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feat: replace per-agent BM25 memory with persistent append-only decision log
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Zhigong Liu
@@ -1,120 +1,53 @@
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# TradingAgents/graph/reflection.py
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from typing import Any, Dict
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from typing import Any
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class Reflector:
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"""Handles reflection on decisions and updating memory."""
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"""Handles reflection on trading decisions."""
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def __init__(self, quick_thinking_llm: Any):
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"""Initialize the reflector with an LLM."""
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self.quick_thinking_llm = quick_thinking_llm
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self.reflection_system_prompt = self._get_reflection_prompt()
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self.log_reflection_prompt = self._get_log_reflection_prompt()
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def _get_reflection_prompt(self) -> str:
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"""Get the system prompt for reflection."""
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return """
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You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis.
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Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
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def _get_log_reflection_prompt(self) -> str:
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"""Concise prompt for reflect_on_final_decision (Phase B log entries).
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1. Reasoning:
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- For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite.
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- Analyze the contributing factors to each success or mistake. Consider:
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- Market intelligence.
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- Technical indicators.
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- Technical signals.
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- Price movement analysis.
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- Overall market data analysis
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- News analysis.
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- Social media and sentiment analysis.
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- Fundamental data analysis.
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- Weight the importance of each factor in the decision-making process.
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Produces 2-4 sentences of plain prose — compact enough to be re-injected
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into future agent prompts without bloating the context window.
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"""
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return (
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"You are a trading analyst reviewing your own past decision now that the outcome is known.\n"
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"Write exactly 2-4 sentences of plain prose (no bullets, no headers, no markdown).\n\n"
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"Cover in order:\n"
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"1. Was the directional call correct? (cite the alpha figure)\n"
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"2. Which part of the investment thesis held or failed?\n"
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"3. One concrete lesson to apply to the next similar analysis.\n\n"
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"Be specific and terse. Your output will be stored verbatim in a decision log "
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"and re-read by future analysts, so every word must earn its place."
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)
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2. Improvement:
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- For any incorrect decisions, propose revisions to maximize returns.
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- Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date).
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3. Summary:
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- Summarize the lessons learned from the successes and mistakes.
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- Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
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4. Query:
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- Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
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- Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
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Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis.
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"""
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def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
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"""Extract the current market situation from the state."""
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curr_market_report = current_state["market_report"]
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curr_sentiment_report = current_state["sentiment_report"]
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curr_news_report = current_state["news_report"]
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curr_fundamentals_report = current_state["fundamentals_report"]
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return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}"
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def _reflect_on_component(
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self, component_type: str, report: str, situation: str, returns_losses
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def reflect_on_final_decision(
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self,
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final_decision: str,
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raw_return: float,
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alpha_return: float,
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) -> str:
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"""Generate reflection for a component."""
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"""Single reflection call on the final trade decision with outcome context.
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Used by Phase B deferred reflection. The final_trade_decision already
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synthesises all analyst insights, so no separate market context is needed.
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"""
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messages = [
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("system", self.reflection_system_prompt),
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("system", self.log_reflection_prompt),
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(
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"human",
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f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
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(
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f"Raw return: {raw_return:+.1%}\n"
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f"Alpha vs SPY: {alpha_return:+.1%}\n\n"
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f"Final Decision:\n{final_decision}"
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),
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),
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]
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result = self.quick_thinking_llm.invoke(messages).content
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return result
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def reflect_bull_researcher(self, current_state, returns_losses, bull_memory):
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"""Reflect on bull researcher's analysis and update memory."""
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situation = self._extract_current_situation(current_state)
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bull_debate_history = current_state["investment_debate_state"]["bull_history"]
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result = self._reflect_on_component(
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"BULL", bull_debate_history, situation, returns_losses
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)
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bull_memory.add_situations([(situation, result)])
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def reflect_bear_researcher(self, current_state, returns_losses, bear_memory):
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"""Reflect on bear researcher's analysis and update memory."""
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situation = self._extract_current_situation(current_state)
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bear_debate_history = current_state["investment_debate_state"]["bear_history"]
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result = self._reflect_on_component(
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"BEAR", bear_debate_history, situation, returns_losses
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)
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bear_memory.add_situations([(situation, result)])
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def reflect_trader(self, current_state, returns_losses, trader_memory):
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"""Reflect on trader's decision and update memory."""
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situation = self._extract_current_situation(current_state)
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trader_decision = current_state["trader_investment_plan"]
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result = self._reflect_on_component(
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"TRADER", trader_decision, situation, returns_losses
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)
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trader_memory.add_situations([(situation, result)])
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def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory):
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"""Reflect on investment judge's decision and update memory."""
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situation = self._extract_current_situation(current_state)
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judge_decision = current_state["investment_debate_state"]["judge_decision"]
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result = self._reflect_on_component(
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"INVEST JUDGE", judge_decision, situation, returns_losses
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)
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invest_judge_memory.add_situations([(situation, result)])
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def reflect_portfolio_manager(self, current_state, returns_losses, portfolio_manager_memory):
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"""Reflect on portfolio manager's decision and update memory."""
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situation = self._extract_current_situation(current_state)
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judge_decision = current_state["risk_debate_state"]["judge_decision"]
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result = self._reflect_on_component(
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"PORTFOLIO MANAGER", judge_decision, situation, returns_losses
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)
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portfolio_manager_memory.add_situations([(situation, result)])
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return self.quick_thinking_llm.invoke(messages).content
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