex=True), + errors="coerce", + ) + + def _format_txn(row: pd.Series) -> str: + date_val = row.get("Start Date", "") + if isinstance(date_val, pd.Timestamp): + date_val = date_val.strftime("%Y-%m-%d") + insider = row.get("Insider", "N/A") + position = row.get("Position", "N/A") + shares = row.get("Shares", "N/A") + value = row.get("Value", "N/A") + ownership = row.get("Ownership", "N/A") + return f"{date_val} | {insider} ({position}) | {shares} shares | ${value} | Ownership: {ownership}" + + # Highlight largest purchase/sale by value in the last 3 months + if "Value" in data.columns: + value_numeric = _coerce_numeric(data["Value"]) + data = data.assign(_value_numeric=value_numeric) + + purchases = data[data["Transaction"] == "Purchase"] + sales = data[data["Transaction"] == "Sale"] + + if not purchases.empty and purchases["_value_numeric"].notna().any(): + top_purchase = purchases.loc[purchases["_value_numeric"].idxmax()] + header += f"- **Largest Purchase (3mo):** {_format_txn(top_purchase)}\n" + if not sales.empty and sales["_value_numeric"].notna().any(): + top_sale = sales.loc[sales["_value_numeric"].idxmax()] + header += f"- **Largest Sale (3mo):** {_format_txn(top_sale)}\n" header += "\n## Transaction Details\n\n" - + # Select key columns for output - output_cols = ['Start Date', 'Insider', 'Position', 'Transaction', 'Shares', 'Value', 'Ownership'] + output_cols = [ + "Start Date", + "Insider", + "Position", + "Transaction", + "Shares", + "Value", + "Ownership", + ] available_cols = [c for c in output_cols if c in data.columns] - + csv_string = data[available_cols].to_csv(index=False) - + return header + csv_string - + except Exception as e: return f"Error retrieving insider transactions for {ticker}: {str(e)}" + +def get_stock_price( + ticker: Annotated[str, "ticker symbol of the company"], + curr_date: Annotated[str, "current date (for reference)"] = None, +) -> float: + """ + Get the current/latest stock price for a ticker. + + Args: + ticker: Stock symbol + curr_date: Optional date (not used, included for API consistency) + + Returns: + Current stock price as a float, or None if unavailable + """ + try: + with suppress_yfinance_warnings(): + stock = yf.Ticker(ticker.upper()) + # Try fast_info first (more efficient) + try: + price = stock.fast_info.get("lastPrice") + if price is not None: + return float(price) + except Exception: + pass + + # Fallback to history + hist = stock.history(period="1d") + if not hist.empty: + return float(hist["Close"].iloc[-1]) + + return None + except Exception as e: + logger.error(f"Error getting stock price for {ticker}: {e}") + return None + + def validate_ticker(symbol: str) -> bool: """ Validate if a ticker symbol exists and has trading data. @@ -883,34 +719,68 @@ def validate_ticker(symbol: str) -> bool: # fast_info attributes are lazy-loaded _ = ticker.fast_info.get("lastPrice") return True - + except Exception: # Fallback to older method if fast_info fails or is missing try: return not ticker.history(period="1d", progress=False).empty - except: + except Exception: return False +def validate_tickers_batch(symbols: Annotated[List[str], "list of ticker symbols"]) -> dict: + """Validate multiple tickers by downloading minimal recent data.""" + if not symbols: + return {"valid": [], "invalid": []} + + cleaned = [] + for symbol in symbols: + if not symbol: + continue + cleaned.append(str(symbol).strip().upper()) + cleaned = [s for s in cleaned if s] + if not cleaned: + return {"valid": [], "invalid": []} + + data = yf.download( + cleaned, + period="1d", + group_by="ticker", + progress=False, + auto_adjust=False, + ) + valid = [] + if isinstance(data.columns, pd.MultiIndex): + available = set(data.columns.get_level_values(0)) + valid = [s for s in cleaned if s in available and not data[s].dropna(how="all").empty] + else: + # Single ticker case + if not data.empty: + valid = [cleaned[0]] + invalid = [s for s in cleaned if s not in valid] + return {"valid": valid, "invalid": invalid} + + def get_fundamentals( ticker: Annotated[str, "ticker symbol of the company"], - curr_date: Annotated[str, "current date (for reference)"] = None + curr_date: Annotated[str, "current date (for reference)"] = None, ) -> str: """ Get comprehensive fundamental data for a ticker using yfinance. Returns data in a format similar to Alpha Vantage's OVERVIEW endpoint. - + This is a FREE alternative to Alpha Vantage with no rate limits. """ import json - + try: - ticker_obj = yf.Ticker(ticker.upper()) - info = ticker_obj.info - - if not info or info.get('regularMarketPrice') is None: + with suppress_yfinance_warnings(): + ticker_obj = yf.Ticker(ticker.upper()) + info = ticker_obj.info + + if not info or info.get("regularMarketPrice") is None: return f"No fundamental data found for symbol '{ticker}'" - + # Build a structured response similar to Alpha Vantage fundamentals = { # Company Info @@ -926,7 +796,6 @@ def get_fundamentals( "Address": f"{info.get('address1', '')} {info.get('city', '')}, {info.get('state', '')} {info.get('zip', '')}".strip(), "OfficialSite": info.get("website", "N/A"), "FiscalYearEnd": info.get("fiscalYearEnd", "N/A"), - # Valuation "MarketCapitalization": str(info.get("marketCap", "N/A")), "EBITDA": str(info.get("ebitda", "N/A")), @@ -938,7 +807,6 @@ def get_fundamentals( "PriceToSalesRatioTTM": str(info.get("priceToSalesTrailing12Months", "N/A")), "EVToRevenue": str(info.get("enterpriseToRevenue", "N/A")), "EVToEBITDA": str(info.get("enterpriseToEbitda", "N/A")), - # Earnings & Revenue "EPS": str(info.get("trailingEps", "N/A")), "ForwardEPS": str(info.get("forwardEps", "N/A")), @@ -947,20 +815,17 @@ def get_fundamentals( "GrossProfitTTM": str(info.get("grossProfits", "N/A")), "QuarterlyRevenueGrowthYOY": str(info.get("revenueGrowth", "N/A")), "QuarterlyEarningsGrowthYOY": str(info.get("earningsGrowth", "N/A")), - # Margins & Returns "ProfitMargin": str(info.get("profitMargins", "N/A")), "OperatingMarginTTM": str(info.get("operatingMargins", "N/A")), "GrossMargins": str(info.get("grossMargins", "N/A")), "ReturnOnAssetsTTM": str(info.get("returnOnAssets", "N/A")), "ReturnOnEquityTTM": str(info.get("returnOnEquity", "N/A")), - # Dividend "DividendPerShare": str(info.get("dividendRate", "N/A")), "DividendYield": str(info.get("dividendYield", "N/A")), "ExDividendDate": str(info.get("exDividendDate", "N/A")), "PayoutRatio": str(info.get("payoutRatio", "N/A")), - # Balance Sheet "TotalCash": str(info.get("totalCash", "N/A")), "TotalDebt": str(info.get("totalDebt", "N/A")), @@ -969,7 +834,6 @@ def get_fundamentals( "DebtToEquity": str(info.get("debtToEquity", "N/A")), "FreeCashFlow": str(info.get("freeCashflow", "N/A")), "OperatingCashFlow": str(info.get("operatingCashflow", "N/A")), - # Trading Info "Beta": str(info.get("beta", "N/A")), "52WeekHigh": str(info.get("fiftyTwoWeekHigh", "N/A")), @@ -981,11 +845,9 @@ def get_fundamentals( "SharesShort": str(info.get("sharesShort", "N/A")), "ShortRatio": str(info.get("shortRatio", "N/A")), "ShortPercentOfFloat": str(info.get("shortPercentOfFloat", "N/A")), - # Ownership "PercentInsiders": str(info.get("heldPercentInsiders", "N/A")), "PercentInstitutions": str(info.get("heldPercentInstitutions", "N/A")), - # Analyst "AnalystTargetPrice": str(info.get("targetMeanPrice", "N/A")), "AnalystTargetHigh": str(info.get("targetHighPrice", "N/A")), @@ -994,10 +856,10 @@ def get_fundamentals( "RecommendationKey": info.get("recommendationKey", "N/A"), "RecommendationMean": str(info.get("recommendationMean", "N/A")), } - + # Return as formatted JSON string return json.dumps(fundamentals, indent=4) - + except Exception as e: return f"Error retrieving fundamentals for {ticker}: {str(e)}" @@ -1005,106 +867,116 @@ def get_fundamentals( def get_options_activity( ticker: Annotated[str, "ticker symbol of the company"], num_expirations: Annotated[int, "number of nearest expiration dates to analyze"] = 3, - curr_date: Annotated[str, "current date (for reference)"] = None + curr_date: Annotated[str, "current date (for reference)"] = None, ) -> str: """ Get options activity for a specific ticker using yfinance. Analyzes volume, open interest, and put/call ratios. - + This is a FREE alternative to Tradier with no API key required. """ try: ticker_obj = yf.Ticker(ticker.upper()) - + # Get available expiration dates expirations = ticker_obj.options if not expirations: return f"No options data available for {ticker}" - + # Analyze the nearest N expiration dates - expirations_to_analyze = expirations[:min(num_expirations, len(expirations))] - + expirations_to_analyze = expirations[: min(num_expirations, len(expirations))] + report = f"## Options Activity for {ticker.upper()}\n\n" report += f"**Available Expirations:** {len(expirations)} dates\n" report += f"**Analyzing:** {', '.join(expirations_to_analyze)}\n\n" - + total_call_volume = 0 total_put_volume = 0 total_call_oi = 0 total_put_oi = 0 - + unusual_activity = [] - + for exp_date in expirations_to_analyze: try: opt = ticker_obj.option_chain(exp_date) calls = opt.calls puts = opt.puts - + if calls.empty and puts.empty: continue - + # Calculate totals for this expiration - call_vol = calls['volume'].sum() if 'volume' in calls.columns else 0 - put_vol = puts['volume'].sum() if 'volume' in puts.columns else 0 - call_oi = calls['openInterest'].sum() if 'openInterest' in calls.columns else 0 - put_oi = puts['openInterest'].sum() if 'openInterest' in puts.columns else 0 - + call_vol = calls["volume"].sum() if "volume" in calls.columns else 0 + put_vol = puts["volume"].sum() if "volume" in puts.columns else 0 + call_oi = calls["openInterest"].sum() if "openInterest" in calls.columns else 0 + put_oi = puts["openInterest"].sum() if "openInterest" in puts.columns else 0 + # Handle NaN values call_vol = 0 if pd.isna(call_vol) else int(call_vol) put_vol = 0 if pd.isna(put_vol) else int(put_vol) call_oi = 0 if pd.isna(call_oi) else int(call_oi) put_oi = 0 if pd.isna(put_oi) else int(put_oi) - + total_call_volume += call_vol total_put_volume += put_vol total_call_oi += call_oi total_put_oi += put_oi - + # Find unusual activity (high volume relative to OI) for _, row in calls.iterrows(): - vol = row.get('volume', 0) - oi = row.get('openInterest', 0) + vol = row.get("volume", 0) + oi = row.get("openInterest", 0) if pd.notna(vol) and pd.notna(oi) and oi > 0 and vol > oi * 0.5 and vol > 100: - unusual_activity.append({ - 'type': 'CALL', - 'expiration': exp_date, - 'strike': row['strike'], - 'volume': int(vol), - 'openInterest': int(oi), - 'vol_oi_ratio': round(vol / oi, 2) if oi > 0 else 0, - 'impliedVolatility': round(row.get('impliedVolatility', 0) * 100, 1) - }) - + unusual_activity.append( + { + "type": "CALL", + "expiration": exp_date, + "strike": row["strike"], + "volume": int(vol), + "openInterest": int(oi), + "vol_oi_ratio": round(vol / oi, 2) if oi > 0 else 0, + "impliedVolatility": round( + row.get("impliedVolatility", 0) * 100, 1 + ), + } + ) + for _, row in puts.iterrows(): - vol = row.get('volume', 0) - oi = row.get('openInterest', 0) + vol = row.get("volume", 0) + oi = row.get("openInterest", 0) if pd.notna(vol) and pd.notna(oi) and oi > 0 and vol > oi * 0.5 and vol > 100: - unusual_activity.append({ - 'type': 'PUT', - 'expiration': exp_date, - 'strike': row['strike'], - 'volume': int(vol), - 'openInterest': int(oi), - 'vol_oi_ratio': round(vol / oi, 2) if oi > 0 else 0, - 'impliedVolatility': round(row.get('impliedVolatility', 0) * 100, 1) - }) - + unusual_activity.append( + { + "type": "PUT", + "expiration": exp_date, + "strike": row["strike"], + "volume": int(vol), + "openInterest": int(oi), + "vol_oi_ratio": round(vol / oi, 2) if oi > 0 else 0, + "impliedVolatility": round( + row.get("impliedVolatility", 0) * 100, 1 + ), + } + ) + except Exception as e: report += f"*Error fetching {exp_date}: {str(e)}*\n" continue - + # Calculate put/call ratios - pc_volume_ratio = round(total_put_volume / total_call_volume, 3) if total_call_volume > 0 else 0 + pc_volume_ratio = ( + round(total_put_volume / total_call_volume, 3) if total_call_volume > 0 else 0 + ) pc_oi_ratio = round(total_put_oi / total_call_oi, 3) if total_call_oi > 0 else 0 - + # Summary report += "### Summary\n" report += "| Metric | Calls | Puts | Put/Call Ratio |\n" report += "|--------|-------|------|----------------|\n" report += f"| Volume | {total_call_volume:,} | {total_put_volume:,} | {pc_volume_ratio} |\n" report += f"| Open Interest | {total_call_oi:,} | {total_put_oi:,} | {pc_oi_ratio} |\n\n" - + # Sentiment interpretation report += "### Sentiment Analysis\n" if pc_volume_ratio < 0.7: @@ -1113,20 +985,20 @@ def get_options_activity( report += "- **Volume P/C Ratio:** Bearish (more put volume)\n" else: report += "- **Volume P/C Ratio:** Neutral\n" - + if pc_oi_ratio < 0.7: report += "- **OI P/C Ratio:** Bullish positioning\n" elif pc_oi_ratio > 1.3: report += "- **OI P/C Ratio:** Bearish positioning\n" else: report += "- **OI P/C Ratio:** Neutral positioning\n" - + # Unusual activity if unusual_activity: # Sort by volume/OI ratio - unusual_activity.sort(key=lambda x: x['vol_oi_ratio'], reverse=True) + unusual_activity.sort(key=lambda x: x["vol_oi_ratio"], reverse=True) top_unusual = unusual_activity[:10] - + report += "\n### Unusual Activity (High Volume vs Open Interest)\n" report += "| Type | Expiry | Strike | Volume | OI | Vol/OI | IV |\n" report += "|------|--------|--------|--------|----|---------|----|---|\n" @@ -1134,16 +1006,149 @@ def get_options_activity( report += f"| {item['type']} | {item['expiration']} | ${item['strike']} | {item['volume']:,} | {item['openInterest']:,} | {item['vol_oi_ratio']}x | {item['impliedVolatility']}% |\n" else: report += "\n*No unusual options activity detected.*\n" - + return report - + except Exception as e: return f"Error retrieving options activity for {ticker}: {str(e)}" +def analyze_options_flow( + ticker: Annotated[str, "ticker symbol of the company"], + num_expirations: Annotated[int, "number of nearest expiration dates to analyze"] = 3, +) -> Dict[str, Any]: + """ + Analyze options flow to detect unusual activity that signals informed trading. + + Returns structured data for filtering/ranking decisions. + + Signals: + - very_bullish: P/C ratio < 0.5 (heavy call buying) + - bullish: P/C ratio 0.5-0.7 + - neutral: P/C ratio 0.7-1.3 + - bearish: P/C ratio 1.3-2.0 + - very_bearish: P/C ratio > 2.0 (heavy put buying) + + Returns: + Dict with signal, ratios, unusual activity flags + """ + result = { + "ticker": ticker.upper(), + "signal": "neutral", + "pc_volume_ratio": None, + "pc_oi_ratio": None, + "total_call_volume": 0, + "total_put_volume": 0, + "unusual_calls": 0, + "unusual_puts": 0, + "has_unusual_activity": False, + "is_bullish_flow": False, + "error": None, + } + + try: + ticker_obj = yf.Ticker(ticker.upper()) + expirations = ticker_obj.options + + if not expirations: + result["error"] = "No options data" + return result + + expirations_to_analyze = expirations[: min(num_expirations, len(expirations))] + + total_call_volume = 0 + total_put_volume = 0 + total_call_oi = 0 + total_put_oi = 0 + unusual_calls = 0 + unusual_puts = 0 + + for exp_date in expirations_to_analyze: + try: + opt = ticker_obj.option_chain(exp_date) + calls = opt.calls + puts = opt.puts + + if calls.empty and puts.empty: + continue + + # Calculate totals + call_vol = calls["volume"].sum() if "volume" in calls.columns else 0 + put_vol = puts["volume"].sum() if "volume" in puts.columns else 0 + call_oi = calls["openInterest"].sum() if "openInterest" in calls.columns else 0 + put_oi = puts["openInterest"].sum() if "openInterest" in puts.columns else 0 + + call_vol = 0 if pd.isna(call_vol) else int(call_vol) + put_vol = 0 if pd.isna(put_vol) else int(put_vol) + call_oi = 0 if pd.isna(call_oi) else int(call_oi) + put_oi = 0 if pd.isna(put_oi) else int(put_oi) + + total_call_volume += call_vol + total_put_volume += put_vol + total_call_oi += call_oi + total_put_oi += put_oi + + # Count unusual activity (volume > 50% of OI and volume > 100) + for _, row in calls.iterrows(): + vol = row.get("volume", 0) + oi = row.get("openInterest", 0) + if pd.notna(vol) and pd.notna(oi) and oi > 0 and vol > oi * 0.5 and vol > 100: + unusual_calls += 1 + + for _, row in puts.iterrows(): + vol = row.get("volume", 0) + oi = row.get("openInterest", 0) + if pd.notna(vol) and pd.notna(oi) and oi > 0 and vol > oi * 0.5 and vol > 100: + unusual_puts += 1 + + except Exception: + continue + + # Calculate ratios + pc_volume_ratio = ( + round(total_put_volume / total_call_volume, 3) if total_call_volume > 0 else None + ) + pc_oi_ratio = round(total_put_oi / total_call_oi, 3) if total_call_oi > 0 else None + + # Determine signal based on P/C ratio + signal = "neutral" + if pc_volume_ratio is not None: + if pc_volume_ratio < 0.5: + signal = "very_bullish" + elif pc_volume_ratio < 0.7: + signal = "bullish" + elif pc_volume_ratio > 2.0: + signal = "very_bearish" + elif pc_volume_ratio > 1.3: + signal = "bearish" + + # Determine if there's unusual bullish flow + has_unusual = (unusual_calls + unusual_puts) > 0 + is_bullish_flow = has_unusual and unusual_calls > unusual_puts * 2 + + result.update( + { + "signal": signal, + "pc_volume_ratio": pc_volume_ratio, + "pc_oi_ratio": pc_oi_ratio, + "total_call_volume": total_call_volume, + "total_put_volume": total_put_volume, + "unusual_calls": unusual_calls, + "unusual_puts": unusual_puts, + "has_unusual_activity": has_unusual, + "is_bullish_flow": is_bullish_flow, + } + ) + + return result + + except Exception as e: + result["error"] = str(e) + return result + + def _get_ticker_universe( - tickers: Optional[Union[str, List[str]]] = None, - max_tickers: Optional[int] = None + tickers: Optional[Union[str, List[str]]] = None, max_tickers: Optional[int] = None ) -> List[str]: """ Get a list of ticker symbols. @@ -1162,42 +1167,124 @@ def _get_ticker_universe( # Load from config file from tradingagents.default_config import DEFAULT_CONFIG + ticker_file = DEFAULT_CONFIG.get("tickers_file") if not ticker_file: - print("Warning: tickers_file not configured, using fallback list") + logger.warning("tickers_file not configured, using fallback list") return _get_default_tickers()[:max_tickers] if max_tickers else _get_default_tickers() # Load tickers from file try: ticker_path = Path(ticker_file) if ticker_path.exists(): - with open(ticker_path, 'r') as f: + with open(ticker_path, "r") as f: ticker_list = [line.strip().upper() for line in f if line.strip()] # Remove duplicates while preserving order seen = set() ticker_list = [t for t in ticker_list if t and t not in seen and not seen.add(t)] return ticker_list[:max_tickers] if max_tickers else ticker_list else: - print(f"Warning: Ticker file not found at {ticker_file}, using fallback list") + logger.warning(f"Ticker file not found at {ticker_file}, using fallback list") return _get_default_tickers()[:max_tickers] if max_tickers else _get_default_tickers() except Exception as e: - print(f"Warning: Could not load ticker list from file: {e}, using fallback") + logger.warning(f"Could not load ticker list from file: {e}, using fallback") return _get_default_tickers()[:max_tickers] if max_tickers else _get_default_tickers() def _get_default_tickers() -> List[str]: """Fallback list of major US stocks if ticker file is not found.""" return [ - "AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA", "BRK-B", "V", "UNH", - "XOM", "JNJ", "JPM", "WMT", "MA", "PG", "LLY", "AVGO", "HD", "MRK", - "COST", "ABBV", "PEP", "ADBE", "TMO", "CSCO", "NFLX", "ACN", "DHR", "ABT", - "VZ", "WFC", "CRM", "PM", "LIN", "DIS", "BMY", "NKE", "TXN", "RTX", - "QCOM", "UPS", "HON", "AMGN", "DE", "INTU", "AMAT", "LOW", "SBUX", "C", - "BKNG", "ADP", "GE", "TJX", "AXP", "SPGI", "MDT", "GILD", "ISRG", "BLK", - "SYK", "ZTS", "CI", "CME", "ICE", "EQIX", "REGN", "APH", "KLAC", "CDNS", - "SNPS", "MCHP", "FTNT", "ANSS", "CTSH", "WDAY", "ON", "NXPI", "MPWR", "CRWD", - "AMD", "INTC", "MU", "LRCX", "PANW", "NOW", "DDOG", "ZS", "NET", "TEAM" + "AAPL", + "MSFT", + "GOOGL", + "AMZN", + "NVDA", + "META", + "TSLA", + "BRK-B", + "V", + "UNH", + "XOM", + "JNJ", + "JPM", + "WMT", + "MA", + "PG", + "LLY", + "AVGO", + "HD", + "MRK", + "COST", + "ABBV", + "PEP", + "ADBE", + "TMO", + "CSCO", + "NFLX", + "ACN", + "DHR", + "ABT", + "VZ", + "WFC", + "CRM", + "PM", + "LIN", + "DIS", + "BMY", + "NKE", + "TXN", + "RTX", + "QCOM", + "UPS", + "HON", + "AMGN", + "DE", + "INTU", + "AMAT", + "LOW", + "SBUX", + "C", + "BKNG", + "ADP", + "GE", + "TJX", + "AXP", + "SPGI", + "MDT", + "GILD", + "ISRG", + "BLK", + "SYK", + "ZTS", + "CI", + "CME", + "ICE", + "EQIX", + "REGN", + "APH", + "KLAC", + "CDNS", + "SNPS", + "MCHP", + "FTNT", + "ANSS", + "CTSH", + "WDAY", + "ON", + "NXPI", + "MPWR", + "CRWD", + "AMD", + "INTC", + "MU", + "LRCX", + "PANW", + "NOW", + "DDOG", + "ZS", + "NET", + "TEAM", ] @@ -1226,38 +1313,38 @@ def get_pre_earnings_accumulation_signal( # Get 1 month of data to calculate baseline hist = stock.history(period="1mo") if len(hist) < 20: - return {'signal': False, 'reason': 'Insufficient data'} + return {"signal": False, "reason": "Insufficient data"} # Baseline volume (excluding recent period) - baseline_volume = hist['Volume'][:-lookback_days].mean() + baseline_volume = hist["Volume"][:-lookback_days].mean() # Recent volume - recent_volume = hist['Volume'][-lookback_days:].mean() + recent_volume = hist["Volume"][-lookback_days:].mean() # Volume ratio volume_ratio = recent_volume / baseline_volume if baseline_volume > 0 else 0 # Price movement in recent period - price_start = hist['Close'].iloc[-lookback_days] - price_end = hist['Close'].iloc[-1] + price_start = hist["Close"].iloc[-lookback_days] + price_end = hist["Close"].iloc[-1] price_change_pct = ((price_end - price_start) / price_start) * 100 # SIGNAL CRITERIA: # - Volume up at least 50% (1.5x) # - Price relatively flat (< 5% move) - accumulation_signal = volume_ratio >= 1.5 and abs(price_change_pct) < 5.0 + accumulation_signal = volume_ratio >= 2.0 and abs(price_change_pct) < 5.0 return { - 'signal': accumulation_signal, - 'volume_ratio': round(volume_ratio, 2), - 'price_change_pct': round(price_change_pct, 2), - 'current_price': round(price_end, 2), - 'baseline_volume': int(baseline_volume), - 'recent_volume': int(recent_volume), + "signal": accumulation_signal, + "volume_ratio": round(volume_ratio, 2), + "price_change_pct": round(price_change_pct, 2), + "current_price": round(price_end, 2), + "baseline_volume": int(baseline_volume), + "recent_volume": int(recent_volume), } except Exception as e: - return {'signal': False, 'reason': str(e)} + return {"signal": False, "reason": str(e)} def check_if_price_reacted( @@ -1286,22 +1373,89 @@ def check_if_price_reacted( # Get recent history hist = stock.history(period="1mo") if len(hist) < lookback_days: - return {'reacted': None, 'reason': 'Insufficient data', 'status': 'unknown'} + return {"reacted": None, "reason": "Insufficient data", "status": "unknown"} # Check price movement in lookback period - price_start = hist['Close'].iloc[-lookback_days] - price_end = hist['Close'].iloc[-1] + price_start = hist["Close"].iloc[-lookback_days] + price_end = hist["Close"].iloc[-1] price_change_pct = ((price_end - price_start) / price_start) * 100 # Determine if already reacted reacted = abs(price_change_pct) >= reaction_threshold return { - 'reacted': reacted, - 'price_change_pct': round(price_change_pct, 2), - 'status': 'lagging' if reacted else 'leading', - 'current_price': round(price_end, 2), + "reacted": reacted, + "price_change_pct": round(price_change_pct, 2), + "status": "lagging" if reacted else "leading", + "current_price": round(price_end, 2), } except Exception as e: - return {'reacted': None, 'reason': str(e), 'status': 'unknown'} \ No newline at end of file + return {"reacted": None, "reason": str(e), "status": "unknown"} + + +def check_intraday_movement( + ticker: Annotated[str, "ticker symbol to analyze"], + movement_threshold: Annotated[float, "% change to consider as 'already moved'"] = 15.0, +) -> dict: + """ + Check if a stock has already moved significantly today (intraday). + + This helps filter out stocks that have already experienced their major price move, + avoiding "chasing" stocks that have already run. + + Args: + ticker: Stock symbol to check + movement_threshold: Intraday % change to consider as "already moved" (default 15%) + + Returns: + Dict with: + - 'already_moved': bool (True if price moved more than threshold) + - 'intraday_change_pct': float (% change from open to current/last) + - 'open_price': float + - 'current_price': float + - 'status': str ('fresh' if not moved, 'stale' if already moved) + """ + try: + with suppress_yfinance_warnings(): + stock = yf.Ticker(ticker.upper()) + + # Get today's intraday data (1-day period with 1-minute interval) + # This gives us open, current price, high, low for today + hist = stock.history(period="1d", interval="1m") + + if hist.empty: + # Fallback to daily data if intraday not available + hist_daily = stock.history(period="5d") + if hist_daily.empty or len(hist_daily) == 0: + return { + "already_moved": None, + "reason": "No price data available", + "status": "unknown", + } + + # Use today's open and close from daily data + today_data = hist_daily.iloc[-1] + open_price = today_data["Open"] + current_price = today_data["Close"] + else: + # Use intraday data - first candle's open vs latest candle's close + open_price = hist["Open"].iloc[0] + current_price = hist["Close"].iloc[-1] + + # Calculate intraday change percentage + intraday_change_pct = ((current_price - open_price) / open_price) * 100 + + # Determine if stock already moved significantly + already_moved = abs(intraday_change_pct) >= movement_threshold + + return { + "already_moved": already_moved, + "intraday_change_pct": round(intraday_change_pct, 2), + "open_price": round(open_price, 2), + "current_price": round(current_price, 2), + "status": "stale" if already_moved else "fresh", + } + + except Exception as e: + return {"already_moved": None, "reason": str(e), "status": "unknown"} diff --git a/tradingagents/dataflows/yfin_utils.py b/tradingagents/dataflows/yfin_utils.py deleted file mode 100644 index bd7ca324..00000000 --- a/tradingagents/dataflows/yfin_utils.py +++ /dev/null @@ -1,117 +0,0 @@ -# gets data/stats - -import yfinance as yf -from typing import Annotated, Callable, Any, Optional -from pandas import DataFrame -import pandas as pd -from functools import wraps - -from .utils import save_output, SavePathType, decorate_all_methods - - -def init_ticker(func: Callable) -> Callable: - """Decorator to initialize yf.Ticker and pass it to the function.""" - - @wraps(func) - def wrapper(symbol: Annotated[str, "ticker symbol"], *args, **kwargs) -> Any: - ticker = yf.Ticker(symbol) - return func(ticker, *args, **kwargs) - - return wrapper - - -@decorate_all_methods(init_ticker) -class YFinanceUtils: - - def get_stock_data( - symbol: Annotated[str, "ticker symbol"], - start_date: Annotated[ - str, "start date for retrieving stock price data, YYYY-mm-dd" - ], - end_date: Annotated[ - str, "end date for retrieving stock price data, YYYY-mm-dd" - ], - save_path: SavePathType = None, - ) -> DataFrame: - """retrieve stock price data for designated ticker symbol""" - ticker = symbol - # add one day to the end_date so that the data range is inclusive - end_date = pd.to_datetime(end_date) + pd.DateOffset(days=1) - end_date = end_date.strftime("%Y-%m-%d") - stock_data = ticker.history(start=start_date, end=end_date) - # save_output(stock_data, f"Stock data for {ticker.ticker}", save_path) - return stock_data - - def get_stock_info( - symbol: Annotated[str, "ticker symbol"], - ) -> dict: - """Fetches and returns latest stock information.""" - ticker = symbol - stock_info = ticker.info - return stock_info - - def get_company_info( - symbol: Annotated[str, "ticker symbol"], - save_path: Optional[str] = None, - ) -> DataFrame: - """Fetches and returns company information as a DataFrame.""" - ticker = symbol - info = ticker.info - company_info = { - "Company Name": info.get("shortName", "N/A"), - "Industry": info.get("industry", "N/A"), - "Sector": info.get("sector", "N/A"), - "Country": info.get("country", "N/A"), - "Website": info.get("website", "N/A"), - } - company_info_df = DataFrame([company_info]) - if save_path: - company_info_df.to_csv(save_path) - print(f"Company info for {ticker.ticker} saved to {save_path}") - return company_info_df - - def get_stock_dividends( - symbol: Annotated[str, "ticker symbol"], - save_path: Optional[str] = None, - ) -> DataFrame: - """Fetches and returns the latest dividends data as a DataFrame.""" - ticker = symbol - dividends = ticker.dividends - if save_path: - dividends.to_csv(save_path) - print(f"Dividends for {ticker.ticker} saved to {save_path}") - return dividends - - def get_income_stmt(symbol: Annotated[str, "ticker symbol"]) -> DataFrame: - """Fetches and returns the latest income statement of the company as a DataFrame.""" - ticker = symbol - income_stmt = ticker.financials - return income_stmt - - def get_balance_sheet(symbol: Annotated[str, "ticker symbol"]) -> DataFrame: - """Fetches and returns the latest balance sheet of the company as a DataFrame.""" - ticker = symbol - balance_sheet = ticker.balance_sheet - return balance_sheet - - def get_cash_flow(symbol: Annotated[str, "ticker symbol"]) -> DataFrame: - """Fetches and returns the latest cash flow statement of the company as a DataFrame.""" - ticker = symbol - cash_flow = ticker.cashflow - return cash_flow - - def get_analyst_recommendations(symbol: Annotated[str, "ticker symbol"]) -> tuple: - """Fetches the latest analyst recommendations and returns the most common recommendation and its count.""" - ticker = symbol - recommendations = ticker.recommendations - if recommendations.empty: - return None, 0 # No recommendations available - - # Assuming 'period' column exists and needs to be excluded - row_0 = recommendations.iloc[0, 1:] # Exclude 'period' column if necessary - - # Find the maximum voting result - max_votes = row_0.max() - majority_voting_result = row_0[row_0 == max_votes].index.tolist() - - return majority_voting_result[0], max_votes diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py index 17548bd0..40138055 100644 --- a/tradingagents/default_config.py +++ b/tradingagents/default_config.py @@ -28,19 +28,17 @@ DEFAULT_CONFIG = { "final_recommendations": 15, # Number of final opportunities to recommend "deep_dive_max_workers": 1, # Parallel workers for deep-dive analysis (1 = sequential) "discovery_mode": "hybrid", # "traditional", "semantic", or "hybrid" - # Ranking context truncation "truncate_ranking_context": False, # True = truncate to save tokens, False = full context "max_news_chars": 500, # Only used if truncate_ranking_context=True "max_insider_chars": 300, # Only used if truncate_ranking_context=True "max_recommendations_chars": 300, # Only used if truncate_ranking_context=True - # Tool execution logging "log_tool_calls": True, # Capture tool inputs/outputs to results logs "log_tool_calls_console": False, # Mirror tool logs to Python logger + "log_prompts_console": False, # Show LLM prompts in console (always saved to log file) "tool_log_max_chars": 10_000, # Max chars stored per tool output "tool_log_exclude": ["validate_ticker"], # Tool names to exclude from logging - # Console price charts (output formatting) "console_price_charts": True, # Render mini price charts in console output "price_chart_library": "plotille", # "plotille" (prettier) or "plotext" fallback @@ -50,7 +48,6 @@ DEFAULT_CONFIG = { "price_chart_height": 12, # Chart height (rows) "price_chart_max_tickers": 10, # Max tickers to chart per run "price_chart_show_movement_stats": True, # Show movement stats in console - # ======================================== # FILTER STAGE SETTINGS # ======================================== @@ -58,24 +55,30 @@ DEFAULT_CONFIG = { # Liquidity filter "min_average_volume": 500_000, # Minimum average volume "volume_lookback_days": 10, # Days to average for liquidity check - # Same-day mover filter (remove stocks that already moved today) "filter_same_day_movers": True, # Enable/disable filter "intraday_movement_threshold": 10.0, # Intraday % change threshold - # Recent mover filter (remove stocks that moved in recent days) "filter_recent_movers": True, # Enable/disable filter "recent_movement_lookback_days": 7, # Days to check for recent moves "recent_movement_threshold": 10.0, # % change threshold "recent_mover_action": "filter", # "filter" or "deprioritize" + # Volume / compression detection + "volume_cache_key": "default", # Cache key for volume data + "min_market_cap": 0, # Minimum market cap in billions (0 = no filter) + "compression_atr_pct_max": 2.0, # Max ATR % for compression detection + "compression_bb_width_max": 6.0, # Max Bollinger bandwidth for compression + "compression_min_volume_ratio": 1.3, # Min volume ratio for compression }, - # ======================================== # ENRICHMENT STAGE SETTINGS # ======================================== "enrichment": { "batch_news_vendor": "google", # Vendor for batch news: "openai" or "google" "batch_news_batch_size": 150, # Tickers per API call + "news_lookback_days": 0.5, # Days of news history for enrichment + "context_max_snippets": 2, # Max news snippets per candidate + "context_snippet_max_chars": 140, # Max chars per snippet }, # ======================================== # PIPELINES (priority and budget per pipeline) @@ -224,6 +227,17 @@ DEFAULT_CONFIG = { "min_short_interest_pct": 15.0, # Minimum short interest % "min_days_to_cover": 5.0, # Minimum days to cover ratio }, + "ml_signal": { + "enabled": True, + "pipeline": "momentum", + "limit": 15, + "min_win_prob": 0.35, # Minimum P(WIN) to surface as candidate + "lookback_period": "1y", # OHLCV history to fetch (needs ~210 trading days) + # ticker_file: path to ticker list (defaults to tickers_file from root config) + # ticker_universe: explicit list overrides ticker_file if set + "fetch_market_cap": False, # Skip for speed (1 NaN out of 30 features) + "max_workers": 8, # Parallel feature computation threads + }, }, }, # Memory settings diff --git a/tradingagents/graph/__init__.py b/tradingagents/graph/__init__.py index 80982c19..901edddd 100644 --- a/tradingagents/graph/__init__.py +++ b/tradingagents/graph/__init__.py @@ -1,11 +1,11 @@ # TradingAgents/graph/__init__.py -from .trading_graph import TradingAgentsGraph from .conditional_logic import ConditionalLogic -from .setup import GraphSetup from .propagation import Propagator from .reflection import Reflector +from .setup import GraphSetup from .signal_processing import SignalProcessor +from .trading_graph import TradingAgentsGraph __all__ = [ "TradingAgentsGraph", diff --git a/tradingagents/graph/conditional_logic.py b/tradingagents/graph/conditional_logic.py index e7c87859..2bc9c5db 100644 --- a/tradingagents/graph/conditional_logic.py +++ b/tradingagents/graph/conditional_logic.py @@ -11,37 +11,29 @@ class ConditionalLogic: self.max_debate_rounds = max_debate_rounds self.max_risk_discuss_rounds = max_risk_discuss_rounds - def should_continue_market(self, state: AgentState): - """Determine if market analysis should continue.""" + def _should_continue_tools(self, state: AgentState, tool_call_indicator: str, clear_msg: str): + """Helper to determine if analysis should continue with tools.""" messages = state["messages"] last_message = messages[-1] if last_message.tool_calls: - return "tools_market" - return "Msg Clear Market" + return tool_call_indicator + return clear_msg + + def should_continue_market(self, state: AgentState): + """Determine if market analysis should continue.""" + return self._should_continue_tools(state, "tools_market", "Msg Clear Market") def should_continue_social(self, state: AgentState): """Determine if social media analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_social" - return "Msg Clear Social" + return self._should_continue_tools(state, "tools_social", "Msg Clear Social") def should_continue_news(self, state: AgentState): """Determine if news analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_news" - return "Msg Clear News" + return self._should_continue_tools(state, "tools_news", "Msg Clear News") def should_continue_fundamentals(self, state: AgentState): """Determine if fundamentals analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_fundamentals" - return "Msg Clear Fundamentals" + return self._should_continue_tools(state, "tools_fundamentals", "Msg Clear Fundamentals") def should_continue_debate(self, state: AgentState) -> str: """Determine if debate should continue.""" diff --git a/tradingagents/graph/discovery_graph.py b/tradingagents/graph/discovery_graph.py index 34c20632..84dd54f1 100644 --- a/tradingagents/graph/discovery_graph.py +++ b/tradingagents/graph/discovery_graph.py @@ -1,28 +1,21 @@ -from typing import Any, Callable, Dict, List, Optional +from __future__ import annotations + +from threading import Lock +from typing import TYPE_CHECKING, Any, Dict, List from langgraph.graph import END, StateGraph from tradingagents.agents.utils.agent_states import DiscoveryState +from tradingagents.dataflows.discovery.discovery_config import DiscoveryConfig from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY from tradingagents.dataflows.discovery.utils import PRIORITY_ORDER, Priority, serialize_for_log from tradingagents.tools.executor import execute_tool +from tradingagents.utils.logger import get_logger +logger = get_logger(__name__) -# Known PERMANENTLY delisted tickers (verified mergers, bankruptcies, delistings) -# NOTE: This list should only contain tickers that are CONFIRMED to be permanently delisted. -# Do NOT add actively traded stocks here. Use the dynamic delisted_cache for uncertain cases. -def get_delisted_tickers(): - """Get combined list of delisted tickers from permanent list + dynamic cache.""" - from tradingagents.dataflows.discovery.utils import get_delisted_tickers - - return get_delisted_tickers() - - -def is_valid_ticker(ticker: str) -> bool: - """Validate if a ticker is tradeable and not junk.""" - from tradingagents.dataflows.discovery.utils import is_valid_ticker - - return is_valid_ticker(ticker) +if TYPE_CHECKING: + from tradingagents.graph.price_charts import PriceChartBuilder class DiscoveryGraph: @@ -30,7 +23,7 @@ class DiscoveryGraph: Discovery Graph for finding investment opportunities. Orchestrates the discovery workflow: scanning -> filtering -> ranking. - Supports traditional, semantic, and hybrid discovery modes. + Uses the modular scanner registry to discover candidates. """ # Node names @@ -39,20 +32,8 @@ class DiscoveryGraph: NODE_RANKER = "ranker" # Source types - SOURCE_NEWS_MENTION = "news_direct_mention" - SOURCE_SEMANTIC = "semantic_news_match" SOURCE_UNKNOWN = "unknown" - # Priority levels (lower number = higher priority) - PRIORITY_ORDER = PRIORITY_ORDER - - # Priority level names - PRIORITY_CRITICAL = Priority.CRITICAL.value - PRIORITY_HIGH = Priority.HIGH.value - PRIORITY_MEDIUM = Priority.MEDIUM.value - PRIORITY_LOW = Priority.LOW.value - PRIORITY_UNKNOWN = Priority.UNKNOWN.value - def __init__(self, config: Dict[str, Any] = None): """ Initialize Discovery Graph. @@ -64,19 +45,32 @@ class DiscoveryGraph: - results_dir: Directory for saving results """ self.config = config or {} + self._tool_logs_lock = Lock() # Thread-safe state mutation lock # Load scanner modules to trigger registration from tradingagents.dataflows.discovery import scanners + _ = scanners # Ensure scanners module is loaded # Initialize LLMs from tradingagents.utils.llm_factory import create_llms - self.deep_thinking_llm, self.quick_thinking_llm = create_llms(self.config) + try: + self.deep_thinking_llm, self.quick_thinking_llm = create_llms(self.config) + except Exception as e: + logger.error(f"Failed to initialize LLMs: {e}") + raise ValueError( + f"LLM initialization failed. Check your config's llm_provider setting. Error: {e}" + ) from e - # Load configurations - self._load_discovery_config() - self._load_logging_config() + # Load typed discovery configuration + self.dc = DiscoveryConfig.from_config(self.config) + + # Alias frequently-used config for downstream compatibility + self.log_tool_calls = self.dc.logging.log_tool_calls + self.log_tool_calls_console = self.dc.logging.log_tool_calls_console + self.tool_log_max_chars = self.dc.logging.tool_log_max_chars + self.tool_log_exclude = set(self.dc.logging.tool_log_exclude) # Store run directory for saving results self.run_dir = self.config.get("discovery_run_dir", None) @@ -88,74 +82,6 @@ class DiscoveryGraph: self.graph = self._create_graph() - def _load_discovery_config(self) -> None: - """Load discovery-specific configuration with defaults.""" - discovery_config = self.config.get("discovery", {}) - - # Scanner limits - self.reddit_trending_limit = discovery_config.get("reddit_trending_limit", 15) - self.market_movers_limit = discovery_config.get("market_movers_limit", 10) - self.max_candidates_to_analyze = discovery_config.get("max_candidates_to_analyze", 100) - self.analyze_all_candidates = discovery_config.get("analyze_all_candidates", False) - self.final_recommendations = discovery_config.get("final_recommendations", 3) - self.deep_dive_max_workers = discovery_config.get("deep_dive_max_workers", 3) - - # Volume and movement filters - self.min_average_volume = discovery_config.get("min_average_volume", 0) - self.volume_lookback_days = discovery_config.get("volume_lookback_days", 20) - self.volume_cache_key = discovery_config.get("volume_cache_key", "default") - self.filter_same_day_movers = discovery_config.get("filter_same_day_movers", True) - self.intraday_movement_threshold = discovery_config.get("intraday_movement_threshold", 15.0) - - # Earnings discovery limits - self.max_earnings_candidates = discovery_config.get("max_earnings_candidates", 50) - self.max_days_until_earnings = discovery_config.get("max_days_until_earnings", 7) - self.min_market_cap = discovery_config.get( - "min_market_cap", 0 - ) # In billions, 0 = no filter - - # News settings - self.news_lookback_days = discovery_config.get("news_lookback_days", 7) - self.batch_news_vendor = discovery_config.get("batch_news_vendor", "openai") - self.batch_news_batch_size = discovery_config.get("batch_news_batch_size", 50) - - # Discovery mode: "traditional", "semantic", or "hybrid" - self.discovery_mode = discovery_config.get("discovery_mode", "hybrid") - - # Semantic discovery settings - self.semantic_news_sources = discovery_config.get("semantic_news_sources", ["openai"]) - self.semantic_news_lookback_hours = discovery_config.get("semantic_news_lookback_hours", 24) - self.semantic_min_news_importance = discovery_config.get("semantic_min_news_importance", 5) - self.semantic_min_similarity = discovery_config.get("semantic_min_similarity", 0.2) - self.semantic_max_tickers_per_news = discovery_config.get( - "semantic_max_tickers_per_news", 5 - ) - - # Console price charts - self.console_price_charts = discovery_config.get("console_price_charts", False) - self.price_chart_library = discovery_config.get("price_chart_library", "plotille") - self.price_chart_windows = discovery_config.get("price_chart_windows", ["1m"]) - self.price_chart_lookback_days = discovery_config.get("price_chart_lookback_days", 30) - self.price_chart_width = discovery_config.get("price_chart_width", 60) - self.price_chart_height = discovery_config.get("price_chart_height", 12) - self.price_chart_max_tickers = discovery_config.get("price_chart_max_tickers", 10) - self.price_chart_show_movement_stats = discovery_config.get( - "price_chart_show_movement_stats", True - ) - - def _load_logging_config(self) -> None: - """Load logging configuration.""" - discovery_config = self.config.get("discovery", {}) - - self.log_tool_calls = discovery_config.get("log_tool_calls", True) - self.log_tool_calls_console = discovery_config.get("log_tool_calls_console", False) - self.tool_log_max_chars = discovery_config.get("tool_log_max_chars", 10_000) - self.tool_log_exclude = set(discovery_config.get("tool_log_exclude", [])) - - def _safe_serialize(self, value: Any) -> str: - """Safely serialize any value to a string.""" - return serialize_for_log(value) - def _log_tool_call( self, tool_logs: List[Dict[str, Any]], @@ -185,7 +111,7 @@ class DiscoveryGraph: """ from datetime import datetime - output_str = self._safe_serialize(output) + output_str = serialize_for_log(output) log_entry = { "timestamp": datetime.now().isoformat(), @@ -202,12 +128,10 @@ class DiscoveryGraph: tool_logs.append(log_entry) if self.log_tool_calls_console: - import logging - output_preview = output_str if self.tool_log_max_chars and len(output_preview) > self.tool_log_max_chars: output_preview = output_preview[: self.tool_log_max_chars] + "..." - logging.getLogger(__name__).info( + logger.info( "TOOL %s node=%s step=%s params=%s error=%s output=%s", tool_name, node, @@ -301,109 +225,13 @@ class DiscoveryGraph: return workflow.compile() - def semantic_scanner_node(self, state: DiscoveryState) -> Dict[str, Any]: - """ - Scan market using semantic news-ticker matching. - - Uses news semantic scanner to find tickers mentioned in or - semantically related to recent market-moving news. - - Args: - state: Current discovery state - - Returns: - Updated state with semantic candidates - """ - print("🔍 Scanning market with semantic discovery...") - - # Update performance tracking for historical recommendations (runs before discovery) + def _update_performance_tracking(self) -> None: + """Update performance tracking for historical recommendations (runs before discovery).""" try: self.analytics.update_performance_tracking() except Exception as e: - print(f" Warning: Performance tracking update failed: {e}") - print(" Continuing with discovery...") - - tool_logs = state.setdefault("tool_logs", []) - - def log_callback(entry: Dict[str, Any]) -> None: - tool_logs.append(entry) - state["tool_logs"] = tool_logs - - try: - from tradingagents.dataflows.semantic_discovery import SemanticDiscovery - - # Build config for semantic discovery - semantic_config = { - "project_dir": self.config.get("project_dir", "."), - "use_openai_embeddings": True, - "news_sources": self.semantic_news_sources, - "max_news_items": 20, - "news_lookback_hours": self.semantic_news_lookback_hours, - "min_news_importance": self.semantic_min_news_importance, - "min_similarity_threshold": self.semantic_min_similarity, - "max_tickers_per_news": self.semantic_max_tickers_per_news, - "max_total_candidates": self.max_candidates_to_analyze, - "log_callback": log_callback, - } - - # Run semantic discovery - discovery = SemanticDiscovery(semantic_config) - ranked_candidates = discovery.discover() - - # Also get directly mentioned tickers from news (highest signal) - directly_mentioned = discovery.get_directly_mentioned_tickers() - - # Convert to candidate format - candidates = [] - - # Add directly mentioned tickers first (highest priority) - for ticker_info in directly_mentioned: - candidates.append( - { - "ticker": ticker_info["ticker"], - "source": self.SOURCE_NEWS_MENTION, - "context": f"Directly mentioned in news: {ticker_info['news_title']}", - "priority": self.PRIORITY_CRITICAL, # Direct mention = highest priority - "news_sentiment": ticker_info.get("sentiment", "neutral"), - "news_importance": ticker_info.get("importance", 5), - "news_context": [ticker_info], - } - ) - - # Add semantically matched tickers - for rank_info in ranked_candidates: - ticker = rank_info["ticker"] - news_matches = rank_info["news_matches"] - - # Combine all news titles for richer context - all_news_titles = "; ".join([n["news_title"] for n in news_matches[:3]]) - - candidates.append( - { - "ticker": ticker, - "source": self.SOURCE_SEMANTIC, - "context": f"News-driven: {all_news_titles}", - "priority": self.PRIORITY_HIGH, # News-driven is always high priority (leading indicator) - "semantic_score": rank_info["aggregate_score"], - "num_news_matches": rank_info["num_news_matches"], - "news_context": news_matches, # Store full news context for later - } - ) - - print(f" Found {len(candidates)} candidates from semantic discovery.") - - return { - "tickers": [c["ticker"] for c in candidates], - "candidate_metadata": candidates, - "tool_logs": state.get("tool_logs", []), - "status": "scanned", - } - - except Exception as e: - print(f" Error in semantic discovery: {e}") - print(" Falling back to traditional scanner...") - # Directly call traditional scanner to avoid recursion - return self.traditional_scanner_node(state) + logger.warning(f"Performance tracking update failed: {e}") + logger.warning("Continuing with discovery...") def _merge_candidates_into_dict( self, candidates: List[Dict[str, Any]], target_dict: Dict[str, Dict[str, Any]] @@ -422,101 +250,55 @@ class DiscoveryGraph: ticker = candidate["ticker"] if ticker not in target_dict: - self._add_new_candidate(candidate, target_dict) + # First time seeing this ticker - initialize tracking fields + entry = candidate.copy() + source = candidate.get("source", self.SOURCE_UNKNOWN) + context = candidate.get("context", "").strip() + entry["all_sources"] = [source] + entry["all_contexts"] = [context] if context else [] + target_dict[ticker] = entry else: - self._merge_with_existing_candidate(candidate, target_dict[ticker]) + # Duplicate ticker - merge sources, contexts, and priority + existing = target_dict[ticker] + existing.setdefault("all_sources", [existing.get("source", self.SOURCE_UNKNOWN)]) + existing.setdefault( + "all_contexts", + [existing.get("context", "")] if existing.get("context") else [], + ) - def _add_new_candidate( - self, candidate: Dict[str, Any], target_dict: Dict[str, Dict[str, Any]] + incoming_source = candidate.get("source", self.SOURCE_UNKNOWN) + if incoming_source not in existing["all_sources"]: + existing["all_sources"].append(incoming_source) + + incoming_context = candidate.get("context", "").strip() + incoming_rank = PRIORITY_ORDER.get( + candidate.get("priority", Priority.UNKNOWN.value), 4 + ) + existing_rank = PRIORITY_ORDER.get( + existing.get("priority", Priority.UNKNOWN.value), 4 + ) + + if incoming_rank < existing_rank: + # Higher priority incoming - upgrade and prepend context + existing["priority"] = candidate.get("priority") + existing["source"] = candidate.get("source") + self._add_context(incoming_context, existing, prepend=True) + else: + self._add_context(incoming_context, existing, prepend=False) + + def _add_context( + self, new_context: str, candidate: Dict[str, Any], *, prepend: bool ) -> None: """ - Add a new candidate to the target dictionary. + Add context string to a candidate's context fields. + + When prepend is True, the new context leads the combined string + (used when a higher-priority source is being merged in). Args: - candidate: Candidate dictionary to add - target_dict: Target dictionary to add to - """ - ticker = candidate["ticker"] - target_dict[ticker] = candidate.copy() - - source = candidate.get("source", self.SOURCE_UNKNOWN) - context = candidate.get("context", "").strip() - - target_dict[ticker]["all_sources"] = [source] - target_dict[ticker]["all_contexts"] = [context] if context else [] - - def _merge_with_existing_candidate( - self, incoming: Dict[str, Any], existing: Dict[str, Any] - ) -> None: - """ - Merge incoming candidate data with existing candidate. - - Args: - incoming: New candidate data to merge - existing: Existing candidate data to update - """ - # Initialize list fields if needed - existing.setdefault("all_sources", [existing.get("source", self.SOURCE_UNKNOWN)]) - existing.setdefault( - "all_contexts", [existing.get("context", "")] if existing.get("context") else [] - ) - - # Update sources - incoming_source = incoming.get("source", self.SOURCE_UNKNOWN) - if incoming_source not in existing["all_sources"]: - existing["all_sources"].append(incoming_source) - - # Update priority and contexts based on priority ranking - self._update_priority_and_context(incoming, existing) - - def _update_priority_and_context( - self, incoming: Dict[str, Any], existing: Dict[str, Any] - ) -> None: - """ - Update priority and context based on incoming candidate priority. - - If incoming has higher priority, upgrades existing candidate. - Otherwise, just appends context. - - Args: - incoming: New candidate data - existing: Existing candidate data to update - """ - incoming_rank = self.PRIORITY_ORDER.get(incoming.get("priority", self.PRIORITY_UNKNOWN), 4) - existing_rank = self.PRIORITY_ORDER.get(existing.get("priority", self.PRIORITY_UNKNOWN), 4) - incoming_context = incoming.get("context", "").strip() - - if incoming_rank < existing_rank: - # Higher priority - upgrade and prepend context - existing["priority"] = incoming.get("priority") - existing["source"] = incoming.get("source") - self._prepend_context(incoming_context, existing) - else: - # Same or lower priority - just append context - self._append_context(incoming_context, existing) - - def _prepend_context(self, new_context: str, candidate: Dict[str, Any]) -> None: - """ - Prepend context to existing candidate (for higher priority updates). - - Args: - new_context: New context string to prepend - candidate: Candidate dictionary to update - """ - if not new_context: - return - - candidate["all_contexts"].append(new_context) - current_ctx = candidate.get("context", "") - candidate["context"] = f"{new_context}; Also: {current_ctx}" if current_ctx else new_context - - def _append_context(self, new_context: str, candidate: Dict[str, Any]) -> None: - """ - Append context to existing candidate (for same/lower priority updates). - - Args: - new_context: New context string to append + new_context: New context string to add candidate: Candidate dictionary to update + prepend: If True, new context leads the combined string """ if not new_context or new_context in candidate["all_contexts"]: return @@ -527,7 +309,10 @@ class DiscoveryGraph: if not current_ctx: candidate["context"] = new_context elif new_context not in current_ctx: - candidate["context"] = f"{current_ctx}; Also: {new_context}" + if prepend: + candidate["context"] = f"{new_context}; Also: {current_ctx}" + else: + candidate["context"] = f"{current_ctx}; Also: {new_context}" def scanner_node(self, state: DiscoveryState) -> Dict[str, Any]: """ @@ -542,16 +327,9 @@ class DiscoveryGraph: Returns: Updated state with discovered candidates """ - print("Scanning market for opportunities...") + logger.info("Scanning market for opportunities...") - # Update performance tracking for historical recommendations (runs before discovery) - try: - self.analytics.update_performance_tracking() - except Exception as e: - print(f" Warning: Performance tracking update failed: {e}") - print(" Continuing with discovery...") - - # Initialize tool_logs in state + self._update_performance_tracking() state.setdefault("tool_logs", []) # Get execution config @@ -570,7 +348,7 @@ class DiscoveryGraph: # Check if scanner's pipeline is enabled if not pipeline_config.get(pipeline, {}).get("enabled", True): - print(f" Skipping {scanner_class.name} (pipeline '{pipeline}' disabled)") + logger.info(f"Skipping {scanner_class.name} (pipeline '{pipeline}' disabled)") continue try: @@ -579,13 +357,13 @@ class DiscoveryGraph: # Check if scanner is enabled if not scanner.is_enabled(): - print(f" Skipping {scanner_class.name} (scanner disabled)") + logger.info(f"Skipping {scanner_class.name} (scanner disabled)") continue enabled_scanners.append((scanner, scanner_class.name, pipeline)) except Exception as e: - print(f" Error instantiating {scanner_class.name}: {e}") + logger.error(f"Error instantiating {scanner_class.name}: {e}") continue # Run scanners concurrently or sequentially based on config @@ -605,7 +383,7 @@ class DiscoveryGraph: final_candidates = list(all_candidates_dict.values()) final_tickers = [c["ticker"] for c in final_candidates] - print(f" Found {len(final_candidates)} unique candidates from all scanners.") + logger.info(f"Found {len(final_candidates)} unique candidates from all scanners.") # Return state with tickers, candidate_metadata, tool_logs, status return { @@ -640,15 +418,15 @@ class DiscoveryGraph: state["tool_executor"] = self._execute_tool_logged # Call scanner.scan_with_validation(state) - print(f" Running {name}...") + logger.info(f"Running {name}...") candidates = scanner.scan_with_validation(state) # Route candidates to appropriate pipeline pipeline_candidates[pipeline].extend(candidates) - print(f" Found {len(candidates)} candidates") + logger.info(f"Found {len(candidates)} candidates") except Exception as e: - print(f" Error in {name}: {e}") + logger.error(f"Error in {name}: {e}") continue return pipeline_candidates @@ -672,14 +450,12 @@ class DiscoveryGraph: Returns: Dict mapping pipeline -> list of candidates """ - import logging from concurrent.futures import ThreadPoolExecutor, TimeoutError, as_completed - logger = logging.getLogger(__name__) pipeline_candidates: Dict[str, List[Dict[str, Any]]] = {} - print( - f" Running {len(enabled_scanners)} scanners concurrently (max {max_workers} workers)..." + logger.info( + f"Running {len(enabled_scanners)} scanners concurrently (max {max_workers} workers)..." ) def run_scanner(scanner_info: tuple) -> tuple: @@ -688,20 +464,19 @@ class DiscoveryGraph: try: # Create a copy of state for thread safety scanner_state = state.copy() + scanner_state["tool_logs"] = [] # Fresh log list scanner_state["tool_executor"] = self._execute_tool_logged # Run scanner with validation candidates = scanner.scan_with_validation(scanner_state) - # Merge tool_logs back into main state (thread-safe append) - if "tool_logs" in scanner_state: - state.setdefault("tool_logs", []).extend(scanner_state["tool_logs"]) - - return (name, pipeline, candidates, None) + # Return logs to be merged later (not in-place) + scanner_logs = scanner_state.get("tool_logs", []) + return (name, pipeline, candidates, None, scanner_logs) except Exception as e: logger.error(f"Scanner {name} failed: {e}", exc_info=True) - return (name, pipeline, [], str(e)) + return (name, pipeline, [], str(e), []) # Submit all scanner tasks with ThreadPoolExecutor(max_workers=max_workers) as executor: @@ -717,25 +492,28 @@ class DiscoveryGraph: try: # Get result with per-scanner timeout - name, pipeline, candidates, error = future.result(timeout=timeout_seconds) + name, pipeline, candidates, error, scanner_logs = future.result(timeout=timeout_seconds) # Initialize pipeline list if needed if pipeline not in pipeline_candidates: pipeline_candidates[pipeline] = [] if error: - print(f" ⚠️ {name}: {error}") + logger.warning(f"⚠️ {name}: {error}") else: pipeline_candidates[pipeline].extend(candidates) - print(f" ✓ {name}: {len(candidates)} candidates") + logger.info(f"✓ {name}: {len(candidates)} candidates") + + # Thread-safe log merging + if scanner_logs: + with self._tool_logs_lock: + state.setdefault("tool_logs", []).extend(scanner_logs) except TimeoutError: - logger.warning(f"Scanner {scanner_name} timed out after {timeout_seconds}s") - print(f" ⏱️ {scanner_name}: timeout after {timeout_seconds}s") + logger.warning(f"⏱️ {scanner_name}: timeout after {timeout_seconds}s") except Exception as e: - logger.error(f"Scanner {scanner_name} failed unexpectedly: {e}", exc_info=True) - print(f" ⚠️ {scanner_name}: unexpected error") + logger.error(f"⚠️ {scanner_name}: unexpected error - {e}", exc_info=True) finally: completed_count += 1 @@ -746,246 +524,6 @@ class DiscoveryGraph: return pipeline_candidates - def hybrid_scanner_node(self, state: DiscoveryState) -> Dict[str, Any]: - """ - Run both semantic and traditional discovery with smart deduplication. - - Combines news-driven semantic discovery (leading indicators) with - traditional discovery (social, market movers, earnings). Merges - results and boosts candidates confirmed by multiple sources. - - Args: - state: Current discovery state - - Returns: - Updated state with merged candidates from both approaches - """ - print("🔍 Hybrid Discovery: Combining news-driven AND traditional signals...") - - # Update performance tracking once (not in each sub-scanner) - try: - self.analytics.update_performance_tracking() - except Exception as e: - print(f" Warning: Performance tracking update failed: {e}") - print(" Continuing with discovery...") - - tool_logs = state.setdefault("tool_logs", []) - - def log_callback(entry: Dict[str, Any]) -> None: - tool_logs.append(entry) - state["tool_logs"] = tool_logs - - # We will merge all candidates into this dict - unique_candidates = {} - all_tickers = set() - - # ======================================== - # Phase 1: Semantic Discovery (news-driven - leading indicators) - # ======================================== - print("\n📰 Phase 1: Semantic Discovery (news-driven)...") - try: - from tradingagents.dataflows.semantic_discovery import SemanticDiscovery - - # Build config for semantic discovery - semantic_config = { - "project_dir": self.config.get("project_dir", "."), - "use_openai_embeddings": True, - "news_sources": self.semantic_news_sources, - "max_news_items": 20, - "news_lookback_hours": self.semantic_news_lookback_hours, - "min_news_importance": self.semantic_min_news_importance, - "min_similarity_threshold": self.semantic_min_similarity, - "max_tickers_per_news": self.semantic_max_tickers_per_news, - "max_total_candidates": self.max_candidates_to_analyze, - "log_callback": log_callback, - } - - # Run semantic discovery - discovery = SemanticDiscovery(semantic_config) - ranked_candidates = discovery.discover() - - # Also get directly mentioned tickers from news (highest signal) - directly_mentioned = discovery.get_directly_mentioned_tickers() - - # Prepare semantic candidates list - semantic_candidates = [] - - # Add directly mentioned tickers first (highest priority) - for ticker_info in directly_mentioned: - semantic_candidates.append( - { - "ticker": ticker_info["ticker"], - "source": self.SOURCE_NEWS_MENTION, - "context": f"Directly mentioned in news: {ticker_info['news_title']}", - "priority": self.PRIORITY_CRITICAL, # Direct mention = highest priority - "news_sentiment": ticker_info.get("sentiment", "neutral"), - "news_importance": ticker_info.get("importance", 5), - "news_context": [ticker_info], - } - ) - all_tickers.add(ticker_info["ticker"]) - - # Add semantically matched tickers - for rank_info in ranked_candidates: - ticker = rank_info["ticker"] - news_matches = rank_info["news_matches"] - - # Combine all news titles for richer context - all_news_titles = "; ".join([n["news_title"] for n in news_matches[:3]]) - - semantic_candidates.append( - { - "ticker": ticker, - "source": self.SOURCE_SEMANTIC, - "context": f"News-driven: {all_news_titles}", - "priority": self.PRIORITY_HIGH, # News-driven is always high priority (leading indicator) - "semantic_score": rank_info["aggregate_score"], - "num_news_matches": rank_info["num_news_matches"], - "news_context": news_matches, - } - ) - all_tickers.add(ticker) - - print(f" Found {len(semantic_candidates)} candidates from semantic discovery") - - # Merge semantic candidates into unique dict - self._merge_candidates_into_dict(semantic_candidates, unique_candidates) - - except Exception as e: - print(f" Semantic discovery failed: {e}") - print(" Continuing with traditional discovery...") - - # ======================================== - # Phase 2: Traditional Discovery (social, market movers, etc.) - # ======================================== - print("\n📊 Phase 2: Traditional Discovery (Reddit, market movers, earnings, etc.)...") - traditional_candidates = self._run_traditional_scanners(state) - print(f" Found {len(traditional_candidates)} candidates from traditional discovery") - - # Merge traditional candidates into unique dict - self._merge_candidates_into_dict(traditional_candidates, unique_candidates) - - # ======================================== - # Phase 3: Post-Merge Processing - # ======================================== - print("\n🔄 Phase 3: Finalizing candidates...") - - final_candidates = list(unique_candidates.values()) - - # Check for multi-source confirmation - semantic_sources = {self.SOURCE_SEMANTIC, self.SOURCE_NEWS_MENTION} - - for c in final_candidates: - sources = c.get("all_sources", []) - has_semantic = any(s in semantic_sources for s in sources) - has_traditional = any( - s not in semantic_sources and s != self.SOURCE_UNKNOWN for s in sources - ) - - if has_semantic and has_traditional: - # Found by BOTH semantic and traditional - boost confidence - c["multi_source_confirmed"] = True - if c.get("priority") == self.PRIORITY_HIGH: - c["priority"] = self.PRIORITY_CRITICAL # Upgrade to critical - - # Sort by priority - final_candidates.sort( - key=lambda x: self.PRIORITY_ORDER.get(x.get("priority", self.PRIORITY_UNKNOWN), 4) - ) - - # Update all_tickers set - all_tickers = {c["ticker"] for c in final_candidates} - - # Count by priority for reporting - critical_count = sum( - 1 for c in final_candidates if c.get("priority") == self.PRIORITY_CRITICAL - ) - high_count = sum(1 for c in final_candidates if c.get("priority") == self.PRIORITY_HIGH) - medium_count = sum(1 for c in final_candidates if c.get("priority") == self.PRIORITY_MEDIUM) - low_count = sum(1 for c in final_candidates if c.get("priority") == self.PRIORITY_LOW) - multi_confirmed = sum(1 for c in final_candidates if c.get("multi_source_confirmed")) - - print(f"\n✅ Hybrid discovery complete: {len(final_candidates)} total candidates") - print( - f" Priority: {critical_count} critical, {high_count} high, {medium_count} medium, {low_count} low" - ) - if multi_confirmed: - print( - f" 🎯 {multi_confirmed} candidates confirmed by BOTH semantic AND traditional sources" - ) - - return { - "tickers": list(all_tickers), - "candidate_metadata": final_candidates, - "tool_logs": state.get("tool_logs", []), - "status": "scanned", - } - - def _run_traditional_scanners(self, state: DiscoveryState) -> List[Dict[str, Any]]: - """ - Run all traditional scanner sources and return candidates. - - Traditional sources include: - - Reddit trending - - Market movers - - Earnings calendar - - IPO calendar - - Short interest - - Unusual volume - - Analyst rating changes - - Insider buying - - Args: - state: Current discovery state - - Returns: - List of candidates (without deduplication) - """ - from tradingagents.dataflows.discovery.scanners import TraditionalScanner - - scanner = TraditionalScanner( - config=self.config, llm=self.quick_thinking_llm, tool_executor=self._execute_tool_logged - ) - return scanner.scan(state) - - def traditional_scanner_node(self, state: DiscoveryState) -> Dict[str, Any]: - """ - Traditional market scanning: Reddit, market movers, earnings, etc. - - Args: - state: Current discovery state - - Returns: - Updated state with traditional candidates - """ - print("🔍 Scanning market for opportunities...") - - # Update performance tracking for historical recommendations (runs before discovery) - try: - self.analytics.update_performance_tracking() - except Exception as e: - print(f" Warning: Performance tracking update failed: {e}") - print(" Continuing with discovery...") - - state.setdefault("tool_logs", []) - - # Run all traditional scanners - candidates = self._run_traditional_scanners(state) - - # Deduplicate candidates - unique_candidates = {} - self._merge_candidates_into_dict(candidates, unique_candidates) - - final_candidates = list(unique_candidates.values()) - print(f" Found {len(final_candidates)} unique candidates.") - - return { - "tickers": [c["ticker"] for c in final_candidates], - "candidate_metadata": final_candidates, - "tool_logs": state.get("tool_logs", []), - "status": "scanned", - } - def filter_node(self, state: DiscoveryState) -> Dict[str, Any]: """ Filter candidates and enrich with additional data. @@ -1051,9 +589,9 @@ class DiscoveryGraph: trade_date = resolve_trade_date_str({"trade_date": trade_date}) - print(f"\n{'='*60}") - print(f"Discovery Analysis - {trade_date}") - print(f"{'='*60}") + logger.info(f"\n{'='*60}") + logger.info(f"Discovery Analysis - {trade_date}") + logger.info(f"{'='*60}") initial_state = { "trade_date": trade_date, @@ -1070,385 +608,74 @@ class DiscoveryGraph: self.analytics.save_discovery_results(final_state, trade_date, self.config) # Extract and save rankings if available - rankings = final_state.get("final_ranking", []) - if isinstance(rankings, str): - try: - import json - - rankings = json.loads(rankings) - except Exception: - rankings = [] - if rankings: - if isinstance(rankings, dict) and "rankings" in rankings: - rankings_list = rankings["rankings"] - elif isinstance(rankings, list): - rankings_list = rankings - else: - rankings_list = [] - - if rankings_list: - self.analytics.save_recommendations( - rankings_list, trade_date, self.config.get("llm_provider", "unknown") - ) + rankings_list = self._normalize_rankings(final_state.get("final_ranking", [])) + if rankings_list: + self.analytics.save_recommendations( + rankings_list, trade_date, self.config.get("llm_provider", "unknown") + ) return final_state + # ------------------------------------------------------------------ + # Price chart delegation (implementation in price_charts.py) + # ------------------------------------------------------------------ + + def _get_chart_builder(self) -> PriceChartBuilder: + """Lazily create and cache the PriceChartBuilder instance.""" + if not hasattr(self, "_chart_builder"): + from tradingagents.graph.price_charts import PriceChartBuilder + + c = self.dc.charts + self._chart_builder = PriceChartBuilder( + enabled=c.enabled, + library=c.library, + windows=c.windows, + lookback_days=c.lookback_days, + width=c.width, + height=c.height, + max_tickers=c.max_tickers, + show_movement_stats=c.show_movement_stats, + ) + return self._chart_builder + def build_price_chart_bundle(self, rankings: Any) -> Dict[str, Dict[str, Any]]: """Build per-ticker chart + movement stats for top recommendations.""" - if not self.console_price_charts: - return {} - - rankings_list = self._normalize_rankings(rankings) - tickers: List[str] = [] - for item in rankings_list: - ticker = (item.get("ticker") or "").upper() - if ticker and ticker not in tickers: - tickers.append(ticker) - - if not tickers: - return {} - - tickers = tickers[: self.price_chart_max_tickers] - chart_windows = self._get_chart_windows() - renderer = self._get_chart_renderer() - if renderer is None: - return {} - - bundle: Dict[str, Dict[str, Any]] = {} - for ticker in tickers: - series = self._fetch_price_series(ticker) - if not series: - bundle[ticker] = { - "chart": f"{ticker}: no price history available", - "charts": {}, - "movement": {}, - } - continue - - per_window_charts: Dict[str, str] = {} - for window in chart_windows: - window_closes = self._get_window_closes(ticker, series, window) - if len(window_closes) < 2: - continue - - change_pct = None - if window_closes[0]: - change_pct = (window_closes[-1] / window_closes[0] - 1) * 100.0 - - label = window.upper() - title = f"{ticker} ({label})" - if change_pct is not None: - title = f"{ticker} ({label}, {change_pct:+.1f}%)" - - chart_text = renderer(window_closes, title) - if chart_text: - per_window_charts[window] = chart_text - - primary_chart = "" - if per_window_charts: - first_key = chart_windows[0] - primary_chart = per_window_charts.get( - first_key, next(iter(per_window_charts.values())) - ) - - bundle[ticker] = { - "chart": primary_chart, - "charts": per_window_charts, - "movement": self._compute_movement_stats(series), - } - return bundle + return self._get_chart_builder().build_bundle(self._normalize_rankings(rankings)) def build_price_chart_map(self, rankings: Any) -> Dict[str, str]: """Build mini price charts keyed by ticker.""" - bundle = self.build_price_chart_bundle(rankings) - return {ticker: item.get("chart", "") for ticker, item in bundle.items()} + return self._get_chart_builder().build_map(self._normalize_rankings(rankings)) def build_price_chart_strings(self, rankings: Any) -> List[str]: """Build mini price charts for top recommendations (returns ANSI strings).""" - charts = self.build_price_chart_map(rankings) - return list(charts.values()) if charts else [] + return self._get_chart_builder().build_strings(self._normalize_rankings(rankings)) def _print_price_charts(self, rankings_list: List[Dict[str, Any]]) -> None: """Render mini price charts for top recommendations in the console.""" - charts = self.build_price_chart_strings(rankings_list) - if not charts: - return - - print(f"\n📈 Price Charts (last {self.price_chart_lookback_days} days)") - for chart in charts: - print(chart) - - def _fetch_price_series(self, ticker: str) -> List[Dict[str, Any]]: - """Fetch recent daily close prices with dates for charting and movement stats.""" - try: - import pandas as pd - import yfinance as yf - - from tradingagents.dataflows.y_finance import suppress_yfinance_warnings - - history_days = max(self.price_chart_lookback_days + 10, 390) - with suppress_yfinance_warnings(): - data = yf.download( - ticker, - period=f"{history_days}d", - interval="1d", - auto_adjust=True, - progress=False, - ) - - if data is None or data.empty: - return [] - - series = None - if isinstance(data.columns, pd.MultiIndex): - if "Close" in data.columns.get_level_values(0): - close_data = data["Close"] - series = ( - close_data.iloc[:, 0] - if isinstance(close_data, pd.DataFrame) - else close_data - ) - elif "Close" in data.columns: - series = data["Close"] - - if series is None: - series = data.iloc[:, 0] - - if isinstance(series, pd.DataFrame): - series = series.iloc[:, 0] - - series = series.dropna() - if series.empty: - return [] - - points: List[Dict[str, Any]] = [] - for index, close in series.items(): - dt = getattr(index, "to_pydatetime", lambda: index)() - points.append({"date": dt, "close": float(close)}) - return points - except Exception as exc: - print(f" {ticker}: error fetching prices: {exc}") - return [] - - def _get_chart_renderer(self) -> Optional[Callable[[List[float], str], str]]: - """Return selected chart renderer, with fallback to plotext.""" - preferred = str(self.price_chart_library or "plotext").lower().strip() - - if preferred == "plotille": - try: - import plotille - - return lambda closes, title: self._render_plotille_chart(plotille, closes, title) - except Exception as exc: - print(f" ⚠️ plotille unavailable, falling back to plotext: {exc}") - - try: - import plotext as plt - - return lambda closes, title: self._render_plotext_chart(plt, closes, title) - except Exception as exc: - print(f" ⚠️ plotext not available, skipping charts: {exc}") - return None - - def _render_plotille_chart(self, plotille: Any, closes: List[float], title: str) -> str: - """Build a plotille chart and return as ANSI string.""" - if not closes: - return "" - - fig = plotille.Figure() - fig.width = self.price_chart_width - fig.height = self.price_chart_height - fig.color_mode = "byte" - fig.set_x_limits(min_=0, max_=max(1, len(closes) - 1)) - - min_close = min(closes) - max_close = max(closes) - if min_close == max_close: - padding = max(0.01, min_close * 0.01) - min_close -= padding - max_close += padding - fig.set_y_limits(min_=min_close, max_=max_close) - fig.plot(range(len(closes)), closes, lc=45) - - return f"{title}\n{fig.show(legend=False)}" - - def _render_plotext_chart(self, plt: Any, closes: List[float], title: str) -> str: - """Build a single plotext line chart and return as ANSI string.""" - self._reset_plotext(plt) - - if hasattr(plt, "plotsize"): - plt.plotsize(self.price_chart_width, self.price_chart_height) - - if hasattr(plt, "theme"): - try: - plt.theme("pro") - except Exception: - pass - - if hasattr(plt, "title"): - plt.title(title) - - if hasattr(plt, "xlabel"): - plt.xlabel("") - if hasattr(plt, "ylabel"): - plt.ylabel("") - - plt.plot(closes) - - if hasattr(plt, "build"): - chart = plt.build() - if chart: - return chart - - plt.show() - return "" - - def _compute_movement_stats(self, series: List[Dict[str, Any]]) -> Dict[str, Optional[float]]: - """Compute 1D, 7D, 6M, and 1Y percent movement from latest close.""" - if not series: - return {} - - from datetime import timedelta - - latest = series[-1] - latest_date = latest["date"] - latest_close = latest["close"] - - if not latest_close: - return {} - - windows = { - "1d": timedelta(days=1), - "7d": timedelta(days=7), - "1m": timedelta(days=30), - "6m": timedelta(days=182), - "1y": timedelta(days=365), - } - - stats: Dict[str, Optional[float]] = {} - for label, delta in windows.items(): - target_date = latest_date - delta - baseline = None - for point in series: - if point["date"] <= target_date: - baseline = point["close"] - else: - break - - if baseline and baseline != 0: - stats[label] = (latest_close / baseline - 1.0) * 100.0 - else: - stats[label] = None - return stats - - def _get_chart_windows(self) -> List[str]: - """Normalize configured chart windows.""" - allowed = {"1d", "7d", "1m", "6m", "1y"} - configured = self.price_chart_windows - if isinstance(configured, str): - configured = [part.strip().lower() for part in configured.split(",")] - elif not isinstance(configured, list): - configured = ["1m"] - - windows = [] - for value in configured: - key = str(value).strip().lower() - if key in allowed and key not in windows: - windows.append(key) - return windows or ["1m"] - - def _get_window_closes( - self, ticker: str, series: List[Dict[str, Any]], window: str - ) -> List[float]: - """Return closes for a given chart window.""" - if not series: - return [] - - from datetime import timedelta - - if window == "1d": - intraday = self._fetch_intraday_closes(ticker) - if len(intraday) >= 2: - return intraday - # fallback to last 2 daily points if intraday unavailable - return [point["close"] for point in series[-2:]] - - window_days = { - "7d": 7, - "1m": 30, - "6m": 182, - "1y": 365, - }.get(window, self.price_chart_lookback_days) - - latest_date = series[-1]["date"] - cutoff = latest_date - timedelta(days=window_days) - closes = [point["close"] for point in series if point["date"] >= cutoff] - return closes - - def _fetch_intraday_closes(self, ticker: str) -> List[float]: - """Fetch intraday close prices for 1-day chart window.""" - try: - import pandas as pd - import yfinance as yf - - from tradingagents.dataflows.y_finance import suppress_yfinance_warnings - - with suppress_yfinance_warnings(): - data = yf.download( - ticker, - period="1d", - interval="15m", - auto_adjust=True, - progress=False, - ) - - if data is None or data.empty: - return [] - - series = None - if isinstance(data.columns, pd.MultiIndex): - if "Close" in data.columns.get_level_values(0): - close_data = data["Close"] - series = ( - close_data.iloc[:, 0] - if isinstance(close_data, pd.DataFrame) - else close_data - ) - elif "Close" in data.columns: - series = data["Close"] - - if series is None: - series = data.iloc[:, 0] - - if isinstance(series, pd.DataFrame): - series = series.iloc[:, 0] - - return [float(value) for value in series.dropna().to_list()] - except Exception: - return [] + self._get_chart_builder().print_charts(rankings_list) @staticmethod def _normalize_rankings(rankings: Any) -> List[Dict[str, Any]]: """Normalize ranking payload into a list of ranking dicts.""" - rankings_list: List[Dict[str, Any]] = [] if isinstance(rankings, str): try: import json - rankings = json.loads(rankings) - except Exception: - rankings = [] + parsed = json.loads(rankings) + # Validate parsed result is expected type + if isinstance(parsed, dict): + return parsed.get("rankings", []) + elif isinstance(parsed, list): + return parsed + else: + logger.warning(f"Unexpected JSON type after parsing: {type(parsed)}") + return [] + except Exception as e: + logger.warning(f"Failed to parse rankings JSON: {e}") + return [] if isinstance(rankings, dict): - rankings_list = rankings.get("rankings", []) - elif isinstance(rankings, list): - rankings_list = rankings - return rankings_list - - @staticmethod - def _reset_plotext(plt: Any) -> None: - """Clear plotext state between charts.""" - for method in ("clf", "clear_figure", "clear_data"): - func = getattr(plt, method, None) - if callable(func): - func() - return + return rankings.get("rankings", []) + if isinstance(rankings, list): + return rankings + logger.warning(f"Unexpected rankings type: {type(rankings)}") + return [] diff --git a/tradingagents/graph/price_charts.py b/tradingagents/graph/price_charts.py new file mode 100644 index 00000000..aed7d84b --- /dev/null +++ b/tradingagents/graph/price_charts.py @@ -0,0 +1,388 @@ +"""Price chart building and rendering for discovery recommendations. + +Extracts all chart-related logic (fetching price data, rendering charts, +computing movement stats) into a standalone class so that DiscoveryGraph +stays focused on orchestration. +""" + +from datetime import timedelta +from typing import Any, Callable, Dict, List, Optional + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +class PriceChartBuilder: + """Builds per-ticker console price charts and movement statistics.""" + + def __init__( + self, + *, + enabled: bool = False, + library: str = "plotille", + windows: Any = None, + lookback_days: int = 30, + width: int = 60, + height: int = 12, + max_tickers: int = 10, + show_movement_stats: bool = True, + ) -> None: + self.enabled = enabled + self.library = library + self.raw_windows = windows if windows is not None else ["1m"] + self.lookback_days = lookback_days + self.width = width + self.height = height + self.max_tickers = max_tickers + self.show_movement_stats = show_movement_stats + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def build_bundle(self, rankings_list: List[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]: + """Build per-ticker chart + movement stats for top recommendations.""" + if not self.enabled: + return {} + + tickers = _unique_tickers(rankings_list, self.max_tickers) + if not tickers: + return {} + + chart_windows = self._normalize_windows() + renderer = self._get_renderer() + if renderer is None: + return {} + + bundle: Dict[str, Dict[str, Any]] = {} + for ticker in tickers: + series = self._fetch_price_series(ticker) + if not series: + bundle[ticker] = { + "chart": f"{ticker}: no price history available", + "charts": {}, + "movement": {}, + } + continue + + per_window_charts: Dict[str, str] = {} + for window in chart_windows: + window_closes = self._get_window_closes(ticker, series, window) + if len(window_closes) < 2: + continue + + change_pct = None + if window_closes[0]: + change_pct = (window_closes[-1] / window_closes[0] - 1) * 100.0 + + label = window.upper() + title = f"{ticker} ({label})" + if change_pct is not None: + title = f"{ticker} ({label}, {change_pct:+.1f}%)" + + chart_text = renderer(window_closes, title) + if chart_text: + per_window_charts[window] = chart_text + + primary_chart = "" + if per_window_charts: + first_key = chart_windows[0] + primary_chart = per_window_charts.get( + first_key, next(iter(per_window_charts.values())) + ) + + bundle[ticker] = { + "chart": primary_chart, + "charts": per_window_charts, + "movement": _compute_movement_stats(series), + } + return bundle + + def build_map(self, rankings_list: List[Dict[str, Any]]) -> Dict[str, str]: + """Build mini price charts keyed by ticker.""" + bundle = self.build_bundle(rankings_list) + return {ticker: item.get("chart", "") for ticker, item in bundle.items()} + + def build_strings(self, rankings_list: List[Dict[str, Any]]) -> List[str]: + """Build mini price charts for top recommendations (returns ANSI strings).""" + charts = self.build_map(rankings_list) + return list(charts.values()) if charts else [] + + def print_charts(self, rankings_list: List[Dict[str, Any]]) -> None: + """Render mini price charts for top recommendations in the console.""" + charts = self.build_strings(rankings_list) + if not charts: + return + + logger.info(f"📈 Price Charts (last {self.lookback_days} days)") + for chart in charts: + logger.info(chart) + + # ------------------------------------------------------------------ + # Data fetching + # ------------------------------------------------------------------ + + def _fetch_price_series(self, ticker: str) -> List[Dict[str, Any]]: + """Fetch recent daily close prices with dates for charting and movement stats.""" + try: + from tradingagents.dataflows.y_finance import download_history + + history_days = max(self.lookback_days + 10, 390) + data = download_history( + ticker, + period=f"{history_days}d", + interval="1d", + auto_adjust=True, + progress=False, + ) + + series = _extract_close_series(data) + if series is None: + return [] + + points: List[Dict[str, Any]] = [] + for idx, close in series.items(): + dt = getattr(idx, "to_pydatetime", lambda: idx)() + points.append({"date": dt, "close": float(close)}) + return points + except Exception as exc: + logger.error(f"{ticker}: error fetching prices: {exc}") + return [] + + def _fetch_intraday_closes(self, ticker: str) -> List[float]: + """Fetch intraday close prices for 1-day chart window.""" + try: + from tradingagents.dataflows.y_finance import download_history + + data = download_history( + ticker, + period="1d", + interval="15m", + auto_adjust=True, + progress=False, + ) + + series = _extract_close_series(data) + if series is None: + return [] + + return [float(value) for value in series.to_list()] + except Exception: + return [] + + # ------------------------------------------------------------------ + # Window / renderer helpers + # ------------------------------------------------------------------ + + def _normalize_windows(self) -> List[str]: + """Normalize configured chart windows.""" + allowed = {"1d", "7d", "1m", "6m", "1y"} + configured = self.raw_windows + if isinstance(configured, str): + configured = [part.strip().lower() for part in configured.split(",")] + elif not isinstance(configured, list): + configured = ["1m"] + + windows: List[str] = [] + for value in configured: + key = str(value).strip().lower() + if key in allowed and key not in windows: + windows.append(key) + return windows or ["1m"] + + def _get_window_closes( + self, ticker: str, series: List[Dict[str, Any]], window: str + ) -> List[float]: + """Return closes for a given chart window.""" + if not series: + return [] + + if window == "1d": + intraday = self._fetch_intraday_closes(ticker) + if len(intraday) >= 2: + return intraday + return [point["close"] for point in series[-2:]] + + window_days = { + "7d": 7, + "1m": 30, + "6m": 182, + "1y": 365, + }.get(window, self.lookback_days) + + latest_date = series[-1]["date"] + cutoff = latest_date - timedelta(days=window_days) + return [point["close"] for point in series if point["date"] >= cutoff] + + def _get_renderer(self) -> Optional[Callable[[List[float], str], str]]: + """Return selected chart renderer, with fallback to plotext.""" + preferred = str(self.library or "plotext").lower().strip() + + if preferred == "plotille": + try: + import plotille + + return lambda closes, title: self._render_plotille(plotille, closes, title) + except Exception as exc: + logger.warning(f"⚠️ plotille unavailable, falling back to plotext: {exc}") + + try: + import plotext as plt + + return lambda closes, title: self._render_plotext(plt, closes, title) + except Exception as exc: + logger.warning(f"⚠️ plotext not available, skipping charts: {exc}") + return None + + # ------------------------------------------------------------------ + # Renderers + # ------------------------------------------------------------------ + + def _render_plotille(self, plotille: Any, closes: List[float], title: str) -> str: + """Build a plotille chart and return as ANSI string.""" + if not closes: + return "" + + fig = plotille.Figure() + fig.width = self.width + fig.height = self.height + fig.color_mode = "byte" + fig.set_x_limits(min_=0, max_=max(1, len(closes) - 1)) + + min_close = min(closes) + max_close = max(closes) + if min_close == max_close: + padding = max(0.01, min_close * 0.01) + min_close -= padding + max_close += padding + fig.set_y_limits(min_=min_close, max_=max_close) + fig.plot(range(len(closes)), closes, lc=45) + + return f"{title}\n{fig.show(legend=False)}" + + def _render_plotext(self, plt: Any, closes: List[float], title: str) -> str: + """Build a single plotext line chart and return as ANSI string.""" + _reset_plotext(plt) + + if hasattr(plt, "plotsize"): + plt.plotsize(self.width, self.height) + + if hasattr(plt, "theme"): + try: + plt.theme("pro") + except Exception: + pass + + if hasattr(plt, "title"): + plt.title(title) + + if hasattr(plt, "xlabel"): + plt.xlabel("") + if hasattr(plt, "ylabel"): + plt.ylabel("") + + plt.plot(closes) + + if hasattr(plt, "build"): + chart = plt.build() + if chart: + return chart + + plt.show() + return "" + + +# ------------------------------------------------------------------ +# Module-level helpers (stateless) +# ------------------------------------------------------------------ + + +def _unique_tickers(rankings_list: List[Dict[str, Any]], limit: int) -> List[str]: + """Extract unique uppercase tickers from a rankings list, up to *limit*.""" + tickers: List[str] = [] + for item in rankings_list: + ticker = (item.get("ticker") or "").upper() + if ticker and ticker not in tickers: + tickers.append(ticker) + return tickers[:limit] + + +def _extract_close_series(data: Any) -> Any: + """ + Extract the Close column from a yfinance DataFrame, handling MultiIndex. + + Returns a pandas Series of close prices with NaNs dropped, or None if + the input is empty. + """ + import pandas as pd + + if data is None or data.empty: + return None + + series = None + if isinstance(data.columns, pd.MultiIndex): + if "Close" in data.columns.get_level_values(0): + close_data = data["Close"] + series = ( + close_data.iloc[:, 0] + if isinstance(close_data, pd.DataFrame) + else close_data + ) + elif "Close" in data.columns: + series = data["Close"] + + if series is None: + series = data.iloc[:, 0] + + if isinstance(series, pd.DataFrame): + series = series.iloc[:, 0] + + series = series.dropna() + return series if not series.empty else None + + +def _compute_movement_stats(series: List[Dict[str, Any]]) -> Dict[str, Optional[float]]: + """Compute 1D, 7D, 1M, 6M, and 1Y percent movement from latest close.""" + if not series: + return {} + + latest = series[-1] + latest_date = latest["date"] + latest_close = latest["close"] + + if not latest_close: + return {} + + windows = { + "1d": timedelta(days=1), + "7d": timedelta(days=7), + "1m": timedelta(days=30), + "6m": timedelta(days=182), + "1y": timedelta(days=365), + } + + stats: Dict[str, Optional[float]] = {} + for label, delta in windows.items(): + target_date = latest_date - delta + baseline = None + for point in series: + if point["date"] <= target_date: + baseline = point["close"] + else: + break + + if baseline and baseline != 0: + stats[label] = (latest_close / baseline - 1.0) * 100.0 + else: + stats[label] = None + return stats + + +def _reset_plotext(plt: Any) -> None: + """Clear plotext state between charts.""" + for method in ("clf", "clear_figure", "clear_data"): + func = getattr(plt, method, None) + if callable(func): + func() + return diff --git a/tradingagents/graph/propagation.py b/tradingagents/graph/propagation.py index 58ebd0a8..1612a11c 100644 --- a/tradingagents/graph/propagation.py +++ b/tradingagents/graph/propagation.py @@ -1,8 +1,8 @@ # TradingAgents/graph/propagation.py -from typing import Dict, Any +from typing import Any, Dict + from tradingagents.agents.utils.agent_states import ( - AgentState, InvestDebateState, RiskDebateState, ) @@ -15,9 +15,7 @@ class Propagator: """Initialize with configuration parameters.""" self.max_recur_limit = max_recur_limit - def create_initial_state( - self, company_name: str, trade_date: str - ) -> Dict[str, Any]: + def create_initial_state(self, company_name: str, trade_date: str) -> Dict[str, Any]: """Create the initial state for the agent graph.""" return { "messages": [("human", company_name)], diff --git a/tradingagents/graph/reflection.py b/tradingagents/graph/reflection.py index 33303231..86eefa87 100644 --- a/tradingagents/graph/reflection.py +++ b/tradingagents/graph/reflection.py @@ -1,6 +1,7 @@ # TradingAgents/graph/reflection.py -from typing import Dict, Any +from typing import Any, Dict + from langchain_openai import ChatOpenAI @@ -75,9 +76,7 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur situation = self._extract_current_situation(current_state) bull_debate_history = current_state["investment_debate_state"]["bull_history"] - result = self._reflect_on_component( - "BULL", bull_debate_history, situation, returns_losses - ) + result = self._reflect_on_component("BULL", bull_debate_history, situation, returns_losses) bull_memory.add_situations([(situation, result)]) def reflect_bear_researcher(self, current_state, returns_losses, bear_memory): @@ -85,9 +84,7 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur situation = self._extract_current_situation(current_state) bear_debate_history = current_state["investment_debate_state"]["bear_history"] - result = self._reflect_on_component( - "BEAR", bear_debate_history, situation, returns_losses - ) + result = self._reflect_on_component("BEAR", bear_debate_history, situation, returns_losses) bear_memory.add_situations([(situation, result)]) def reflect_trader(self, current_state, returns_losses, trader_memory): @@ -95,9 +92,7 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur situation = self._extract_current_situation(current_state) trader_decision = current_state["trader_investment_plan"] - result = self._reflect_on_component( - "TRADER", trader_decision, situation, returns_losses - ) + result = self._reflect_on_component("TRADER", trader_decision, situation, returns_losses) trader_memory.add_situations([(situation, result)]) def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory): @@ -115,7 +110,5 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur situation = self._extract_current_situation(current_state) judge_decision = current_state["risk_debate_state"]["judge_decision"] - result = self._reflect_on_component( - "RISK JUDGE", judge_decision, situation, returns_losses - ) + result = self._reflect_on_component("RISK JUDGE", judge_decision, situation, returns_losses) risk_manager_memory.add_situations([(situation, result)]) diff --git a/tradingagents/graph/setup.py b/tradingagents/graph/setup.py index b270ffc0..8a3b33c0 100644 --- a/tradingagents/graph/setup.py +++ b/tradingagents/graph/setup.py @@ -1,8 +1,9 @@ # TradingAgents/graph/setup.py -from typing import Dict, Any +from typing import Dict + from langchain_openai import ChatOpenAI -from langgraph.graph import END, StateGraph, START +from langgraph.graph import END, START, StateGraph from langgraph.prebuilt import ToolNode from tradingagents.agents import * @@ -37,9 +38,7 @@ class GraphSetup: self.risk_manager_memory = risk_manager_memory self.conditional_logic = conditional_logic - def setup_graph( - self, selected_analysts=["market", "social", "news", "fundamentals"] - ): + def setup_graph(self, selected_analysts=["market", "social", "news", "fundamentals"]): """Set up and compile the agent workflow graph. Args: @@ -58,40 +57,28 @@ class GraphSetup: tool_nodes = {} if "market" in selected_analysts: - analyst_nodes["market"] = create_market_analyst( - self.quick_thinking_llm - ) + analyst_nodes["market"] = create_market_analyst(self.quick_thinking_llm) delete_nodes["market"] = create_msg_delete() tool_nodes["market"] = self.tool_nodes["market"] if "social" in selected_analysts: - analyst_nodes["social"] = create_social_media_analyst( - self.quick_thinking_llm - ) + analyst_nodes["social"] = create_social_media_analyst(self.quick_thinking_llm) delete_nodes["social"] = create_msg_delete() tool_nodes["social"] = self.tool_nodes["social"] if "news" in selected_analysts: - analyst_nodes["news"] = create_news_analyst( - self.quick_thinking_llm - ) + analyst_nodes["news"] = create_news_analyst(self.quick_thinking_llm) delete_nodes["news"] = create_msg_delete() tool_nodes["news"] = self.tool_nodes["news"] if "fundamentals" in selected_analysts: - analyst_nodes["fundamentals"] = create_fundamentals_analyst( - self.quick_thinking_llm - ) + analyst_nodes["fundamentals"] = create_fundamentals_analyst(self.quick_thinking_llm) delete_nodes["fundamentals"] = create_msg_delete() tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"] # Create researcher and manager nodes - bull_researcher_node = create_bull_researcher( - self.quick_thinking_llm, self.bull_memory - ) - bear_researcher_node = create_bear_researcher( - self.quick_thinking_llm, self.bear_memory - ) + bull_researcher_node = create_bull_researcher(self.quick_thinking_llm, self.bull_memory) + bear_researcher_node = create_bear_researcher(self.quick_thinking_llm, self.bear_memory) research_manager_node = create_research_manager( self.deep_thinking_llm, self.invest_judge_memory ) @@ -101,9 +88,7 @@ class GraphSetup: risky_analyst = create_risky_debator(self.quick_thinking_llm) neutral_analyst = create_neutral_debator(self.quick_thinking_llm) safe_analyst = create_safe_debator(self.quick_thinking_llm) - risk_manager_node = create_risk_manager( - self.deep_thinking_llm, self.risk_manager_memory - ) + risk_manager_node = create_risk_manager(self.deep_thinking_llm, self.risk_manager_memory) # Create workflow workflow = StateGraph(AgentState) @@ -111,9 +96,7 @@ class GraphSetup: # Add analyst nodes to the graph for analyst_type, node in analyst_nodes.items(): workflow.add_node(f"{analyst_type.capitalize()} Analyst", node) - workflow.add_node( - f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type] - ) + workflow.add_node(f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type]) workflow.add_node(f"tools_{analyst_type}", tool_nodes[analyst_type]) # Add other nodes diff --git a/tradingagents/graph/signal_processing.py b/tradingagents/graph/signal_processing.py index 5c00dfc3..d1975462 100644 --- a/tradingagents/graph/signal_processing.py +++ b/tradingagents/graph/signal_processing.py @@ -1,6 +1,7 @@ # TradingAgents/graph/signal_processing.py import re + from langchain_openai import ChatOpenAI diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py index caa4a877..b7049c00 100644 --- a/tradingagents/graph/trading_graph.py +++ b/tradingagents/graph/trading_graph.py @@ -1,36 +1,30 @@ # TradingAgents/graph/trading_graph.py +import json import os from pathlib import Path -import json -from datetime import date -from typing import Dict, Any, Tuple, List, Optional - -from langchain_openai import ChatOpenAI -from langchain_anthropic import ChatAnthropic -from langchain_google_genai import ChatGoogleGenerativeAI +from typing import Any, Dict from langgraph.prebuilt import ToolNode from tradingagents.agents import * -from tradingagents.default_config import DEFAULT_CONFIG from tradingagents.agents.utils.memory import FinancialSituationMemory -from tradingagents.agents.utils.agent_states import ( - AgentState, - InvestDebateState, - RiskDebateState, -) from tradingagents.dataflows.config import set_config +from tradingagents.default_config import DEFAULT_CONFIG # Import tools from new registry-based system from tradingagents.tools.generator import get_agent_tools +from tradingagents.utils.logger import get_logger + from .conditional_logic import ConditionalLogic -from .setup import GraphSetup from .propagation import Propagator from .reflection import Reflector +from .setup import GraphSetup from .signal_processing import SignalProcessor +logger = get_logger(__name__) + class TradingAgentsGraph: """Main class that orchestrates the trading agents framework.""" @@ -61,22 +55,10 @@ class TradingAgentsGraph: ) # Initialize LLMs - if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter": - self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], api_key=os.getenv("OPENAI_API_KEY")) - self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], api_key=os.getenv("OPENAI_API_KEY")) - elif self.config["llm_provider"].lower() == "anthropic": - self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], api_key=os.getenv("ANTHROPIC_API_KEY")) - self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], api_key=os.getenv("ANTHROPIC_API_KEY")) - elif self.config["llm_provider"].lower() == "google": - # Explicitly pass Google API key from environment - google_api_key = os.getenv("GOOGLE_API_KEY") - if not google_api_key: - raise ValueError("GOOGLE_API_KEY environment variable not set. Please add it to your .env file.") - self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"], google_api_key=google_api_key) - self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"], google_api_key=google_api_key) - else: - raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}") - + from tradingagents.utils.llm_factory import create_llms + + self.deep_thinking_llm, self.quick_thinking_llm = create_llms(self.config) + # Initialize memories only if enabled if self.config.get("enable_memory", False): self.bull_memory = FinancialSituationMemory("bull_memory", self.config) @@ -127,24 +109,26 @@ class TradingAgentsGraph: def _load_historical_memories(self): """Load pre-built historical memories from disk.""" - import pickle import glob + import pickle - memory_dir = self.config.get("memory_dir", os.path.join(self.config["data_dir"], "memories")) + memory_dir = self.config.get( + "memory_dir", os.path.join(self.config["data_dir"], "memories") + ) if not os.path.exists(memory_dir): - print(f"⚠️ Memory directory not found: {memory_dir}") - print(" Run scripts/build_historical_memories.py to create memories") + logger.warning(f"⚠️ Memory directory not found: {memory_dir}") + logger.warning("Run scripts/build_historical_memories.py to create memories") return - print(f"\n📚 Loading historical memories from {memory_dir}...") + logger.info(f"📚 Loading historical memories from {memory_dir}...") memory_map = { "bull": self.bull_memory, "bear": self.bear_memory, "trader": self.trader_memory, "invest_judge": self.invest_judge_memory, - "risk_manager": self.risk_manager_memory + "risk_manager": self.risk_manager_memory, } for agent_type, memory in memory_map.items(): @@ -153,14 +137,14 @@ class TradingAgentsGraph: files = glob.glob(pattern) if not files: - print(f" ⚠️ No historical memories found for {agent_type}") + logger.warning(f"⚠️ No historical memories found for {agent_type}") continue # Use the most recent file latest_file = max(files, key=os.path.getmtime) try: - with open(latest_file, 'rb') as f: + with open(latest_file, "rb") as f: data = pickle.load(f) # Add memories to the collection @@ -169,17 +153,19 @@ class TradingAgentsGraph: documents=data["documents"], metadatas=data["metadatas"], embeddings=data["embeddings"], - ids=data["ids"] + ids=data["ids"], ) - print(f" ✅ {agent_type}: Loaded {len(data['documents'])} memories from {os.path.basename(latest_file)}") + logger.info( + f"✅ {agent_type}: Loaded {len(data['documents'])} memories from {os.path.basename(latest_file)}" + ) else: - print(f" ⚠️ {agent_type}: Empty memory file") + logger.warning(f"⚠️ {agent_type}: Empty memory file") except Exception as e: - print(f" ❌ Error loading {agent_type} memories: {e}") + logger.error(f"❌ Error loading {agent_type} memories: {e}") - print("📚 Historical memory loading complete\n") + logger.info("📚 Historical memory loading complete") def _create_tool_nodes(self) -> Dict[str, ToolNode]: """Create tool nodes for different agents using registry-based system. @@ -197,9 +183,6 @@ class TradingAgentsGraph: if agent_tools: tool_nodes[agent_name] = ToolNode(agent_tools) else: - # Log warning if no tools found for this agent - import logging - logger = logging.getLogger(__name__) logger.warning(f"No tools found for agent '{agent_name}' in registry") return tool_nodes @@ -210,9 +193,7 @@ class TradingAgentsGraph: self.ticker = company_name # Initialize state - init_agent_state = self.propagator.create_initial_state( - company_name, trade_date - ) + init_agent_state = self.propagator.create_initial_state(company_name, trade_date) args = self.propagator.get_graph_args() if self.debug: @@ -252,12 +233,8 @@ class TradingAgentsGraph: "bull_history": final_state["investment_debate_state"]["bull_history"], "bear_history": final_state["investment_debate_state"]["bear_history"], "history": final_state["investment_debate_state"]["history"], - "current_response": final_state["investment_debate_state"][ - "current_response" - ], - "judge_decision": final_state["investment_debate_state"][ - "judge_decision" - ], + "current_response": final_state["investment_debate_state"]["current_response"], + "judge_decision": final_state["investment_debate_state"]["judge_decision"], }, "trader_investment_decision": final_state["trader_investment_plan"], "risk_debate_state": { @@ -286,16 +263,10 @@ class TradingAgentsGraph: # Skip reflection if memory is disabled if not self.config.get("enable_memory", False): return - - self.reflector.reflect_bull_researcher( - self.curr_state, returns_losses, self.bull_memory - ) - self.reflector.reflect_bear_researcher( - self.curr_state, returns_losses, self.bear_memory - ) - self.reflector.reflect_trader( - self.curr_state, returns_losses, self.trader_memory - ) + + self.reflector.reflect_bull_researcher(self.curr_state, returns_losses, self.bull_memory) + self.reflector.reflect_bear_researcher(self.curr_state, returns_losses, self.bear_memory) + self.reflector.reflect_trader(self.curr_state, returns_losses, self.trader_memory) self.reflector.reflect_invest_judge( self.curr_state, returns_losses, self.invest_judge_memory ) @@ -307,25 +278,26 @@ class TradingAgentsGraph: """Process a signal to extract the core decision.""" return self.signal_processor.process_signal(full_signal) + if __name__ == "__main__": # Build the full TradingAgents graph tg = TradingAgentsGraph() - - print("Generating graph diagrams...") - + + logger.info("Generating graph diagrams...") + # Export a PNG diagram (requires Graphviz) try: # get_graph() returns the drawable graph structure tg.graph.get_graph().draw_png("trading_graph.png") - print("✅ PNG diagram saved as trading_graph.png") + logger.info("✅ PNG diagram saved as trading_graph.png") except Exception as e: - print(f"⚠️ Could not generate PNG (Graphviz may be missing): {e}") - + logger.warning(f"⚠️ Could not generate PNG (Graphviz may be missing): {e}") + # Export a Mermaid markdown file for easy embedding in docs/README try: mermaid_src = tg.graph.get_graph().draw_mermaid() with open("trading_graph.mmd", "w") as f: f.write(mermaid_src) - print("✅ Mermaid diagram saved as trading_graph.mmd") + logger.info("✅ Mermaid diagram saved as trading_graph.mmd") except Exception as e: - print(f"⚠️ Could not generate Mermaid diagram: {e}") + logger.warning(f"⚠️ Could not generate Mermaid diagram: {e}") diff --git a/tradingagents/ml/__init__.py b/tradingagents/ml/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tradingagents/ml/feature_engineering.py b/tradingagents/ml/feature_engineering.py new file mode 100644 index 00000000..18e6177b --- /dev/null +++ b/tradingagents/ml/feature_engineering.py @@ -0,0 +1,355 @@ +"""Shared feature extraction for ML model — used by both training and inference. + +All 20 features are computed locally from OHLCV data via stockstats + pandas. +Zero API calls required for indicator computation. +""" + +from __future__ import annotations + +from typing import Dict, List, Optional + +import numpy as np +import pandas as pd +from stockstats import wrap + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + +# Canonical feature list — order matters for model consistency +FEATURE_COLUMNS: List[str] = [ + # Base indicators (20) + "rsi_14", + "macd", + "macd_signal", + "macd_hist", + "atr_pct", + "bb_width_pct", + "bb_position", + "adx", + "mfi", + "stoch_k", + "volume_ratio_5d", + "volume_ratio_20d", + "return_1d", + "return_5d", + "return_20d", + "sma50_distance", + "sma200_distance", + "high_low_range", + "gap_pct", + "log_market_cap", + # Interaction & derived features (10) + "momentum_x_compression", # strong trend + tight bands = breakout signal + "rsi_momentum", # RSI rate of change (acceleration) + "volume_price_confirm", # volume surge + positive return = confirmed move + "trend_alignment", # SMA50 and SMA200 agree on direction + "volatility_regime", # ATR percentile rank (0-1) within own history + "mean_reversion_signal", # oversold RSI + below lower BB + "breakout_signal", # above upper BB + high volume + "macd_strength", # MACD histogram normalized by ATR + "return_volatility_ratio", # Sharpe-like: return_5d / atr_pct + "trend_momentum_score", # combined trend + momentum z-score +] + +# Minimum rows of OHLCV history needed before features are valid +# (200-day SMA needs 200 rows of prior data) +MIN_HISTORY_ROWS = 210 + + +def compute_features_bulk(ohlcv: pd.DataFrame, market_cap: Optional[float] = None) -> pd.DataFrame: + """Compute all 20 ML features for every row in an OHLCV DataFrame. + + Args: + ohlcv: DataFrame with columns: Date, Open, High, Low, Close, Volume. + Must be sorted by Date ascending. + market_cap: Market capitalization in USD. If None, log_market_cap = NaN. + + Returns: + DataFrame indexed by Date with one column per feature. + Rows with insufficient history (first ~210) will have NaN values. + """ + if ohlcv.empty or len(ohlcv) < MIN_HISTORY_ROWS: + return pd.DataFrame(columns=FEATURE_COLUMNS) + + df = ohlcv.copy() + + # Ensure Date column is available and set as index + if "Date" in df.columns: + df["Date"] = pd.to_datetime(df["Date"]) + df = df.set_index("Date").sort_index() + elif not isinstance(df.index, pd.DatetimeIndex): + df.index = pd.to_datetime(df.index) + df = df.sort_index() + + # Normalize column names (yfinance sometimes returns Title Case) + col_map = {} + for col in df.columns: + lower = col.lower() + if lower == "open": + col_map[col] = "Open" + elif lower == "high": + col_map[col] = "High" + elif lower == "low": + col_map[col] = "Low" + elif lower in ("close", "adj close"): + col_map[col] = "Close" + elif lower == "volume": + col_map[col] = "Volume" + df = df.rename(columns=col_map) + + # Need these columns + for required in ("Open", "High", "Low", "Close", "Volume"): + if required not in df.columns: + logger.warning(f"Missing column {required} in OHLCV data") + return pd.DataFrame(columns=FEATURE_COLUMNS) + + close = df["Close"] + volume = df["Volume"] + + # --- Stockstats indicators --- + ss = wrap(df.copy()) + + features = pd.DataFrame(index=df.index) + + # 1. RSI (14-period) + features["rsi_14"] = ss["rsi_14"] + + # 2-4. MACD (12, 26, 9) + features["macd"] = ss["macd"] + features["macd_signal"] = ss["macds"] + features["macd_hist"] = ss["macdh"] + + # 5. ATR as percentage of price + atr = ss["atr_14"] + features["atr_pct"] = (atr / close) * 100 + + # 6. Bollinger Band width as percentage + bb_upper = ss["boll_ub"] + bb_lower = ss["boll_lb"] + bb_middle = ss["boll"] + features["bb_width_pct"] = ((bb_upper - bb_lower) / bb_middle) * 100 + + # 7. Position within Bollinger Bands (0 = lower band, 1 = upper band) + bb_range = bb_upper - bb_lower + features["bb_position"] = np.where( + bb_range > 0, (close - bb_lower) / bb_range, 0.5 + ) + + # 8. ADX (trend strength) + features["adx"] = ss["dx_14"] + + # 9. Money Flow Index + features["mfi"] = ss["mfi_14"] + + # 10. Stochastic %K + features["stoch_k"] = ss["kdjk"] + + # --- Pandas-computed features --- + + # 11-12. Volume ratios + vol_ma_5 = volume.rolling(5).mean() + vol_ma_20 = volume.rolling(20).mean() + features["volume_ratio_5d"] = volume / vol_ma_5.replace(0, np.nan) + features["volume_ratio_20d"] = volume / vol_ma_20.replace(0, np.nan) + + # 13-15. Historical returns (looking backward — no data leakage) + features["return_1d"] = close.pct_change(1, fill_method=None) * 100 + features["return_5d"] = close.pct_change(5, fill_method=None) * 100 + features["return_20d"] = close.pct_change(20, fill_method=None) * 100 + + # 16-17. Distance from moving averages + sma_50 = close.rolling(50).mean() + sma_200 = close.rolling(200).mean() + features["sma50_distance"] = ((close - sma_50) / sma_50) * 100 + features["sma200_distance"] = ((close - sma_200) / sma_200) * 100 + + # 18. High-Low range as percentage of close + features["high_low_range"] = ((df["High"] - df["Low"]) / close) * 100 + + # 19. Gap percentage (open vs previous close) + prev_close = close.shift(1) + features["gap_pct"] = ((df["Open"] - prev_close) / prev_close) * 100 + + # 20. Log market cap (static per stock) + if market_cap and market_cap > 0: + features["log_market_cap"] = np.log10(market_cap) + else: + features["log_market_cap"] = np.nan + + # --- Interaction & derived features (10) --- + + # 21. Momentum × Compression: strong trend direction + tight Bollinger = breakout setup + # High absolute MACD + low BB width = coiled spring + features["momentum_x_compression"] = features["macd_hist"].abs() / features["bb_width_pct"].replace(0, np.nan) + + # 22. RSI momentum: 5-day rate of change of RSI (acceleration of momentum) + features["rsi_momentum"] = features["rsi_14"] - features["rsi_14"].shift(5) + + # 23. Volume-price confirmation: volume surge accompanied by price move + features["volume_price_confirm"] = features["volume_ratio_5d"] * features["return_1d"] + + # 24. Trend alignment: both SMAs agree (1 = aligned bullish, -1 = aligned bearish) + features["trend_alignment"] = np.sign(features["sma50_distance"]) * np.sign(features["sma200_distance"]) + + # 25. Volatility regime: ATR percentile within rolling 60-day window (0-1) + atr_pct_series = features["atr_pct"] + features["volatility_regime"] = atr_pct_series.rolling(60).apply( + lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() != x.min() else 0.5, + raw=False, + ) + + # 26. Mean reversion signal: oversold RSI + price below lower Bollinger + features["mean_reversion_signal"] = ( + (100 - features["rsi_14"]) / 100 # inversed RSI (higher = more oversold) + ) * (1 - features["bb_position"].clip(0, 1)) # below lower band amplifies signal + + # 27. Breakout signal: above upper BB + high volume ratio + features["breakout_signal"] = ( + features["bb_position"].clip(0, 2) * features["volume_ratio_20d"] + ) + + # 28. MACD strength: histogram normalized by volatility + features["macd_strength"] = features["macd_hist"] / features["atr_pct"].replace(0, np.nan) + + # 29. Return/Volatility ratio: Sharpe-like metric + features["return_volatility_ratio"] = features["return_5d"] / features["atr_pct"].replace(0, np.nan) + + # 30. Trend-momentum composite score + features["trend_momentum_score"] = ( + features["sma50_distance"] * 0.4 + + features["rsi_14"].sub(50) * 0.3 # RSI centered at 50 + + features["macd_hist"] * 0.3 + ) + + return features[FEATURE_COLUMNS] + + +def compute_features_single( + ohlcv: pd.DataFrame, + date: str, + market_cap: Optional[float] = None, +) -> Optional[Dict[str, float]]: + """Compute features for a single date. Used during live inference. + + Args: + ohlcv: Full OHLCV DataFrame (needs ~210 rows of history before `date`). + date: Target date string (YYYY-MM-DD). + market_cap: Market cap in USD. + + Returns: + Dict mapping feature name → value, or None if insufficient data. + """ + features_df = compute_features_bulk(ohlcv, market_cap=market_cap) + if features_df.empty: + return None + + date_ts = pd.Timestamp(date) + # Find the closest date on or before the target + valid = features_df.index[features_df.index <= date_ts] + if len(valid) == 0: + return None + + row = features_df.loc[valid[-1]] + if row.isna().all(): + return None + + return row.to_dict() + + +def compute_features_from_enriched_candidate(cand: Dict) -> Optional[Dict[str, float]]: + """Extract ML features from an already-enriched discovery candidate. + + During live inference, the enrichment pipeline has already computed + many of the values we need. This avoids redundant computation. + + Args: + cand: Enriched candidate dict from filter.py. + + Returns: + Dict of feature values, or None if critical fields are missing. + """ + features: Dict[str, float] = {} + + # Features already available on enriched candidates + features["rsi_14"] = cand.get("rsi_value", np.nan) + features["atr_pct"] = cand.get("atr_pct", np.nan) + features["bb_width_pct"] = cand.get("bb_width_pct", np.nan) + features["volume_ratio_20d"] = cand.get("volume_ratio", np.nan) + + # Market cap + market_cap_bil = cand.get("market_cap_bil") + if market_cap_bil and market_cap_bil > 0: + features["log_market_cap"] = np.log10(market_cap_bil * 1e9) + else: + features["log_market_cap"] = np.nan + + # Intraday return as proxy for return_1d + features["return_1d"] = cand.get("intraday_change_pct", np.nan) + + # Short interest as a signal (use as proxy where we lack full OHLCV) + short_pct = cand.get("short_interest_pct") + if short_pct is not None: + features["log_market_cap"] = features.get("log_market_cap", np.nan) + + # For features not directly available on enriched candidates, + # we need to fetch OHLCV and compute. This is the "full path". + # Return None to signal the caller should use compute_features_single() instead. + missing = [f for f in FEATURE_COLUMNS if f not in features or np.isnan(features.get(f, np.nan))] + if len(missing) > 5: + # Too many missing — need full OHLCV computation + return None + + # Fill remaining with NaN (TabPFN handles missing values) + for col in FEATURE_COLUMNS: + if col not in features: + features[col] = np.nan + + return features + + +def apply_triple_barrier_labels( + close_prices: pd.Series, + profit_target: float = 0.05, + stop_loss: float = 0.03, + max_holding_days: int = 7, +) -> pd.Series: + """Apply triple-barrier labeling to a series of close prices. + + For each day, looks forward up to `max_holding_days` trading days: + +1 (WIN): Price hits +profit_target first + -1 (LOSS): Price hits -stop_loss first + 0 (TIMEOUT): Neither barrier hit within the window + + Args: + close_prices: Series of daily close prices, indexed by date. + profit_target: Upside target as fraction (0.05 = 5%). + stop_loss: Downside limit as fraction (0.03 = 3%). + max_holding_days: Maximum forward-looking trading days. + + Returns: + Series of labels (+1, -1, 0) aligned with close_prices index. + Last `max_holding_days` rows will be NaN (can't look forward). + """ + prices = close_prices.values + n = len(prices) + labels = np.full(n, np.nan) + + for i in range(n - max_holding_days): + entry = prices[i] + upper = entry * (1 + profit_target) + lower = entry * (1 - stop_loss) + + label = 0 # default: timeout + for j in range(1, max_holding_days + 1): + future_price = prices[i + j] + if future_price >= upper: + label = 1 # hit profit target + break + elif future_price <= lower: + label = -1 # hit stop loss + break + + labels[i] = label + + return pd.Series(labels, index=close_prices.index, name="label") diff --git a/tradingagents/ml/predictor.py b/tradingagents/ml/predictor.py new file mode 100644 index 00000000..cbac034d --- /dev/null +++ b/tradingagents/ml/predictor.py @@ -0,0 +1,170 @@ +"""ML predictor for discovery pipeline — loads trained model and runs inference. + +Gracefully degrades: if no model file exists, all predictions return None. +The discovery pipeline works exactly as before without a trained model. +""" + +from __future__ import annotations + +import os +import pickle +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from tradingagents.ml.feature_engineering import FEATURE_COLUMNS +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + +# Default model path relative to project root +DEFAULT_MODEL_DIR = Path("data/ml") +MODEL_FILENAME = "tabpfn_model.pkl" +METRICS_FILENAME = "metrics.json" + +# Class label mapping +LABEL_MAP = {-1: "LOSS", 0: "TIMEOUT", 1: "WIN"} + + +class LGBMWrapper: + """Sklearn-compatible wrapper for LightGBM booster with original label mapping. + + Defined here (not in train script) so pickle can find the class on deserialization. + """ + + def __init__(self, booster, y_train=None): + self.booster = booster + self.classes_ = np.array([-1, 0, 1]) + + def predict_proba(self, X): + if isinstance(X, pd.DataFrame): + X = X.values + return self.booster.predict(X) + + def predict(self, X): + probas = self.predict_proba(X) + mapped = np.argmax(probas, axis=1) + return self.classes_[mapped] + + +class MLPredictor: + """Wraps a trained ML model for win probability prediction. + + Usage: + predictor = MLPredictor.load() # loads from default path + if predictor is not None: + result = predictor.predict(feature_dict) + # result = {"win_prob": 0.73, "loss_prob": 0.12, "timeout_prob": 0.15, "prediction": "WIN"} + """ + + def __init__(self, model: Any, feature_columns: List[str], model_type: str = "tabpfn"): + self.model = model + self.feature_columns = feature_columns + self.model_type = model_type + + @classmethod + def load(cls, model_dir: Optional[str] = None) -> Optional[MLPredictor]: + """Load a trained model from disk. Returns None if no model exists.""" + if model_dir is None: + model_dir = str(DEFAULT_MODEL_DIR) + + model_path = os.path.join(model_dir, MODEL_FILENAME) + if not os.path.exists(model_path): + logger.debug(f"No ML model found at {model_path} — ML predictions disabled") + return None + + try: + with open(model_path, "rb") as f: + saved = pickle.load(f) + + model = saved["model"] + feature_columns = saved.get("feature_columns", FEATURE_COLUMNS) + model_type = saved.get("model_type", "unknown") + + logger.info(f"Loaded ML model ({model_type}) from {model_path}") + return cls(model=model, feature_columns=feature_columns, model_type=model_type) + + except Exception as e: + logger.warning(f"Failed to load ML model from {model_path}: {e}") + return None + + def predict(self, features: Dict[str, float]) -> Optional[Dict[str, Any]]: + """Predict win probability for a single candidate. + + Args: + features: Dict mapping feature names to values (from feature_engineering). + + Returns: + Dict with win_prob, loss_prob, timeout_prob, prediction, or None on error. + """ + try: + # Build feature vector in correct order + X = np.array([[features.get(col, np.nan) for col in self.feature_columns]]) + X_df = pd.DataFrame(X, columns=self.feature_columns) + + # Get probability predictions + probas = self.model.predict_proba(X_df) + + # Map class indices to labels + # Model classes should be [-1, 0, 1] or [0, 1, 2] depending on training + classes = list(self.model.classes_) + + # Build probability dict + result: Dict[str, Any] = {} + for i, cls_label in enumerate(classes): + prob = float(probas[0][i]) + if cls_label == 1 or cls_label == 2: # WIN class + result["win_prob"] = prob + elif cls_label == -1 or cls_label == 0: + if cls_label == -1: + result["loss_prob"] = prob + else: + # Could be timeout (0) in {-1,0,1} or loss in {0,1,2} + if len(classes) == 3 and max(classes) == 2: + result["loss_prob"] = prob + else: + result["timeout_prob"] = prob + + # Ensure all keys present + result.setdefault("win_prob", 0.0) + result.setdefault("loss_prob", 0.0) + result.setdefault("timeout_prob", 0.0) + + # Predicted class + pred_idx = np.argmax(probas[0]) + pred_class = classes[pred_idx] + result["prediction"] = LABEL_MAP.get(pred_class, str(pred_class)) + + return result + + except Exception as e: + logger.warning(f"ML prediction failed: {e}") + return None + + def predict_batch( + self, feature_dicts: List[Dict[str, float]] + ) -> List[Optional[Dict[str, Any]]]: + """Predict win probabilities for multiple candidates.""" + return [self.predict(f) for f in feature_dicts] + + def save(self, model_dir: Optional[str] = None) -> str: + """Save the model to disk.""" + if model_dir is None: + model_dir = str(DEFAULT_MODEL_DIR) + + os.makedirs(model_dir, exist_ok=True) + model_path = os.path.join(model_dir, MODEL_FILENAME) + + saved = { + "model": self.model, + "feature_columns": self.feature_columns, + "model_type": self.model_type, + } + + with open(model_path, "wb") as f: + pickle.dump(saved, f) + + logger.info(f"Saved ML model to {model_path}") + return model_path diff --git a/tradingagents/schemas/__init__.py b/tradingagents/schemas/__init__.py index 0469e002..eb11352b 100644 --- a/tradingagents/schemas/__init__.py +++ b/tradingagents/schemas/__init__.py @@ -1,19 +1,25 @@ """Schemas package for TradingAgents.""" from .llm_outputs import ( - TradeDecision, - TickerList, - TickerWithContext, - TickerContextList, - ThemeList, - MarketMover, - MarketMovers, + DebateDecision, DiscoveryRankingItem, DiscoveryRankingList, + FilingItem, + FilingsList, InvestmentOpportunity, + MarketMover, + MarketMovers, + NewsItem, + NewsList, RankedOpportunities, - DebateDecision, + RedditTicker, + RedditTickerList, RiskAssessment, + ThemeList, + TickerContextList, + TickerList, + TickerWithContext, + TradeDecision, ) __all__ = [ @@ -24,10 +30,16 @@ __all__ = [ "ThemeList", "MarketMovers", "MarketMover", + "NewsItem", + "NewsList", + "FilingItem", + "FilingsList", "DiscoveryRankingItem", "DiscoveryRankingList", "InvestmentOpportunity", "RankedOpportunities", "DebateDecision", "RiskAssessment", + "RedditTicker", + "RedditTickerList", ] diff --git a/tradingagents/schemas/llm_outputs.py b/tradingagents/schemas/llm_outputs.py index c5cb508c..87e20183 100644 --- a/tradingagents/schemas/llm_outputs.py +++ b/tradingagents/schemas/llm_outputs.py @@ -5,31 +5,25 @@ These schemas ensure type-safe, validated responses from LLM calls, eliminating the need for manual parsing and reducing errors. """ -from pydantic import BaseModel, Field -from typing import Literal, List, Optional +from typing import List, Literal, Optional + +from pydantic import BaseModel, ConfigDict, Field class TradeDecision(BaseModel): """Structured output for trading decisions.""" - - decision: Literal["BUY", "SELL", "HOLD"] = Field( - description="The final trading decision" - ) - rationale: str = Field( - description="Detailed explanation of the decision" - ) + + decision: Literal["BUY", "SELL", "HOLD"] = Field(description="The final trading decision") + rationale: str = Field(description="Detailed explanation of the decision") confidence: Literal["high", "medium", "low"] = Field( description="Confidence level in the decision" ) - key_factors: List[str] = Field( - description="List of key factors influencing the decision" - ) - + key_factors: List[str] = Field(description="List of key factors influencing the decision") class TickerList(BaseModel): """Structured output for ticker symbol lists.""" - + tickers: List[str] = Field( description="List of valid stock ticker symbols (1-5 uppercase letters)" ) @@ -37,10 +31,8 @@ class TickerList(BaseModel): class TickerWithContext(BaseModel): """Individual ticker with context description.""" - - ticker: str = Field( - description="Stock ticker symbol (1-5 uppercase letters)" - ) + + ticker: str = Field(description="Stock ticker symbol (1-5 uppercase letters)") context: str = Field( description="Brief description of why this ticker is relevant (key metrics, catalyst, etc.)" ) @@ -48,79 +40,130 @@ class TickerWithContext(BaseModel): class TickerContextList(BaseModel): """Structured output for tickers with context.""" - + candidates: List[TickerWithContext] = Field( description="List of stock tickers with context explaining their relevance" ) +class RedditTicker(BaseModel): + """Individual ticker extracted from Reddit with source classification.""" + + ticker: str = Field( + description="Stock ticker symbol (1-5 uppercase letters only, e.g., AAPL, NVDA, TSLA)" + ) + source: Literal["trending", "dd"] = Field( + description="Source type: 'trending' for social mentions, 'dd' for due diligence research" + ) + context: str = Field( + description="Brief description of the sentiment, thesis, or why the ticker was mentioned" + ) + confidence: Literal["high", "medium", "low"] = Field( + default="medium", + description="Confidence that this is a valid stock ticker (not crypto, index, or gibberish)", + ) + + +class RedditTickerList(BaseModel): + """Structured output for Reddit ticker extraction.""" + + model_config = ConfigDict(extra="forbid") # Strict validation + + tickers: List[RedditTicker] = Field( + description="List of stock tickers extracted from Reddit posts" + ) + + class ThemeList(BaseModel): """Structured output for market themes.""" - - themes: List[str] = Field( - description="List of trending market themes or sectors" - ) + + themes: List[str] = Field(description="List of trending market themes or sectors") class MarketMover(BaseModel): """Individual market mover entry.""" - - ticker: str = Field( - description="Stock ticker symbol" - ) - type: Literal["gainer", "loser"] = Field( - description="Whether this is a top gainer or loser" - ) - change_percent: Optional[float] = Field( - default=None, - description="Percent change for the move" - ) - reason: Optional[str] = Field( - default=None, - description="Brief reason for the movement" - ) + + ticker: str = Field(description="Stock ticker symbol") + type: Literal["gainer", "loser"] = Field(description="Whether this is a top gainer or loser") + change_percent: Optional[float] = Field(default=None, description="Percent change for the move") + reason: Optional[str] = Field(default=None, description="Brief reason for the movement") class MarketMovers(BaseModel): """Structured output for market movers.""" - - movers: List[MarketMover] = Field( - description="List of market movers (gainers and losers)" + + movers: List[MarketMover] = Field(description="List of market movers (gainers and losers)") + + +class NewsItem(BaseModel): + """Individual news item entry.""" + + model_config = ConfigDict(extra="forbid") + + title: str = Field(description="Headline title of the news item") + summary: str = Field(description="2-3 sentence summary of the key points") + published_at: Optional[str] = Field( + default=None, description="ISO-8601 timestamp of publication if available" ) + companies_mentioned: List[str] = Field( + description="List of company names or ticker symbols mentioned" + ) + themes: List[str] = Field(description="List of key themes or categories") + sentiment: Literal["positive", "negative", "neutral"] = Field( + default="neutral", description="Expected price impact direction" + ) + importance: int = Field(ge=1, le=10, description="Importance score from 1-10") + + +class NewsList(BaseModel): + """Structured output for news items.""" + + model_config = ConfigDict(extra="forbid") + + news: List[NewsItem] = Field(description="List of news items") + + +class FilingItem(BaseModel): + """Individual SEC filing entry.""" + + model_config = ConfigDict(extra="forbid") + + title: str = Field(description="Company name and filing type") + summary: str = Field(description="Summary of the material event") + published_at: Optional[str] = Field( + default=None, description="ISO-8601 timestamp of publication if available" + ) + companies_mentioned: List[str] = Field(description="Company names or tickers mentioned") + themes: List[str] = Field( + description="Type of event (e.g., acquisition, guidance, executive change)" + ) + sentiment: Literal["positive", "negative", "neutral"] = Field( + default="neutral", description="Expected price impact direction" + ) + importance: int = Field(ge=1, le=10, description="Importance score from 1-10") + + +class FilingsList(BaseModel): + """Structured output for SEC filings.""" + + model_config = ConfigDict(extra="forbid") + + filings: List[FilingItem] = Field(description="List of important SEC filings") class DiscoveryRankingItem(BaseModel): """Individual discovery ranking entry.""" - ticker: str = Field( - description="Stock ticker symbol" - ) - rank: int = Field( - ge=1, - description="Rank order (1 is highest)" - ) + ticker: str = Field(description="Stock ticker symbol") + rank: int = Field(ge=1, description="Rank order (1 is highest)") strategy_match: str = Field( description="Primary strategy match (e.g., Momentum, Contrarian, Insider)" ) - base_score: float = Field( - ge=0, - le=10, - description="Base strategy score before modifiers" - ) - modifiers: str = Field( - description="Score modifiers with brief rationale" - ) - final_score: float = Field( - description="Final score after modifiers" - ) - confidence: int = Field( - ge=1, - le=10, - description="Confidence score from 1-10" - ) - reason: str = Field( - description="Specific rationale with actionable insight" - ) + base_score: float = Field(ge=0, le=10, description="Base strategy score before modifiers") + modifiers: str = Field(description="Score modifiers with brief rationale") + final_score: float = Field(description="Final score after modifiers") + confidence: int = Field(ge=1, le=10, description="Confidence score from 1-10") + reason: str = Field(description="Specific rationale with actionable insight") class DiscoveryRankingList(BaseModel): @@ -133,69 +176,41 @@ class DiscoveryRankingList(BaseModel): class InvestmentOpportunity(BaseModel): """Individual investment opportunity.""" - - ticker: str = Field( - description="Stock ticker symbol" - ) - score: int = Field( - ge=1, - le=10, - description="Investment score from 1-10" - ) - rationale: str = Field( - description="Why this is a good opportunity" - ) - risk_level: Literal["low", "medium", "high"] = Field( - description="Risk level assessment" - ) + + ticker: str = Field(description="Stock ticker symbol") + score: int = Field(ge=1, le=10, description="Investment score from 1-10") + rationale: str = Field(description="Why this is a good opportunity") + risk_level: Literal["low", "medium", "high"] = Field(description="Risk level assessment") class RankedOpportunities(BaseModel): """Structured output for ranked investment opportunities.""" - + opportunities: List[InvestmentOpportunity] = Field( description="List of investment opportunities ranked by score" ) - market_context: str = Field( - description="Brief overview of current market conditions" - ) + market_context: str = Field(description="Brief overview of current market conditions") class DebateDecision(BaseModel): """Structured output for debate/research manager decisions.""" - - decision: Literal["BUY", "SELL", "HOLD"] = Field( - description="Investment recommendation" - ) - summary: str = Field( - description="Summary of the debate and key arguments" - ) - bull_points: List[str] = Field( - description="Key bullish arguments" - ) - bear_points: List[str] = Field( - description="Key bearish arguments" - ) - investment_plan: str = Field( - description="Detailed investment plan for the trader" - ) + + decision: Literal["BUY", "SELL", "HOLD"] = Field(description="Investment recommendation") + summary: str = Field(description="Summary of the debate and key arguments") + bull_points: List[str] = Field(description="Key bullish arguments") + bear_points: List[str] = Field(description="Key bearish arguments") + investment_plan: str = Field(description="Detailed investment plan for the trader") class RiskAssessment(BaseModel): """Structured output for risk management decisions.""" - + final_decision: Literal["BUY", "SELL", "HOLD"] = Field( description="Final trading decision after risk assessment" ) risk_level: Literal["low", "medium", "high", "very_high"] = Field( description="Overall risk level" ) - adjusted_plan: str = Field( - description="Risk-adjusted investment plan" - ) - risk_factors: List[str] = Field( - description="Key risk factors identified" - ) - mitigation_strategies: List[str] = Field( - description="Strategies to mitigate identified risks" - ) + adjusted_plan: str = Field(description="Risk-adjusted investment plan") + risk_factors: List[str] = Field(description="Key risk factors identified") + mitigation_strategies: List[str] = Field(description="Strategies to mitigate identified risks") diff --git a/tradingagents/tools/executor.py b/tradingagents/tools/executor.py index 8a7ef07f..f09214ed 100644 --- a/tradingagents/tools/executor.py +++ b/tradingagents/tools/executor.py @@ -12,21 +12,24 @@ Key improvements over old system: - No dual registry systems """ -from typing import Any, Optional, List, Dict -import logging import concurrent.futures -from tradingagents.tools.registry import TOOL_REGISTRY, get_vendor_config, get_tool_metadata +from typing import Any, Dict, List, Optional -logger = logging.getLogger(__name__) +from tradingagents.tools.registry import TOOL_REGISTRY, get_tool_metadata, get_vendor_config +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) class ToolExecutionError(Exception): """Raised when tool execution fails across all vendors.""" + pass class VendorNotFoundError(Exception): """Raised when no vendor implementation is found for a tool.""" + pass @@ -72,7 +75,9 @@ def _execute_fallback(tool_name: str, vendor_config: Dict, *args, **kwargs) -> A continue # All vendors failed - error_summary = f"Tool '{tool_name}' failed with all vendors:\n" + "\n".join(f" - {err}" for err in errors) + error_summary = f"Tool '{tool_name}' failed with all vendors:\n" + "\n".join( + f" - {err}" for err in errors + ) logger.error(error_summary) raise ToolExecutionError(error_summary) @@ -101,7 +106,9 @@ def _execute_aggregate(tool_name: str, vendor_config: Dict, metadata: Dict, *arg # Get list of vendors to aggregate (default to all in priority list) vendors_to_aggregate = metadata.get("aggregate_vendors") or vendor_config["vendor_priority"] - logger.debug(f"Executing tool '{tool_name}' in aggregate mode with vendors: {vendors_to_aggregate}") + logger.debug( + f"Executing tool '{tool_name}' in aggregate mode with vendors: {vendors_to_aggregate}" + ) results = [] errors = [] @@ -116,17 +123,16 @@ def _execute_aggregate(tool_name: str, vendor_config: Dict, metadata: Dict, *arg future = executor.submit(vendor_func, *args, **kwargs) future_to_vendor[future] = vendor_name else: - logger.warning(f"Vendor '{vendor_name}' not found in vendors dict for tool '{tool_name}'") + logger.warning( + f"Vendor '{vendor_name}' not found in vendors dict for tool '{tool_name}'" + ) # Collect results as they complete for future in concurrent.futures.as_completed(future_to_vendor): vendor_name = future_to_vendor[future] try: result = future.result() - results.append({ - "vendor": vendor_name, - "data": result - }) + results.append({"vendor": vendor_name, "data": result}) logger.debug(f"Tool '{tool_name}': vendor '{vendor_name}' succeeded") except Exception as e: error_msg = f"Vendor '{vendor_name}' failed: {str(e)}" @@ -135,7 +141,9 @@ def _execute_aggregate(tool_name: str, vendor_config: Dict, metadata: Dict, *arg # Check if we got any results if not results: - error_summary = f"Tool '{tool_name}' aggregate mode: all vendors failed:\n" + "\n".join(f" - {err}" for err in errors) + error_summary = f"Tool '{tool_name}' aggregate mode: all vendors failed:\n" + "\n".join( + f" - {err}" for err in errors + ) logger.error(error_summary) raise ToolExecutionError(error_summary) @@ -225,6 +233,7 @@ def list_available_vendors(tool_name: str) -> List[str]: # LEGACY COMPATIBILITY LAYER # ============================================================================ + def route_to_vendor(method: str, *args, **kwargs) -> Any: """Legacy compatibility function. @@ -241,9 +250,7 @@ def route_to_vendor(method: str, *args, **kwargs) -> Any: Returns: Result from tool execution """ - logger.warning( - f"route_to_vendor() is deprecated. Use execute_tool('{method}', ...) instead." - ) + logger.warning(f"route_to_vendor() is deprecated. Use execute_tool('{method}', ...) instead.") return execute_tool(method, *args, **kwargs) @@ -253,45 +260,47 @@ def route_to_vendor(method: str, *args, **kwargs) -> Any: if __name__ == "__main__": # Enable debug logging + import logging + logging.basicConfig(level=logging.DEBUG) - print("=" * 70) - print("TOOL EXECUTOR - TESTING") - print("=" * 70) + logger.info("=" * 70) + logger.info("TOOL EXECUTOR - TESTING") + logger.info("=" * 70) # Test 1: List available vendors for each tool - print("\nAvailable vendors per tool:") + logger.info("Available vendors per tool:") from tradingagents.tools.registry import get_all_tools for tool_name in get_all_tools(): vendors = list_available_vendors(tool_name) - print(f" {tool_name}:") - print(f" Primary: {vendors[0] if vendors else 'None'}") + logger.info(f" {tool_name}:") + logger.info(f" Primary: {vendors[0] if vendors else 'None'}") if len(vendors) > 1: - print(f" Fallbacks: {', '.join(vendors[1:])}") + logger.info(f" Fallbacks: {', '.join(vendors[1:])}") # Test 2: Show tool info - print("\nTool info examples:") + logger.info("Tool info examples:") for tool_name in ["get_stock_data", "get_news", "get_fundamentals"]: info = get_tool_info(tool_name) if info: - print(f"\n {tool_name}:") - print(f" Category: {info['category']}") - print(f" Agents: {', '.join(info['agents']) if info['agents'] else 'None'}") - print(f" Description: {info['description']}") + logger.info(f" {tool_name}:") + logger.info(f" Category: {info['category']}") + logger.info(f" Agents: {', '.join(info['agents']) if info['agents'] else 'None'}") + logger.info(f" Description: {info['description']}") # Test 3: Validate registry - print("\nValidating registry:") + logger.info("Validating registry:") from tradingagents.tools.registry import validate_registry issues = validate_registry() if issues: - print(" ⚠️ Registry validation issues found:") + logger.warning("⚠️ Registry validation issues found:") for issue in issues[:10]: # Show first 10 - print(f" - {issue}") + logger.warning(f" - {issue}") if len(issues) > 10: - print(f" ... and {len(issues) - 10} more") + logger.warning(f" ... and {len(issues) - 10} more") else: - print(" ✅ Registry is valid!") + logger.info("✅ Registry is valid!") - print("\n" + "=" * 70) + logger.info("=" * 70) diff --git a/tradingagents/tools/generator.py b/tradingagents/tools/generator.py index 6fc96e88..b7da7ce8 100644 --- a/tradingagents/tools/generator.py +++ b/tradingagents/tools/generator.py @@ -11,12 +11,15 @@ Key benefits: - Type annotations generated automatically """ -from typing import Dict, Callable, Any, get_type_hints +from typing import Any, Callable, Dict + from langchain_core.tools import tool -from typing import Annotated -from tradingagents.tools.registry import TOOL_REGISTRY + from tradingagents.tools.executor import execute_tool -import inspect +from tradingagents.tools.registry import TOOL_REGISTRY +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) def generate_langchain_tool(tool_name: str, metadata: Dict[str, Any]) -> Callable: @@ -36,18 +39,21 @@ def generate_langchain_tool(tool_name: str, metadata: Dict[str, Any]) -> Callabl returns_doc = metadata["returns"] # Create Pydantic model for arguments - from pydantic import create_model, Field - + from pydantic import Field, create_model + fields = {} for param_name, param_info in parameters.items(): param_type = _get_python_type(param_info["type"]) description = param_info["description"] - + if "default" in param_info: - fields[param_name] = (param_type, Field(default=param_info["default"], description=description)) + fields[param_name] = ( + param_type, + Field(default=param_info["default"], description=description), + ) else: fields[param_name] = (param_type, Field(..., description=description)) - + ArgsSchema = create_model(f"{tool_name}Schema", **fields) # Create the tool function dynamically @@ -63,7 +69,7 @@ def generate_langchain_tool(tool_name: str, metadata: Dict[str, Any]) -> Callabl # Set function metadata tool_function.__name__ = tool_name tool_function.__doc__ = f"{description}\n\nReturns:\n {returns_doc}" - + # Apply @tool decorator with explicit schema decorated_tool = tool(args_schema=ArgsSchema)(tool_function) @@ -104,7 +110,7 @@ def generate_all_tools() -> Dict[str, Callable]: tool_func = generate_langchain_tool(tool_name, metadata) tools[tool_name] = tool_func except Exception as e: - print(f"⚠️ Failed to generate tool '{tool_name}': {e}") + logger.warning(f"⚠️ Failed to generate tool '{tool_name}': {e}") return tools @@ -130,7 +136,7 @@ def generate_tools_for_agent(agent_name: str) -> Dict[str, Callable]: if tool_name in ALL_TOOLS: tools[tool_name] = ALL_TOOLS[tool_name] else: - print(f"⚠️ Tool '{tool_name}' not found in ALL_TOOLS") + logger.warning(f"⚠️ Tool '{tool_name}' not found in ALL_TOOLS") return tools @@ -185,6 +191,7 @@ def get_agent_tools(agent_name: str) -> list: # TOOL EXPORT HELPER # ============================================================================ + def export_tools_module(output_path: str = "tradingagents/agents/tools.py"): """Export generated tools to a Python file. @@ -194,29 +201,29 @@ def export_tools_module(output_path: str = "tradingagents/agents/tools.py"): Args: output_path: Where to write the tools.py file """ - with open(output_path, 'w') as f: + with open(output_path, "w") as f: f.write('"""\n') - f.write('Auto-generated LangChain tools from registry.\n') - f.write('\n') - f.write('DO NOT EDIT THIS FILE MANUALLY!\n') - f.write('This file is auto-generated from tradingagents/tools/registry.py\n') - f.write('\n') - f.write('To add/modify tools, edit the TOOL_REGISTRY in registry.py,\n') - f.write('then run: python -m tradingagents.tools.generator\n') + f.write("Auto-generated LangChain tools from registry.\n") + f.write("\n") + f.write("DO NOT EDIT THIS FILE MANUALLY!\n") + f.write("This file is auto-generated from tradingagents/tools/registry.py\n") + f.write("\n") + f.write("To add/modify tools, edit the TOOL_REGISTRY in registry.py,\n") + f.write("then run: python -m tradingagents.tools.generator\n") f.write('"""\n\n') - f.write('from tradingagents.tools.generator import ALL_TOOLS\n\n') + f.write("from tradingagents.tools.generator import ALL_TOOLS\n\n") - f.write('# Export all generated tools\n') + f.write("# Export all generated tools\n") for tool_name in sorted(TOOL_REGISTRY.keys()): f.write(f'{tool_name} = ALL_TOOLS["{tool_name}"]\n') - f.write('\n__all__ = [\n') + f.write("\n__all__ = [\n") for tool_name in sorted(TOOL_REGISTRY.keys()): f.write(f' "{tool_name}",\n') - f.write(']\n') + f.write("]\n") - print(f"✅ Exported {len(TOOL_REGISTRY)} tools to {output_path}") + logger.info(f"Exported {len(TOOL_REGISTRY)} tools to {output_path}") # ============================================================================ @@ -224,47 +231,47 @@ def export_tools_module(output_path: str = "tradingagents/agents/tools.py"): # ============================================================================ if __name__ == "__main__": - print("=" * 70) - print("LANGCHAIN TOOL GENERATOR - TESTING") - print("=" * 70) + logger.info("=" * 70) + logger.info("LANGCHAIN TOOL GENERATOR - TESTING") + logger.info("=" * 70) # Test 1: Generate all tools - print(f"\nGenerating all tools...") + logger.info("\nGenerating all tools...") all_tools = generate_all_tools() - print(f"✅ Generated {len(all_tools)} tools") + logger.info(f"Generated {len(all_tools)} tools") # Test 2: Inspect a few tools - print("\nInspecting generated tools:") + logger.info("\nInspecting generated tools:") for tool_name in ["get_stock_data", "get_news", "get_fundamentals"]: if tool_name in all_tools: tool_func = all_tools[tool_name] - print(f"\n {tool_name}:") - print(f" Name: {tool_func.name}") - print(f" Description: {tool_func.description[:80]}...") + logger.info(f"\n {tool_name}:") + logger.info(f" Name: {tool_func.name}") + logger.info(f" Description: {tool_func.description[:80]}...") # Use model_fields instead of deprecated __fields__ - if hasattr(tool_func.args_schema, 'model_fields'): - print(f" Args schema: {list(tool_func.args_schema.model_fields.keys())}") + if hasattr(tool_func.args_schema, "model_fields"): + logger.info(f" Args schema: {list(tool_func.args_schema.model_fields.keys())}") else: - print(f" Args schema: {list(tool_func.args_schema.__fields__.keys())}") + logger.info(f" Args schema: {list(tool_func.args_schema.__fields__.keys())}") # Test 3: Generate tools for specific agents - print("\nTools per agent:") + logger.info("\nTools per agent:") from tradingagents.tools.registry import get_agent_tool_mapping mapping = get_agent_tool_mapping() for agent_name, tool_names in sorted(mapping.items()): agent_tools = get_agent_tools(agent_name) - print(f" {agent_name}: {len(agent_tools)} tools") + logger.info(f" {agent_name}: {len(agent_tools)} tools") for tool in agent_tools: - print(f" - {tool.name}") + logger.info(f" - {tool.name}") # Test 4: Export to file - print("\nExporting tools to file...") + logger.info("\nExporting tools to file...") export_tools_module() - print("\n" + "=" * 70) - print("✅ All tests passed!") - print("\nUsage:") - print(" from tradingagents.tools.generator import get_tool, get_agent_tools") - print(" tool = get_tool('get_stock_data')") - print(" market_tools = get_agent_tools('market')") + logger.info("\n" + "=" * 70) + logger.info("All tests passed!") + logger.info("\nUsage:") + logger.info(" from tradingagents.tools.generator import get_tool, get_agent_tools") + logger.info(" tool = get_tool('get_stock_data')") + logger.info(" market_tools = get_agent_tools('market')") diff --git a/tradingagents/tools/registry.py b/tradingagents/tools/registry.py index 6476bb16..82da24a8 100644 --- a/tradingagents/tools/registry.py +++ b/tradingagents/tools/registry.py @@ -10,80 +10,110 @@ This registry defines ALL tools with their complete metadata: Adding a new tool: Just add one entry here, everything else is auto-generated. """ -from typing import Dict, List, Optional, Callable, Any +from typing import Any, Dict, List, Optional -# Import all vendor implementations -from tradingagents.dataflows.y_finance import ( - get_YFin_data_online, - get_stock_stats_indicators_window, - get_technical_analysis, - get_balance_sheet as get_yfinance_balance_sheet, - get_cashflow as get_yfinance_cashflow, - get_income_statement as get_yfinance_income_statement, - get_insider_transactions as get_yfinance_insider_transactions, - validate_ticker as validate_ticker_yfinance, - get_fundamentals as get_yfinance_fundamentals, - get_options_activity as get_yfinance_options_activity, +from tradingagents.utils.logger import get_logger + +from tradingagents.dataflows.alpha_vantage import ( + get_balance_sheet as get_alpha_vantage_balance_sheet, +) +from tradingagents.dataflows.alpha_vantage import ( + get_cashflow as get_alpha_vantage_cashflow, +) +from tradingagents.dataflows.alpha_vantage import ( + get_fundamentals as get_alpha_vantage_fundamentals, +) +from tradingagents.dataflows.alpha_vantage import ( + get_income_statement as get_alpha_vantage_income_statement, +) +from tradingagents.dataflows.alpha_vantage import ( + get_insider_sentiment as get_alpha_vantage_insider_sentiment, ) from tradingagents.dataflows.alpha_vantage import ( get_stock as get_alpha_vantage_stock, - get_indicator as get_alpha_vantage_indicator, - get_fundamentals as get_alpha_vantage_fundamentals, - get_balance_sheet as get_alpha_vantage_balance_sheet, - get_cashflow as get_alpha_vantage_cashflow, - get_income_statement as get_alpha_vantage_income_statement, - get_insider_transactions as get_alpha_vantage_insider_transactions, - get_news as get_alpha_vantage_news, +) +from tradingagents.dataflows.alpha_vantage import ( get_top_gainers_losers as get_alpha_vantage_movers, ) -from tradingagents.dataflows.alpha_vantage_news import ( - get_global_news as get_alpha_vantage_global_news, -) -from tradingagents.dataflows.openai import ( - get_stock_news_openai, - get_global_news_openai, - get_fundamentals_openai, -) -from tradingagents.dataflows.google import ( - get_google_news, - get_global_news_google, -) -from tradingagents.dataflows.reddit_api import ( - get_reddit_news, - get_reddit_global_news as get_reddit_api_global_news, - get_reddit_trending_tickers, - get_reddit_discussions, -) -from tradingagents.dataflows.finnhub_api import ( - get_recommendation_trends as get_finnhub_recommendation_trends, - get_earnings_calendar as get_finnhub_earnings_calendar, - get_ipo_calendar as get_finnhub_ipo_calendar, -) -from tradingagents.dataflows.twitter_data import ( - get_tweets as get_twitter_tweets, -) -from tradingagents.dataflows.alpha_vantage_volume import ( - get_alpha_vantage_unusual_volume, -) from tradingagents.dataflows.alpha_vantage_analysts import ( get_alpha_vantage_analyst_changes, ) +from tradingagents.dataflows.alpha_vantage_volume import ( + get_alpha_vantage_unusual_volume, + get_cached_average_volume, + get_cached_average_volume_batch, +) +from tradingagents.dataflows.finnhub_api import ( + get_earnings_calendar as get_finnhub_earnings_calendar, +) +from tradingagents.dataflows.finnhub_api import ( + get_ipo_calendar as get_finnhub_ipo_calendar, +) +from tradingagents.dataflows.finnhub_api import ( + get_recommendation_trends as get_finnhub_recommendation_trends, +) +from tradingagents.dataflows.finviz_scraper import ( + get_finviz_insider_buying, + get_finviz_short_interest, +) +from tradingagents.dataflows.openai import ( + get_fundamentals_openai, + get_global_news_openai, + get_stock_news_openai, +) +from tradingagents.dataflows.reddit_api import ( + get_reddit_discussions, + get_reddit_news, + get_reddit_trending_tickers, +) +from tradingagents.dataflows.reddit_api import ( + get_reddit_global_news as get_reddit_api_global_news, +) from tradingagents.dataflows.tradier_api import ( get_tradier_unusual_options, ) -from tradingagents.dataflows.finviz_scraper import ( - get_finviz_short_interest, +from tradingagents.dataflows.twitter_data import ( + get_tweets as get_twitter_tweets, +) +from tradingagents.dataflows.y_finance import ( + get_balance_sheet as get_yfinance_balance_sheet, +) +from tradingagents.dataflows.y_finance import ( + get_cashflow as get_yfinance_cashflow, +) +from tradingagents.dataflows.y_finance import ( + get_fundamentals as get_yfinance_fundamentals, +) +from tradingagents.dataflows.y_finance import ( + get_income_statement as get_yfinance_income_statement, +) +from tradingagents.dataflows.y_finance import ( + get_insider_transactions as get_yfinance_insider_transactions, +) +from tradingagents.dataflows.y_finance import ( + get_options_activity as get_yfinance_options_activity, ) +# Import all vendor implementations +from tradingagents.dataflows.y_finance import ( + get_technical_analysis, + get_YFin_data_online, +) +from tradingagents.dataflows.y_finance import ( + validate_ticker as validate_ticker_yfinance, +) +from tradingagents.dataflows.y_finance import ( + validate_tickers_batch as validate_tickers_batch_yfinance, +) + +logger = get_logger(__name__) # ============================================================================ # TOOL REGISTRY - SINGLE SOURCE OF TRUTH # ============================================================================ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { - # ========== CORE STOCK APIs ========== - "get_stock_data": { "description": "Retrieve stock price data (OHLCV) for a given ticker symbol", "category": "core_stock_apis", @@ -100,7 +130,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Formatted dataframe containing stock price data", }, - "validate_ticker": { "description": "Validate if a ticker symbol exists and is tradeable", "category": "core_stock_apis", @@ -114,9 +143,70 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "bool: True if valid, False otherwise", }, - + "validate_tickers_batch": { + "description": "Validate multiple ticker symbols using Yahoo Finance quote endpoint", + "category": "core_stock_apis", + "agents": [], + "vendors": { + "yfinance": validate_tickers_batch_yfinance, + }, + "vendor_priority": ["yfinance"], + "parameters": { + "symbols": {"type": "list[str]", "description": "Ticker symbols to validate"}, + }, + "returns": "dict: valid/invalid ticker lists", + }, + "get_average_volume": { + "description": "Get average trading volume over a recent window (cached, with fallback download)", + "category": "core_stock_apis", + "agents": [], + "vendors": { + "volume_cache": get_cached_average_volume, + }, + "vendor_priority": ["volume_cache"], + "parameters": { + "symbol": {"type": "str", "description": "Ticker symbol"}, + "lookback_days": {"type": "int", "description": "Days to average", "default": 20}, + "curr_date": { + "type": "str", + "description": "Current date, YYYY-mm-dd", + "default": None, + }, + "cache_key": {"type": "str", "description": "Cache key/universe", "default": "default"}, + "fallback_download": { + "type": "bool", + "description": "Download if cache missing", + "default": True, + }, + }, + "returns": "dict: average and latest volume metadata", + }, + "get_average_volume_batch": { + "description": "Get average trading volumes for multiple tickers using cached data", + "category": "core_stock_apis", + "agents": [], + "vendors": { + "volume_cache": get_cached_average_volume_batch, + }, + "vendor_priority": ["volume_cache"], + "parameters": { + "symbols": {"type": "list[str]", "description": "Ticker symbols"}, + "lookback_days": {"type": "int", "description": "Days to average", "default": 20}, + "curr_date": { + "type": "str", + "description": "Current date, YYYY-mm-dd", + "default": None, + }, + "cache_key": {"type": "str", "description": "Cache key/universe", "default": "default"}, + "fallback_download": { + "type": "bool", + "description": "Download if cache missing", + "default": True, + }, + }, + "returns": "dict: mapping of ticker to volume metadata", + }, # ========== TECHNICAL INDICATORS ========== - # "get_indicators": { # "description": "Get concise technical analysis with signals, trends, and key indicator interpretations", # "category": "technical_indicators", @@ -131,7 +221,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { # }, # "returns": "str: Concise analysis with RSI signals, MACD crossovers, MA trends, Bollinger position, and ATR volatility", # }, - "get_indicators": { "description": "Get concise technical analysis with signals, trends, and key indicator interpretations", "category": "technical_indicators", @@ -146,9 +235,7 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Concise analysis with RSI signals, MACD crossovers, MA trends, Bollinger position, and ATR volatility", }, - # ========== FUNDAMENTAL DATA ========== - "get_fundamentals": { "description": "Retrieve comprehensive fundamental data for a ticker", "category": "fundamental_data", @@ -165,7 +252,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Comprehensive fundamental data report", }, - "get_balance_sheet": { "description": "Retrieve balance sheet data for a ticker", "category": "fundamental_data", @@ -180,7 +266,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Balance sheet data", }, - "get_cashflow": { "description": "Retrieve cash flow statement for a ticker", "category": "fundamental_data", @@ -195,7 +280,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Cash flow statement data", }, - "get_income_statement": { "description": "Retrieve income statement for a ticker", "category": "fundamental_data", @@ -210,7 +294,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Income statement data", }, - "get_recommendation_trends": { "description": "Retrieve analyst recommendation trends", "category": "fundamental_data", @@ -224,9 +307,7 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Analyst recommendation trends", }, - # ========== NEWS & INSIDER DATA ========== - "get_news": { "description": "Retrieve news articles for a specific ticker", "category": "news_data", @@ -247,7 +328,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: News articles and analysis", }, - "get_global_news": { "description": "Retrieve global market news and macroeconomic updates", "category": "news_data", @@ -263,44 +343,42 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { "parameters": { "date": {"type": "str", "description": "Date for news, yyyy-mm-dd"}, "look_back_days": {"type": "int", "description": "Days to look back", "default": 7}, - "limit": {"type": "int", "description": "Number of articles/topics to return", "default": 5}, + "limit": { + "type": "int", + "description": "Number of articles/topics to return", + "default": 5, + }, }, "returns": "str: Global news and macro updates", }, - "get_insider_sentiment": { "description": "Retrieve insider trading sentiment analysis", "category": "news_data", "agents": ["news"], "vendors": { - "yfinance": get_yfinance_insider_transactions, - "alpha_vantage": get_alpha_vantage_insider_transactions, + "alpha_vantage": get_alpha_vantage_insider_sentiment, }, - "vendor_priority": ["yfinance"], + "vendor_priority": ["alpha_vantage"], "parameters": { "ticker": {"type": "str", "description": "Ticker symbol"}, }, "returns": "str: Insider sentiment analysis", }, - "get_insider_transactions": { "description": "Retrieve insider transaction history", "category": "news_data", "agents": ["news"], "vendors": { - "alpha_vantage": get_alpha_vantage_insider_transactions, "yfinance": get_yfinance_insider_transactions, }, - "vendor_priority": ["alpha_vantage", "yfinance"], + "vendor_priority": ["yfinance"], "parameters": { "ticker": {"type": "str", "description": "Ticker symbol"}, }, "returns": "str: Insider transaction history", }, - # ========== DISCOVERY TOOLS ========== # (Used by discovery mode, not bound to regular analysis agents) - "get_trending_tickers": { "description": "Get trending stock tickers from social media", "category": "discovery", @@ -314,7 +392,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: List of trending tickers with sentiment", }, - "get_market_movers": { "description": "Get top market gainers and losers", "category": "discovery", @@ -328,7 +405,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Top gainers and losers", }, - "get_tweets": { "description": "Get tweets related to stocks or market topics", "category": "discovery", @@ -343,7 +419,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Tweets matching the query", }, - "get_earnings_calendar": { "description": "Get upcoming earnings announcements (catalysts for volatility)", "category": "discovery", @@ -358,7 +433,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Formatted earnings calendar with EPS and revenue estimates", }, - "get_ipo_calendar": { "description": "Get upcoming and recent IPOs (new listing opportunities)", "category": "discovery", @@ -373,7 +447,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Formatted IPO calendar with pricing and share details", }, - "get_unusual_volume": { "description": "Find stocks with unusual volume but minimal price movement (accumulation signal)", "category": "discovery", @@ -383,14 +456,29 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "vendor_priority": ["alpha_vantage"], "parameters": { - "date": {"type": "str", "description": "Analysis date in yyyy-mm-dd format", "default": None}, - "min_volume_multiple": {"type": "float", "description": "Minimum volume multiple vs average", "default": 3.0}, - "max_price_change": {"type": "float", "description": "Maximum price change percentage", "default": 5.0}, - "top_n": {"type": "int", "description": "Number of top results to return", "default": 20}, + "date": { + "type": "str", + "description": "Analysis date in yyyy-mm-dd format", + "default": None, + }, + "min_volume_multiple": { + "type": "float", + "description": "Minimum volume multiple vs average", + "default": 3.0, + }, + "max_price_change": { + "type": "float", + "description": "Maximum price change percentage", + "default": 5.0, + }, + "top_n": { + "type": "int", + "description": "Number of top results to return", + "default": 20, + }, }, "returns": "str: Formatted report of stocks with unusual volume patterns", }, - "get_unusual_options_activity": { "description": "Analyze options activity for specific tickers as confirmation signal (not for primary discovery)", "category": "discovery", @@ -402,12 +490,19 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { "vendor_priority": ["yfinance"], "parameters": { "ticker": {"type": "str", "description": "Ticker symbol to analyze"}, - "num_expirations": {"type": "int", "description": "Number of nearest expiration dates to analyze", "default": 3}, - "curr_date": {"type": "str", "description": "Analysis date for reference", "default": None}, + "num_expirations": { + "type": "int", + "description": "Number of nearest expiration dates to analyze", + "default": 3, + }, + "curr_date": { + "type": "str", + "description": "Analysis date for reference", + "default": None, + }, }, "returns": "str: Formatted report of options activity with put/call ratios", }, - "get_analyst_rating_changes": { "description": "Track recent analyst upgrades/downgrades and price target changes", "category": "discovery", @@ -417,13 +512,20 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "vendor_priority": ["alpha_vantage"], "parameters": { - "lookback_days": {"type": "int", "description": "Number of days to look back", "default": 7}, - "change_types": {"type": "list", "description": "Types of changes to track", "default": ["upgrade", "downgrade", "initiated"]}, + "lookback_days": { + "type": "int", + "description": "Number of days to look back", + "default": 7, + }, + "change_types": { + "type": "list", + "description": "Types of changes to track", + "default": ["upgrade", "downgrade", "initiated"], + }, "top_n": {"type": "int", "description": "Number of top results", "default": 20}, }, "returns": "str: Formatted report of recent analyst rating changes with freshness indicators", }, - "get_short_interest": { "description": "Discover stocks with high short interest by scraping Finviz screener (squeeze candidates)", "category": "discovery", @@ -433,13 +535,44 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "vendor_priority": ["finviz"], "parameters": { - "min_short_interest_pct": {"type": "float", "description": "Minimum short interest % of float", "default": 10.0}, - "min_days_to_cover": {"type": "float", "description": "Minimum days to cover ratio", "default": 2.0}, + "min_short_interest_pct": { + "type": "float", + "description": "Minimum short interest % of float", + "default": 10.0, + }, + "min_days_to_cover": { + "type": "float", + "description": "Minimum days to cover ratio", + "default": 2.0, + }, "top_n": {"type": "int", "description": "Number of top results", "default": 20}, }, "returns": "str: Formatted report of discovered high short interest stocks with squeeze potential", }, - + "get_insider_buying": { + "description": "Discover stocks with significant insider buying activity (leading indicator)", + "category": "discovery", + "agents": [], + "vendors": { + "finviz": get_finviz_insider_buying, + }, + "vendor_priority": ["finviz"], + "parameters": { + "transaction_type": { + "type": "str", + "description": "Transaction type: 'buy', 'sell', or 'any'", + "default": "buy", + }, + "top_n": {"type": "int", "description": "Number of top results", "default": 20}, + "lookback_days": {"type": "int", "description": "Days to look back", "default": 3}, + "min_value": { + "type": "int", + "description": "Minimum transaction value", + "default": 25000, + }, + }, + "returns": "str: Formatted report of stocks with recent insider buying/selling activity", + }, "get_reddit_discussions": { "description": "Get Reddit discussions about a specific ticker", "category": "news_data", @@ -455,7 +588,6 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { }, "returns": "str: Reddit discussions and sentiment", }, - "get_options_activity": { "description": "Get options activity for a specific ticker (volume, open interest, put/call ratios, unusual activity)", "category": "discovery", @@ -467,8 +599,16 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { "vendor_priority": ["yfinance"], "parameters": { "ticker": {"type": "str", "description": "Ticker symbol"}, - "num_expirations": {"type": "int", "description": "Number of nearest expiration dates to analyze", "default": 3}, - "curr_date": {"type": "str", "description": "Current date for reference", "default": None}, + "num_expirations": { + "type": "int", + "description": "Number of nearest expiration dates to analyze", + "default": 3, + }, + "curr_date": { + "type": "str", + "description": "Current date for reference", + "default": None, + }, }, "returns": "str: Options activity report with volume, OI, P/C ratios, and unusual activity", }, @@ -479,6 +619,7 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = { # HELPER FUNCTIONS # ============================================================================ + def get_tools_for_agent(agent_name: str) -> List[str]: """Get list of tool names available to a specific agent. @@ -547,7 +688,7 @@ def get_vendor_config(tool_name: str) -> Dict[str, Any]: return { "vendors": metadata.get("vendors", {}), - "vendor_priority": metadata.get("vendor_priority", []) + "vendor_priority": metadata.get("vendor_priority", []), } @@ -555,6 +696,7 @@ def get_vendor_config(tool_name: str) -> Dict[str, Any]: # AGENT-TOOL MAPPING # ============================================================================ + def get_agent_tool_mapping() -> Dict[str, List[str]]: """Get complete mapping of agents to their tools. @@ -579,6 +721,7 @@ def get_agent_tool_mapping() -> Dict[str, List[str]]: # VALIDATION # ============================================================================ + def validate_registry() -> List[str]: """Validate the tool registry for common issues. @@ -589,7 +732,15 @@ def validate_registry() -> List[str]: for tool_name, metadata in TOOL_REGISTRY.items(): # Check required fields - required_fields = ["description", "category", "agents", "vendors", "vendor_priority", "parameters", "returns"] + required_fields = [ + "description", + "category", + "agents", + "vendors", + "vendor_priority", + "parameters", + "returns", + ] for field in required_fields: if field not in metadata: issues.append(f"{tool_name}: Missing required field '{field}'") @@ -606,7 +757,9 @@ def validate_registry() -> List[str]: vendors = metadata.get("vendors", {}) for vendor_name in vendor_priority: if vendor_name not in vendors: - issues.append(f"{tool_name}: Vendor '{vendor_name}' in priority list but not in vendors dict") + issues.append( + f"{tool_name}: Vendor '{vendor_name}' in priority list but not in vendors dict" + ) # Check parameters if not isinstance(metadata.get("parameters"), dict): @@ -616,7 +769,9 @@ def validate_registry() -> List[str]: if "execution_mode" in metadata: execution_mode = metadata["execution_mode"] if execution_mode not in ["fallback", "aggregate"]: - issues.append(f"{tool_name}: Invalid execution_mode '{execution_mode}', must be 'fallback' or 'aggregate'") + issues.append( + f"{tool_name}: Invalid execution_mode '{execution_mode}', must be 'fallback' or 'aggregate'" + ) # Validate aggregate_vendors if present if "aggregate_vendors" in metadata: @@ -626,43 +781,47 @@ def validate_registry() -> List[str]: else: for vendor_name in aggregate_vendors: if vendor_name not in vendors: - issues.append(f"{tool_name}: aggregate_vendor '{vendor_name}' not in vendors dict") + issues.append( + f"{tool_name}: aggregate_vendor '{vendor_name}' not in vendors dict" + ) # Warn if aggregate_vendors specified but execution_mode is not aggregate if metadata.get("execution_mode") != "aggregate": - issues.append(f"{tool_name}: aggregate_vendors specified but execution_mode is not 'aggregate'") + issues.append( + f"{tool_name}: aggregate_vendors specified but execution_mode is not 'aggregate'" + ) return issues if __name__ == "__main__": # Example usage and validation - print("=" * 70) - print("TOOL REGISTRY OVERVIEW") - print("=" * 70) + logger.info("=" * 70) + logger.info("TOOL REGISTRY OVERVIEW") + logger.info("=" * 70) - print(f"\nTotal tools: {len(TOOL_REGISTRY)}") + logger.info(f"Total tools: {len(TOOL_REGISTRY)}") - print("\nTools by category:") + logger.info("Tools by category:") categories = set(m["category"] for m in TOOL_REGISTRY.values()) for category in sorted(categories): tools = get_tools_by_category(category) - print(f" {category}: {len(tools)} tools") + logger.info(f" {category}: {len(tools)} tools") for tool in tools: - print(f" - {tool}") + logger.debug(f" - {tool}") - print("\nAgent-Tool Mapping:") + logger.info("Agent-Tool Mapping:") mapping = get_agent_tool_mapping() for agent, tools in sorted(mapping.items()): - print(f" {agent}: {len(tools)} tools") + logger.info(f" {agent}: {len(tools)} tools") for tool in tools: - print(f" - {tool}") + logger.debug(f" - {tool}") - print("\nValidation:") + logger.info("Validation:") issues = validate_registry() if issues: - print(" ⚠️ Issues found:") + logger.warning("⚠️ Issues found:") for issue in issues: - print(f" - {issue}") + logger.warning(f" - {issue}") else: - print(" ✅ Registry is valid!") + logger.info("✅ Registry is valid!") diff --git a/tradingagents/ui/__init__.py b/tradingagents/ui/__init__.py new file mode 100644 index 00000000..7ca2cc35 --- /dev/null +++ b/tradingagents/ui/__init__.py @@ -0,0 +1,19 @@ +""" +Trading Agents UI package. + +This package contains the Streamlit dashboard and related utilities. +""" + +from tradingagents.ui.utils import ( + load_open_positions, + load_quick_stats, + load_recommendations, + load_statistics, +) + +__all__ = [ + "load_statistics", + "load_recommendations", + "load_open_positions", + "load_quick_stats", +] diff --git a/tradingagents/ui/dashboard.py b/tradingagents/ui/dashboard.py new file mode 100644 index 00000000..82b0744a --- /dev/null +++ b/tradingagents/ui/dashboard.py @@ -0,0 +1,95 @@ +""" +Main Streamlit app entry point for the Trading Agents Dashboard. + +This module sets up the dashboard page configuration, sidebar navigation, +and routing to different pages based on user selection. +""" + +import streamlit as st + +from tradingagents.ui import pages +from tradingagents.ui.utils import load_quick_stats + + +def setup_page_config(): + """Configure the Streamlit page settings.""" + st.set_page_config( + page_title="Trading Agents Dashboard", + page_icon="📊", + layout="wide", + initial_sidebar_state="expanded", + ) + + +def render_sidebar(): + """Render the sidebar with navigation and quick stats.""" + with st.sidebar: + st.title("Trading Agents") + + # Navigation + st.markdown("### Navigation") + page = st.radio( + "Select a page:", + options=["Home", "Today's Picks", "Portfolio", "Performance", "Settings"], + label_visibility="collapsed", + ) + + st.markdown("---") + + # Quick stats section + st.markdown("### Quick Stats") + try: + open_positions, win_rate = load_quick_stats() + + col1, col2 = st.columns(2) + with col1: + st.metric("Open Positions", open_positions) + with col2: + st.metric("Win Rate", f"{win_rate:.1f}%") + except Exception as e: + st.warning(f"Could not load quick stats: {str(e)}") + + return page + + +def route_page(page): + """Route to the appropriate page based on selection.""" + if page == "Home": + pages.home.render() + elif page == "Today's Picks": + pages.todays_picks.render() + elif page == "Portfolio": + pages.portfolio.render() + elif page == "Performance": + pages.performance.render() + elif page == "Settings": + pages.settings.render() + else: + st.error(f"Unknown page: {page}") + + +def main(): + """Main entry point for the Streamlit app.""" + setup_page_config() + + # Custom CSS for better styling + st.markdown( + """ + + """, + unsafe_allow_html=True, + ) + + # Render sidebar and get selected page + selected_page = render_sidebar() + + # Route to selected page + route_page(selected_page) + + +if __name__ == "__main__": + main() diff --git a/tradingagents/ui/pages/__init__.py b/tradingagents/ui/pages/__init__.py new file mode 100644 index 00000000..4ab695db --- /dev/null +++ b/tradingagents/ui/pages/__init__.py @@ -0,0 +1,40 @@ +""" +Dashboard page modules for the Trading Agents UI. + +This package contains all page modules that can be rendered in the dashboard. +Each module should have a render() function that displays the page content. +""" + +try: + from tradingagents.ui.pages import home +except ImportError: + home = None + +try: + from tradingagents.ui.pages import todays_picks +except ImportError: + todays_picks = None + +try: + from tradingagents.ui.pages import portfolio +except ImportError: + portfolio = None + +try: + from tradingagents.ui.pages import performance +except ImportError: + performance = None + +try: + from tradingagents.ui.pages import settings +except ImportError: + settings = None + + +__all__ = [ + "home", + "todays_picks", + "portfolio", + "performance", + "settings", +] diff --git a/tradingagents/ui/pages/home.py b/tradingagents/ui/pages/home.py new file mode 100644 index 00000000..928423e7 --- /dev/null +++ b/tradingagents/ui/pages/home.py @@ -0,0 +1,133 @@ +""" +Home page for the Trading Agents Dashboard. + +This module displays the main dashboard with overview metrics and +pipeline performance visualization. +""" + +import pandas as pd +import plotly.express as px +import streamlit as st + +from tradingagents.ui.utils import load_open_positions, load_statistics, load_strategy_metrics + + +def render() -> None: + """ + Render the home page with overview metrics and pipeline performance. + + Displays: + - Dashboard title + - Warning if no statistics available + - 4-column metric layout (Win Rate, Open Positions, Avg Return, Best Pipeline) + - Pipeline performance scatter plot with quadrant lines + """ + # Page title + st.title("🎯 Trading Discovery Dashboard") + + # Load data + stats = load_statistics() + positions = load_open_positions() + strategy_metrics = load_strategy_metrics() + + # Check if statistics are available + if not stats or not stats.get("overall_7d"): + st.warning("No statistics data available. Run the discovery pipeline to generate data.") + return + + if not strategy_metrics: + st.warning("No strategy performance data available yet.") + return + + # Extract overall metrics from 7-day period + overall_metrics = stats.get("overall_7d", {}) + win_rate_7d = overall_metrics.get("win_rate", 0) + avg_return_7d = overall_metrics.get("avg_return", 0) + open_positions_count = len(positions) if positions else 0 + + # Find best strategy + best_strategy = None + best_win_rate = 0.0 + for item in strategy_metrics: + win_rate = item.get("Win Rate", 0) or 0 + if win_rate > best_win_rate: + best_win_rate = win_rate + best_strategy = {"name": item.get("Strategy", "unknown"), "win_rate": win_rate} + + # Display 4-column metric layout + col1, col2, col3, col4 = st.columns(4) + + with col1: + st.metric( + label="Win Rate (7d)", + value=f"{win_rate_7d:.1f}%", + delta=f"{win_rate_7d - 50:.1f}%" if win_rate_7d >= 50 else None, + ) + + with col2: + st.metric( + label="Open Positions", + value=open_positions_count, + ) + + with col3: + st.metric( + label="Avg Return (7d)", + value=f"{avg_return_7d:.2f}%", + delta=f"{avg_return_7d:.2f}%" if avg_return_7d > 0 else None, + ) + + with col4: + if best_strategy: + st.metric( + label="Best Strategy", + value=best_strategy["name"], + delta=f"{best_strategy['win_rate']:.1f}% WR", + ) + else: + st.metric( + label="Best Strategy", + value="N/A", + ) + + # Strategy Performance scatter plot + st.subheader("Strategy Performance") + + if strategy_metrics: + df = pd.DataFrame(strategy_metrics) + + # Create scatter plot with plotly + fig = px.scatter( + df, + x="Win Rate", + y="Avg Return", + size="Count", + color="Strategy", + hover_name="Strategy", + hover_data={ + "Win Rate": ":.1f", + "Avg Return": ":.2f", + "Count": True, + "Strategy": False, + }, + title="Strategy Performance Analysis", + labels={ + "Win Rate": "Win Rate (%)", + "Avg Return": "Avg Return (%)", + }, + ) + + # Add quadrant lines at y=0 (breakeven) and x=50 (50% win rate) + fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.5) + fig.add_vline(x=50, line_dash="dash", line_color="gray", opacity=0.5) + + # Update layout for better visibility + fig.update_layout( + height=400, + showlegend=True, + hovermode="closest", + ) + + st.plotly_chart(fig, use_container_width=True) + else: + st.info("No strategy data available for visualization.") diff --git a/tradingagents/ui/pages/performance.py b/tradingagents/ui/pages/performance.py new file mode 100644 index 00000000..38b6e6d2 --- /dev/null +++ b/tradingagents/ui/pages/performance.py @@ -0,0 +1,80 @@ +""" +Performance analytics page for the Trading Agents Dashboard. + +This module displays performance metrics and visualization for different scanners, +including win rates, average returns, and trading volume analysis. +""" + +import pandas as pd +import plotly.express as px +import streamlit as st + +from tradingagents.ui.utils import load_strategy_metrics + + +def render() -> None: + """ + Render the performance analytics page. + + Displays: + - Page title + - Warning if no statistics available + - Scanner Performance heatmap with scatter plot showing: + - Win Rate (x-axis) vs Avg Return (y-axis) + - Bubble size = Trade count + - Color = Win Rate (RdYlGn colorscale) + - Quadrant lines at y=0 and x=50 + """ + # Page title + st.title("📊 Performance Analytics") + + # Load data + strategy_metrics = load_strategy_metrics() + + # Check if data is available + if not strategy_metrics: + st.warning("No strategy performance data available. Run performance tracking to generate data.") + return + + # Strategy Performance section + st.subheader("Strategy Performance") + + if strategy_metrics: + df = pd.DataFrame(strategy_metrics) + + # Create scatter plot with plotly + fig = px.scatter( + df, + x="Win Rate", + y="Avg Return", + size="Count", + color="Win Rate", + hover_name="Strategy", + hover_data={ + "Win Rate": ":.1f", + "Avg Return": ":.2f", + "Count": True, + "Strategy": False, + }, + title="Strategy Performance Analysis", + labels={ + "Win Rate": "Win Rate (%)", + "Avg Return": "Avg Return (%)", + }, + color_continuous_scale="RdYlGn", + ) + + # Add quadrant lines at y=0 (breakeven) and x=50 (50% win rate) + fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.5) + fig.add_vline(x=50, line_dash="dash", line_color="gray", opacity=0.5) + + # Update layout for better visibility + fig.update_layout( + height=500, + showlegend=True, + hovermode="closest", + ) + + st.plotly_chart(fig, use_container_width=True) + else: + st.info("No strategy data available for visualization.") diff --git a/tradingagents/ui/pages/portfolio.py b/tradingagents/ui/pages/portfolio.py new file mode 100644 index 00000000..5b7897f8 --- /dev/null +++ b/tradingagents/ui/pages/portfolio.py @@ -0,0 +1,90 @@ +"""Portfolio tracker.""" + +from datetime import datetime + +import pandas as pd +import streamlit as st + +from tradingagents.ui.utils import load_open_positions + + +def render(): + st.title("💼 Portfolio Tracker") + + # Manual add form + with st.expander("➕ Add Position"): + col1, col2, col3, col4 = st.columns(4) + with col1: + ticker = st.text_input("Ticker") + with col2: + entry_price = st.number_input("Entry Price", min_value=0.0) + with col3: + shares = st.number_input("Shares", min_value=0, step=1) + with col4: + st.write("") # Spacing + if st.button("Add"): + if ticker and entry_price > 0 and shares > 0: + from tradingagents.dataflows.discovery.performance.position_tracker import ( + PositionTracker, + ) + + tracker = PositionTracker() + pos = tracker.create_position( + { + "ticker": ticker.upper(), + "entry_price": entry_price, + "shares": shares, + "recommendation_date": datetime.now().isoformat(), + "pipeline": "manual", + "scanner": "manual", + "strategy_match": "manual", + "confidence": 5, + } + ) + tracker.save_position(pos) + st.success(f"Added {ticker.upper()}") + st.rerun() + + # Load positions + positions = load_open_positions() + + if not positions: + st.info("No open positions") + return + + # Summary + total_invested = sum(p["entry_price"] * p.get("shares", 0) for p in positions) + total_current = sum(p["metrics"]["current_price"] * p.get("shares", 0) for p in positions) + total_pnl = total_current - total_invested + total_pnl_pct = (total_pnl / total_invested * 100) if total_invested > 0 else 0 + + col1, col2, col3, col4 = st.columns(4) + with col1: + st.metric("Invested", f"${total_invested:,.2f}") + with col2: + st.metric("Current", f"${total_current:,.2f}") + with col3: + st.metric("P/L", f"${total_pnl:,.2f}", delta=f"{total_pnl_pct:+.1f}%") + with col4: + st.metric("Positions", len(positions)) + + # Table + st.subheader("📊 Positions") + + data = [] + for p in positions: + pnl = (p["metrics"]["current_price"] - p["entry_price"]) * p.get("shares", 0) + data.append( + { + "Ticker": p["ticker"], + "Entry": f"${p['entry_price']:.2f}", + "Current": f"${p['metrics']['current_price']:.2f}", + "Shares": p.get("shares", 0), + "P/L": f"${pnl:.2f}", + "P/L %": f"{p['metrics']['current_return']:+.1f}%", + "Days": p["metrics"]["days_held"], + } + ) + + df = pd.DataFrame(data) + st.dataframe(df, use_container_width=True) diff --git a/tradingagents/ui/pages/settings.py b/tradingagents/ui/pages/settings.py new file mode 100644 index 00000000..71b5cc6d --- /dev/null +++ b/tradingagents/ui/pages/settings.py @@ -0,0 +1,147 @@ +""" +Settings page for the Trading Agents Dashboard. + +This module displays configuration settings and scanner/pipeline status information. +It provides a read-only view of current settings with expandable sections for detailed configuration. +""" + +import streamlit as st + +from tradingagents.default_config import DEFAULT_CONFIG + + +def render() -> None: + """ + Render the settings page. + + Displays: + - Page title + - Configuration info message + - Discovery configuration settings + - Pipelines section with expandable cards showing: + - enabled status + - priority + - deep_dive_budget + - Scanners section with checkboxes showing: + - enabled status for each scanner + """ + # Page title + st.title("⚙️ Settings") + + # Info message + st.info("Configuration UI - TODO: Implement save functionality") + + # Get configuration + config = DEFAULT_CONFIG + discovery_config = config.get("discovery", {}) + + # Display current configuration section + st.subheader("📋 Configuration") + + # Show key discovery settings + col1, col2, col3 = st.columns(3) + + with col1: + st.metric( + label="Discovery Mode", + value=discovery_config.get("discovery_mode", "N/A"), + ) + + with col2: + st.metric( + label="Max Candidates", + value=discovery_config.get("max_candidates_to_analyze", "N/A"), + ) + + with col3: + st.metric( + label="Final Recommendations", + value=discovery_config.get("final_recommendations", "N/A"), + ) + + # Pipelines section + st.subheader("🔄 Pipelines") + + pipelines = discovery_config.get("pipelines", {}) + + if pipelines: + for pipeline_name, pipeline_config in pipelines.items(): + with st.expander( + f"{'✅' if pipeline_config.get('enabled') else '❌'} {pipeline_name.title()}" + ): + col1, col2, col3 = st.columns(3) + + with col1: + st.metric( + label="Enabled", + value="Yes" if pipeline_config.get("enabled") else "No", + ) + + with col2: + st.metric( + label="Priority", + value=pipeline_config.get("priority", "N/A"), + ) + + with col3: + st.metric( + label="Budget", + value=pipeline_config.get("deep_dive_budget", "N/A"), + ) + + if "ranker_prompt" in pipeline_config: + st.caption(f"Ranker: {pipeline_config.get('ranker_prompt', 'N/A')}") + else: + st.info("No pipelines configured") + + # Scanners section + st.subheader("🔍 Scanners") + + scanners = discovery_config.get("scanners", {}) + + if scanners: + col1, col2 = st.columns([2, 1]) + + with col1: + st.write("**Scanner Status**") + + with col2: + st.write("**Enabled**") + + # Display each scanner with checkbox showing enabled status + for scanner_name, scanner_config in scanners.items(): + col1, col2 = st.columns([2, 1]) + + with col1: + st.write(f"• {scanner_name.replace('_', ' ').title()}") + + with col2: + is_enabled = scanner_config.get("enabled", False) + st.write("✅" if is_enabled else "❌") + + # Additional scanner configuration in expander + with st.expander("📊 Scanner Details"): + for scanner_name, scanner_config in scanners.items(): + pipeline = scanner_config.get("pipeline", "N/A") + limit = scanner_config.get("limit", "N/A") + enabled = scanner_config.get("enabled", False) + + st.write( + f"**{scanner_name}** | " + f"Pipeline: {pipeline} | " + f"Limit: {limit} | " + f"Status: {'✅ Enabled' if enabled else '❌ Disabled'}" + ) + else: + st.info("No scanners configured") + + # Data sources section + st.subheader("📡 Data Sources") + + data_vendors = config.get("data_vendors", {}) + + if data_vendors: + for vendor_type, vendor_name in data_vendors.items(): + st.write(f"**{vendor_type.replace('_', ' ').title()}**: {vendor_name}") + else: + st.info("No data sources configured") diff --git a/tradingagents/ui/pages/todays_picks.py b/tradingagents/ui/pages/todays_picks.py new file mode 100644 index 00000000..e196033a --- /dev/null +++ b/tradingagents/ui/pages/todays_picks.py @@ -0,0 +1,142 @@ +"""Today's recommendations.""" + +from datetime import datetime + +import pandas as pd +import plotly.express as px +import streamlit as st + +from tradingagents.ui.utils import load_recommendations + + +@st.cache_data(ttl=3600) +def _load_price_history(ticker: str, period: str) -> pd.DataFrame: + try: + from tradingagents.dataflows.y_finance import download_history + except Exception: + return pd.DataFrame() + + data = download_history( + ticker, + period=period, + interval="1d", + auto_adjust=True, + progress=False, + ) + + if data is None or data.empty: + return pd.DataFrame() + + if isinstance(data.columns, pd.MultiIndex): + tickers = data.columns.get_level_values(1).unique() + target = ticker if ticker in tickers else tickers[0] + data = data.xs(target, level=1, axis=1).copy() + + data = data.reset_index() + date_col = "Date" if "Date" in data.columns else data.columns[0] + close_col = "Close" if "Close" in data.columns else "Adj Close" + if close_col not in data.columns: + return pd.DataFrame() + + return data[[date_col, close_col]].rename(columns={date_col: "date", close_col: "close"}) + + +def render(): + st.title("📋 Today's Recommendations") + + today = datetime.now().strftime("%Y-%m-%d") + recommendations, meta = load_recommendations(today, return_meta=True) + + if not recommendations: + st.warning(f"No recommendations for {today}") + return + + if meta.get("is_fallback") and meta.get("date"): + st.info(f"No recommendations for {today}. Showing latest from {meta['date']}.") + + show_charts = st.checkbox("Show price charts", value=True) + chart_window = st.selectbox( + "Price history window", + ["1mo", "3mo", "6mo", "1y"], + index=1, + ) + + # Filters + col1, col2, col3 = st.columns(3) + with col1: + pipelines = list( + set( + (r.get("pipeline") or r.get("strategy_match") or "unknown") + for r in recommendations + ) + ) + pipeline_filter = st.multiselect("Pipeline", pipelines, default=pipelines) + with col2: + min_confidence = st.slider("Min Confidence", 1, 10, 7) + with col3: + min_score = st.slider("Min Score", 0, 100, 70) + + # Apply filters + filtered = [ + r + for r in recommendations + if (r.get("pipeline") or r.get("strategy_match") or "unknown") in pipeline_filter + and r.get("confidence", 0) >= min_confidence + and r.get("final_score", 0) >= min_score + ] + + st.write(f"**{len(filtered)}** of **{len(recommendations)}** recommendations") + + # Display recommendations + for i, rec in enumerate(filtered, 1): + ticker = rec.get("ticker", "UNKNOWN") + score = rec.get("final_score", 0) + confidence = rec.get("confidence", 0) + pipeline = (rec.get("pipeline") or rec.get("strategy_match") or "unknown").title() + scanner = rec.get("scanner") or rec.get("strategy_match") or "unknown" + entry_price = rec.get("entry_price") + current_price = rec.get("current_price") + + with st.expander( + f"#{i} {ticker} - {rec.get('company_name', '')} (Score: {score}, Conf: {confidence}/10)" + ): + col1, col2 = st.columns([2, 1]) + + with col1: + st.write(f"**Pipeline:** {pipeline}") + st.write(f"**Scanner/Strategy:** {scanner}") + if entry_price is not None: + st.write(f"**Entry Price:** ${entry_price:.2f}") + if current_price is not None: + st.write(f"**Current Price:** ${current_price:.2f}") + st.write(f"**Thesis:** {rec.get('reason', 'N/A')}") + if show_charts: + history = _load_price_history(ticker, chart_window) + if history.empty: + st.caption("Price history unavailable.") + else: + last_close = history["close"].iloc[-1] + st.caption(f"Last close: ${last_close:.2f}") + fig = px.line( + history, + x="date", + y="close", + title=None, + labels={"date": "", "close": "Price"}, + ) + fig.update_traces(line=dict(color="#1f77b4", width=2)) + fig.update_layout( + height=260, + margin=dict(l=10, r=10, t=10, b=10), + xaxis=dict(showgrid=False), + yaxis=dict(showgrid=True, gridcolor="rgba(0,0,0,0.08)"), + hovermode="x unified", + ) + fig.update_yaxes(tickprefix="$") + st.plotly_chart(fig, use_container_width=True) + + with col2: + if st.button("✅ Enter Position", key=f"enter_{ticker}"): + st.info("Position entry modal (TODO)") + if st.button("👀 Watch", key=f"watch_{ticker}"): + st.success(f"Added {ticker} to watchlist") diff --git a/tradingagents/ui/utils.py b/tradingagents/ui/utils.py new file mode 100644 index 00000000..f2042053 --- /dev/null +++ b/tradingagents/ui/utils.py @@ -0,0 +1,282 @@ +""" +Utility functions for the Trading Agents Dashboard. + +This module provides helper functions for loading data from various sources +including statistics, recommendations, positions, and quick metrics. +""" + +import json +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +def get_data_directory() -> Path: + """Get the data directory path.""" + return Path(__file__).parent.parent.parent / "data" + + +def load_statistics() -> Dict[str, Any]: + """ + Load statistics data from JSON file. + + Returns: + Dictionary containing statistics data + """ + stats_file = get_data_directory() / "recommendations" / "statistics.json" + + if not stats_file.exists(): + return {} + + try: + with open(stats_file, "r") as f: + return json.load(f) + except Exception as e: + logger.error(f"Error loading statistics: {e}") + return {} + + +def _extract_date_from_filename(filename: str) -> Optional[str]: + name = filename + if name.endswith("_recommendations.json"): + date_str = name[: -len("_recommendations.json")] + elif name.endswith(".json"): + date_str = name[:-5] + else: + return None + + try: + datetime.strptime(date_str, "%Y-%m-%d") + return date_str + except ValueError: + return None + + +def _find_latest_recommendations_file( + recommendations_dir: Path, +) -> Tuple[Optional[Path], Optional[str]]: + if not recommendations_dir.exists(): + return None, None + + ignore = {"statistics.json", "performance_database.json"} + dated_files: List[Tuple[str, Path]] = [] + for path in recommendations_dir.glob("*.json"): + if path.name in ignore: + continue + date_str = _extract_date_from_filename(path.name) + if date_str: + dated_files.append((date_str, path)) + + if not dated_files: + return None, None + + dated_files.sort(key=lambda item: item[0]) + latest_date, latest_path = dated_files[-1] + return latest_path, latest_date + + +def _load_recommendations_payload( + rec_file: Path, +) -> Tuple[List[Dict[str, Any]], Optional[str]]: + try: + with open(rec_file, "r") as f: + data = json.load(f) + except Exception as e: + logger.error(f"Error loading recommendations from {rec_file}: {e}") + return [], None + + if isinstance(data, dict): + return data.get("recommendations", []) or [], data.get("date") + if isinstance(data, list): + return data, None + return [], None + + +def load_recommendations( + date: Optional[str] = None, *, return_meta: bool = False +) -> Union[List[Dict[str, Any]], Tuple[List[Dict[str, Any]], Dict[str, Any]]]: + """ + Load recommendations data from JSON file. + + Args: + date: Optional date in format YYYY-MM-DD. If None, loads today's recommendations. + return_meta: If True, returns (recommendations, meta) tuple. + + Returns: + List of recommendation dictionaries + """ + requested_date = date or datetime.now().strftime("%Y-%m-%d") + recommendations_dir = get_data_directory() / "recommendations" + + candidates = [ + recommendations_dir / f"{requested_date}_recommendations.json", + recommendations_dir / f"{requested_date}.json", + ] + + rec_file = next((p for p in candidates if p.exists()), None) + used_date = requested_date + is_fallback = False + + if rec_file is None and date is None: + rec_file, latest_date = _find_latest_recommendations_file(recommendations_dir) + if rec_file is not None: + used_date = latest_date or requested_date + is_fallback = True + + if rec_file is None: + meta = { + "requested_date": requested_date, + "date": None, + "source_file": None, + "is_fallback": False, + } + return ([], meta) if return_meta else [] + + recommendations, payload_date = _load_recommendations_payload(rec_file) + if payload_date: + used_date = payload_date + + meta = { + "requested_date": requested_date, + "date": used_date, + "source_file": str(rec_file), + "is_fallback": is_fallback, + } + return (recommendations, meta) if return_meta else recommendations + + +def load_open_positions() -> List[Dict[str, Any]]: + """ + Load open positions from the position tracker. + + Returns: + List of open position dictionaries + """ + try: + from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker + + tracker = PositionTracker() + positions = tracker.load_all_open_positions() + return positions if positions else [] + except Exception as e: + logger.error(f"Error loading open positions: {e}") + return [] + + +def load_performance_database() -> List[Dict[str, Any]]: + """ + Load the performance database (flattened list of recommendations). + """ + db_file = get_data_directory() / "recommendations" / "performance_database.json" + if not db_file.exists(): + return [] + + try: + with open(db_file, "r") as f: + data = json.load(f) + except Exception as e: + logger.error(f"Error loading performance database: {e}") + return [] + + if isinstance(data, dict): + by_date = data.get("recommendations_by_date", {}) + recs: List[Dict[str, Any]] = [] + for items in by_date.values(): + if isinstance(items, list): + recs.extend(items) + return recs + + if isinstance(data, list): + return data + + return [] + + +def load_strategy_metrics() -> List[Dict[str, Any]]: + """ + Build per-strategy metrics from the performance database if available. + Falls back to statistics.json when performance database is missing. + """ + recs = load_performance_database() + if recs: + metrics: Dict[str, Dict[str, float]] = {} + for rec in recs: + strategy = rec.get("strategy_match", "unknown") + if strategy not in metrics: + metrics[strategy] = { + "count": 0, + "wins": 0, + "sum_return": 0.0, + } + + if "return_7d" in rec: + metrics[strategy]["count"] += 1 + metrics[strategy]["sum_return"] += float(rec.get("return_7d", 0.0) or 0.0) + if rec.get("win_7d"): + metrics[strategy]["wins"] += 1 + + results = [] + for strategy, data in metrics.items(): + count = int(data["count"]) + if count == 0: + continue + win_rate = round((data["wins"] / count) * 100, 1) + avg_return = round(data["sum_return"] / count, 2) + results.append( + { + "Strategy": strategy, + "Win Rate": win_rate, + "Avg Return": avg_return, + "Count": count, + } + ) + return results + + stats = load_statistics() + by_strategy = stats.get("by_strategy", {}) if stats else {} + results = [] + for strategy, data in by_strategy.items(): + win_rate = data.get("win_rate_7d") or data.get("win_rate", 0) + avg_return = data.get("avg_return_7d", 0) + count = data.get("wins_7d", 0) + data.get("losses_7d", 0) + results.append( + { + "Strategy": strategy, + "Win Rate": win_rate, + "Avg Return": avg_return, + "Count": count, + } + ) + return results + + +def load_quick_stats() -> Tuple[int, float]: + """ + Load quick statistics for the sidebar. + + Returns: + Tuple of (open_positions_count, win_rate_percentage) + """ + # Load open positions + positions = load_open_positions() + open_positions_count = len(positions) + + # Calculate win rate from statistics + stats = load_statistics() + win_rate = 0.0 + + if stats and "trades" in stats and len(stats["trades"]) > 0: + winning_trades = sum( + 1 + for trade in stats["trades"] + if trade.get("status") == "closed" and trade.get("profit", 0) > 0 + ) + total_trades = sum(1 for trade in stats["trades"] if trade.get("status") == "closed") + if total_trades > 0: + win_rate = (winning_trades / total_trades) * 100 + + return open_positions_count, win_rate diff --git a/tradingagents/utils/llm_factory.py b/tradingagents/utils/llm_factory.py new file mode 100644 index 00000000..4e58ac4f --- /dev/null +++ b/tradingagents/utils/llm_factory.py @@ -0,0 +1,62 @@ +import os +from typing import Any, Dict, Tuple + +from langchain_anthropic import ChatAnthropic +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_google_genai import ChatGoogleGenerativeAI +from langchain_openai import ChatOpenAI + +from tradingagents.utils.logger import get_logger + +logger = get_logger(__name__) + + +def create_llms(config: Dict[str, Any]) -> Tuple[BaseChatModel, BaseChatModel]: + """ + Factory to create deep and quick thinking LLMs based on configuration. + + Args: + config: Configuration dictionary containing keys: + - llm_provider: 'openai', 'anthropic', 'google', 'ollama', or 'openrouter' + - deep_think_llm: Model name for complex reasoning + - quick_think_llm: Model name for simple tasks + + Returns: + Tuple containing (deep_thinking_llm, quick_thinking_llm) + + Raises: + ValueError: If provider is unsupported or API keys are missing. + """ + provider = config.get("llm_provider", "openai").lower() + + if provider in ["openai", "ollama", "openrouter"]: + api_key = os.getenv("OPENAI_API_KEY") + # For Ollama (local), API key might not be needed, but usually langgraph expects it or base_url + # If openrouter, it uses openai compatible interface + + deep_llm = ChatOpenAI(model=config["deep_think_llm"], api_key=api_key) + quick_llm = ChatOpenAI(model=config["quick_think_llm"], api_key=api_key) + + elif provider == "anthropic": + api_key = os.getenv("ANTHROPIC_API_KEY") + if not api_key: + # Try to warn but proceed (library might raise) + logger.warning("ANTHROPIC_API_KEY not found in environment.") + + deep_llm = ChatAnthropic(model=config["deep_think_llm"], api_key=api_key) + quick_llm = ChatAnthropic(model=config["quick_think_llm"], api_key=api_key) + + elif provider == "google": + api_key = os.getenv("GOOGLE_API_KEY") + if not api_key: + raise ValueError( + "GOOGLE_API_KEY environment variable not set. Please add it to your .env file." + ) + + deep_llm = ChatGoogleGenerativeAI(model=config["deep_think_llm"], google_api_key=api_key) + quick_llm = ChatGoogleGenerativeAI(model=config["quick_think_llm"], google_api_key=api_key) + + else: + raise ValueError(f"Unsupported LLM provider: {provider}") + + return deep_llm, quick_llm diff --git a/tradingagents/utils/logger.py b/tradingagents/utils/logger.py new file mode 100644 index 00000000..59e2bb67 --- /dev/null +++ b/tradingagents/utils/logger.py @@ -0,0 +1,61 @@ +import logging +import os +import sys +from typing import Optional + + +def get_logger(name: str, level: Optional[int] = None) -> logging.Logger: + """ + Get a configured logger instance. + + Environment variables: + LOG_LEVEL: Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) + LOG_FILE: Path to log file (if set, logs will be written to this file) + LOG_TO_CONSOLE: Set to 'false' to disable console logging (default: true) + + Args: + name: The name of the logger (usually __name__) + level: Optional logging level override (defaults to INFO or LOG_LEVEL env var) + + Returns: + Configured logger instance + + Example: + export LOG_FILE=ranker_debug.log + export LOG_LEVEL=DEBUG + python cli/main.py + """ + logger = logging.getLogger(name) + + # If logger is already configured, return it + if logger.hasHandlers(): + return logger + + # Get level from environment variable or use provided/default + if level is None: + env_level = os.getenv("LOG_LEVEL", "INFO").upper() + level = getattr(logging, env_level, logging.INFO) + logger.setLevel(level) + + # Create formatter + formatter = logging.Formatter( + "[%(asctime)s] %(levelname)s in %(module)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S" + ) + + # Add file handler if LOG_FILE is set + log_file = os.getenv("LOG_FILE") + if log_file: + file_handler = logging.FileHandler(log_file, mode="a") + file_handler.setLevel(level) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + # Add console handler (unless explicitly disabled) + log_to_console = os.getenv("LOG_TO_CONSOLE", "true").lower() != "false" + if log_to_console: + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(level) + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + + return logger diff --git a/tradingagents/utils/structured_output.py b/tradingagents/utils/structured_output.py index 606c6294..83c61254 100644 --- a/tradingagents/utils/structured_output.py +++ b/tradingagents/utils/structured_output.py @@ -5,48 +5,46 @@ Provides helper functions to easily configure LLMs for structured output across different providers (OpenAI, Anthropic, Google). """ -from typing import Type, Any, Dict +from typing import Any, Dict, Type + from pydantic import BaseModel def get_structured_llm(llm: Any, schema: Type[BaseModel]): """ Configure an LLM to return structured output based on a Pydantic schema. - + Args: llm: The LangChain LLM instance schema: Pydantic BaseModel class defining the expected output structure - + Returns: Configured LLM that returns structured output - + Example: ```python from tradingagents.schemas import TradeDecision from tradingagents.utils.structured_output import get_structured_llm - + structured_llm = get_structured_llm(llm, TradeDecision) response = structured_llm.invoke("Should I buy AAPL?") # response is a dict matching TradeDecision schema ``` """ - return llm.with_structured_output( - schema=schema.model_json_schema(), - method="json_schema" - ) + return llm.with_structured_output(schema=schema.model_json_schema(), method="json_schema") def extract_structured_response(response: Dict[str, Any], schema: 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