141 lines
7.0 KiB
Python
141 lines
7.0 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_yfinance_news, get_analyst_sentiment, get_sector_performance, execute_text_tool_calls, needs_followup_call, execute_default_tools, generate_analysis_from_data
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from tradingagents.dataflows.config import get_config
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from tradingagents.log_utils import add_log, step_timer, symbol_progress
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ANALYST_RESPONSE_FORMAT = """
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RESPONSE FORMAT (follow this structure exactly):
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## EXECUTIVE SUMMARY
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2-3 sentences: Key sentiment finding and directional bias (BULLISH / BEARISH / NEUTRAL).
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## KEY DATA POINTS
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- Bullet list of the 5 most significant sentiment signals with specific numbers
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- Include analyst consensus, price target implied upside, sector positioning
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## SIGNAL ASSESSMENT
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Your overall sentiment reading: BULLISH / BEARISH / NEUTRAL
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1-2 sentences explaining why, referencing specific data.
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## RISK FACTORS
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2-3 specific risks or sentiment divergences.
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## CONFIDENCE: HIGH / MEDIUM / LOW
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1 sentence justifying your confidence level.
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| Signal Source | Finding | Sentiment | Weight |
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|--------------|---------|-----------|--------|
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| (fill with key sentiment signals) |
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RULES:
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- Maximum 3000 characters total
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- Do NOT repeat raw data verbatim — summarize trends and insights
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- Complete your ENTIRE analysis in a SINGLE response"""
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def create_social_media_analyst(llm):
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def social_media_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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tools = [
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get_yfinance_news,
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get_analyst_sentiment,
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get_sector_performance,
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]
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system_message = (
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"You are a sentiment and market perception analyst. Your job is to assess the overall market sentiment around a company "
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"by synthesizing multiple signal sources:\n"
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"- `get_yfinance_news`: Curated news from Yahoo Finance (multiple publishers) — analyze headlines, publishers, recency, and tone\n"
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"- `get_analyst_sentiment`: Wall Street consensus — price targets, buy/sell/hold distribution, implied upside/downside\n"
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"- `get_sector_performance`: Sector context — how the stock is positioned vs moving averages, 52-week range, beta, and index\n\n"
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"Synthesize these into a unified sentiment assessment. Quantify sentiment where possible (e.g., '70% of analysts rate Buy', "
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"'trading at 85% of 52-week range', 'implied upside of 15%'). Identify sentiment divergences (e.g., analysts bullish but "
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"price below moving averages). Do not simply state trends are mixed — provide specific, actionable insights."
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+ ANALYST_RESPONSE_FORMAT,
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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chain = prompt | llm.bind_tools(tools)
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step_timer.start_step("social_media_analyst")
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add_log("agent", "social_analyst", f"💬 Social Media Analyst calling LLM for {ticker}...")
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t0 = time.time()
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result = chain.invoke(state["messages"])
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elapsed = time.time() - t0
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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add_log("llm", "social_analyst", f"LLM responded in {elapsed:.1f}s ({len(report)} chars)")
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tool_results = execute_text_tool_calls(report, tools)
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if tool_results:
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add_log("data", "social_analyst", f"Executed {len(tool_results)} tool calls: {', '.join(t['name'] for t in tool_results)}")
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else:
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add_log("agent", "social_analyst", f"🔄 No tool calls found, proactively fetching data for {ticker}...")
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tool_results = execute_default_tools(tools, ticker, current_date)
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add_log("data", "social_analyst", f"Proactively fetched {len(tool_results)} data sources")
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if tool_results and needs_followup_call(report):
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add_log("agent", "social_analyst", f"🔄 Generating analysis from {len(tool_results)} tool results...")
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t1 = time.time()
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followup = generate_analysis_from_data(llm, tool_results, system_message, ticker, current_date)
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elapsed2 = time.time() - t1
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if followup and len(followup) > 100:
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report = followup
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add_log("llm", "social_analyst", f"Follow-up analysis generated in {elapsed2:.1f}s ({len(report)} chars)")
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add_log("agent", "social_analyst", f"✅ Sentiment report ready: {report[:300]}...")
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step_timer.end_step("social_media_analyst", "completed", report[:200])
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symbol_progress.step_done(ticker, "social_media_analyst")
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step_timer.update_details("social_media_analyst", {
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"system_prompt": system_message[:2000],
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"user_prompt": f"Analyze social media sentiment for {ticker} on {current_date}",
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"response": report[:3000],
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"tool_calls": tool_results if tool_results else [],
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})
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else:
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tool_call_info = [{"name": tc["name"], "args": str(tc.get("args", {}))[:200]} for tc in result.tool_calls]
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step_timer.set_details("social_media_analyst", {
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"system_prompt": system_message[:2000],
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"user_prompt": f"Analyze social media sentiment for {ticker} on {current_date}",
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"response": "(Pending - tool calls in progress)",
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"tool_calls": tool_call_info,
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})
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add_log("data", "social_analyst", f"LLM requested {len(result.tool_calls)} tool calls in {elapsed:.1f}s: {', '.join(tc['name'] for tc in result.tool_calls)}")
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return {
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"messages": [result],
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"sentiment_report": report,
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}
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return social_media_analyst_node
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