# TradingAgents/graph/trading_graph.py import os import re 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 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.ace import TradingACE, create_trading_ace # Import the new abstract tool methods from agent_utils from tradingagents.agents.utils.agent_utils import ( get_stock_data, get_indicators, get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_news, get_insider_sentiment, get_insider_transactions, get_global_news ) from .conditional_logic import ConditionalLogic from .setup import GraphSetup from .propagation import Propagator from .reflection import Reflector from .signal_processing import SignalProcessor from tradingagents.ace import TradingACE, create_trading_ace class TradingAgentsGraph: """Main class that orchestrates the trading agents framework.""" def __init__( self, selected_analysts=["market", "social", "news", "fundamentals"], debug=False, config: Dict[str, Any] = None, ace_enabled: bool = True, ace_skillbook_path: Optional[str] = None, ): """Initialize the trading agents graph and components. Args: selected_analysts: List of analyst types to include debug: Whether to run in debug mode config: Configuration dictionary. If None, uses default config ace_enabled: Whether to enable ACE learning (default: True) ace_skillbook_path: Path to load/save ACE skillbook (optional) """ self.debug = debug self.config = config or DEFAULT_CONFIG # Update the interface's config set_config(self.config) # Create necessary directories os.makedirs( os.path.join(self.config["project_dir"], "dataflows/data_cache"), exist_ok=True, ) # 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"], base_url=self.config["backend_url"]) self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"]) elif self.config["llm_provider"].lower() == "anthropic": self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"]) self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"]) elif self.config["llm_provider"].lower() == "google": self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"]) self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"]) else: raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}") # Initialize memories self.bull_memory = FinancialSituationMemory("bull_memory", self.config) self.bear_memory = FinancialSituationMemory("bear_memory", self.config) self.trader_memory = FinancialSituationMemory("trader_memory", self.config) self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config) self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config) # Initialize ACE Engine self.ace_enabled = ace_enabled self.ace_skillbook_path = ace_skillbook_path or self.config.get( "ace_skillbook_path", os.path.join(self.config.get("results_dir", "./results"), "ace_skillbook.json") ) if self.ace_enabled: self.ace_engine = create_trading_ace( config=self.config, skillbook_path=self.ace_skillbook_path, ) else: self.ace_engine = None if self.ace_enabled: self.ace_engine = create_trading_ace( config=self.config, skillbook_path=self.ace_skillbook_path, ) else: self.ace_engine = None # Create tool nodes self.tool_nodes = self._create_tool_nodes() # Initialize components self.conditional_logic = ConditionalLogic() self.graph_setup = GraphSetup( self.quick_thinking_llm, self.deep_thinking_llm, self.tool_nodes, self.bull_memory, self.bear_memory, self.trader_memory, self.invest_judge_memory, self.risk_manager_memory, self.conditional_logic, ace_context=self.get_ace_context(), ) self.propagator = Propagator() self.reflector = Reflector(self.quick_thinking_llm) self.signal_processor = SignalProcessor(self.quick_thinking_llm) # State tracking self.curr_state = None self.ticker = None self.log_states_dict = {} # date to full state dict # Set up the graph self.graph = self.graph_setup.setup_graph(selected_analysts) def _create_tool_nodes(self) -> Dict[str, ToolNode]: """Create tool nodes for different data sources using abstract methods.""" return { "market": ToolNode( [ # Core stock data tools get_stock_data, # Technical indicators get_indicators, ] ), "social": ToolNode( [ # News tools for social media analysis get_news, ] ), "news": ToolNode( [ # News and insider information get_news, get_global_news, get_insider_sentiment, get_insider_transactions, ] ), "fundamentals": ToolNode( [ # Fundamental analysis tools get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, ] ), } def propagate(self, company_name, trade_date): """Run the trading agents graph for a company on a specific date.""" self.ticker = company_name # Initialize state init_agent_state = self.propagator.create_initial_state( company_name, trade_date ) args = self.propagator.get_graph_args() if self.debug: # Debug mode with tracing trace = [] for chunk in self.graph.stream(init_agent_state, **args): if len(chunk["messages"]) == 0: pass else: chunk["messages"][-1].pretty_print() trace.append(chunk) final_state = trace[-1] else: # Standard mode without tracing final_state = self.graph.invoke(init_agent_state, **args) # Store current state for reflection self.curr_state = final_state # Log state self._log_state(trade_date, final_state) # Return decision and processed signal return final_state, self.process_signal(final_state["final_trade_decision"]) def _log_state(self, trade_date, final_state): """Log the final state to a JSON file.""" self.log_states_dict[str(trade_date)] = { "company_of_interest": final_state["company_of_interest"], "trade_date": final_state["trade_date"], "market_report": final_state["market_report"], "sentiment_report": final_state["sentiment_report"], "news_report": final_state["news_report"], "fundamentals_report": final_state["fundamentals_report"], "investment_debate_state": { "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" ], }, "trader_investment_decision": final_state["trader_investment_plan"], "risk_debate_state": { "risky_history": final_state["risk_debate_state"]["risky_history"], "safe_history": final_state["risk_debate_state"]["safe_history"], "neutral_history": final_state["risk_debate_state"]["neutral_history"], "history": final_state["risk_debate_state"]["history"], "judge_decision": final_state["risk_debate_state"]["judge_decision"], }, "investment_plan": final_state["investment_plan"], "final_trade_decision": final_state["final_trade_decision"], } # Save to file directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/") directory.mkdir(parents=True, exist_ok=True) with open( f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json", "w", encoding="utf-8" ) as f: json.dump(self.log_states_dict, f, indent=4) def reflect_and_remember(self, returns_losses): """Reflect on decisions and update memory based on returns.""" 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 ) self.reflector.reflect_risk_manager( self.curr_state, returns_losses, self.risk_manager_memory ) # ACE Learning: Learn from trading execution if self.ace_enabled and self.ace_engine and self.curr_state: self._ace_learn(returns_losses) def _ace_learn_from_analysis(self): """ Trigger ACE learning based on the analytical consistency of all reports. """ if not self.ace_enabled or not self.ace_engine or not self.curr_state: return print(f"DEBUG: ACE analytical reflection triggered for {self.curr_state.get('company_of_interest')}") reports = { "ticker": self.curr_state.get("company_of_interest", "Unknown"), "date": str(self.curr_state.get("trade_date", "Unknown")), "market": self.curr_state.get("market_report", ""), "sentiment": self.curr_state.get("sentiment_report", ""), "news": self.curr_state.get("news_report", ""), "fundamentals": self.curr_state.get("fundamentals_report", ""), "plan": self.curr_state.get("investment_plan", ""), } decision = self.curr_state.get("final_trade_decision", "") decision = re.sub(r"\[ACE_METADATA: .*\]", "", decision).strip() self.ace_engine.learn_from_analysis( reports=reports, decision=decision ) def _ace_learn(self, returns_losses): """Apply ACE learning from the current trading state using Kayba ACE.""" if not self.curr_state: return context = f"{self.curr_state['company_of_interest']} on {self.curr_state['trade_date']}" market_data = "\n\n".join([ f"Market Report:\n{self.curr_state.get('market_report', '')}", f"Sentiment Report:\n{self.curr_state.get('sentiment_report', '')}", f"News Report:\n{self.curr_state.get('news_report', '')}", f"Fundamentals Report:\n{self.curr_state.get('fundamentals_report', '')}", ]) decision = self.curr_state.get("final_trade_decision", "") self.ace_engine.learn_from_trade( context=context, decision=decision, result=str(returns_losses), market_data=market_data, ) def get_ace_context(self) -> str: """Get ACE strategies context for injection into agent prompts.""" if self.ace_enabled and self.ace_engine: return self.ace_engine.get_skills_context() return "" def save_ace_skillbook(self, path: Optional[str] = None) -> str: """Save the ACE skillbook to a file.""" if self.ace_engine: return self.ace_engine.save_skillbook(path) return "" def get_ace_stats(self) -> Dict[str, Any]: """Get ACE learning statistics.""" if self.ace_engine: return self.ace_engine.get_stats() return {} def _ace_learn_from_analysis(self): """ Trigger ACE learning based on the analytical consistency of all reports. """ if not self.ace_enabled or not self.ace_engine or not self.curr_state: return print(f"DEBUG: ACE analytical reflection triggered for {self.curr_state.get('company_of_interest')}") reports = { "ticker": self.curr_state.get("company_of_interest", "Unknown"), "date": str(self.curr_state.get("trade_date", "Unknown")), "market": self.curr_state.get("market_report", ""), "sentiment": self.curr_state.get("sentiment_report", ""), "news": self.curr_state.get("news_report", ""), "fundamentals": self.curr_state.get("fundamentals_report", ""), "plan": self.curr_state.get("investment_plan", ""), } decision = self.curr_state.get("final_trade_decision", "") # Clean metadata tag if present decision = re.sub(r"\[ACE_METADATA: .*\]", "", decision).strip() self.ace_engine.learn_from_analysis( reports=reports, decision=decision ) def get_ace_context(self) -> str: """Get ACE strategies context for injection into agent prompts.""" if self.ace_enabled and self.ace_engine: return self.ace_engine.get_skills_context() return "" def save_ace_skillbook(self, path: Optional[str] = None) -> str: """Save the ACE skillbook to a file.""" if self.ace_engine: return self.ace_engine.save_skillbook(path) return "" def get_ace_stats(self) -> Dict[str, Any]: """Get ACE learning statistics.""" if self.ace_engine: return self.ace_engine.get_stats() return {} def process_signal(self, full_signal): """Process a signal to extract the core decision.""" return self.signal_processor.process_signal(full_signal)