chore(release): v0.1.0 – initial public release of TradingAgents

tradingagents/graph/reflection.py

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+# TradingAgents/graph/reflection.py
+
+from typing import Dict, Any
+from langchain_openai import ChatOpenAI
+
+
+class Reflector:
+    """Handles reflection on decisions and updating memory."""
+
+    def __init__(self, quick_thinking_llm: ChatOpenAI):
+        """Initialize the reflector with an LLM."""
+        self.quick_thinking_llm = quick_thinking_llm
+        self.reflection_system_prompt = self._get_reflection_prompt()
+
+    def _get_reflection_prompt(self) -> str:
+        """Get the system prompt for reflection."""
+        return """
+You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis. 
+Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
+
+1. Reasoning:
+   - 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.
+   - Analyze the contributing factors to each success or mistake. Consider:
+     - Market intelligence.
+     - Technical indicators.
+     - Technical signals.
+     - Price movement analysis.
+     - Overall market data analysis 
+     - News analysis.
+     - Social media and sentiment analysis.
+     - Fundamental data analysis.
+     - Weight the importance of each factor in the decision-making process.
+
+2. Improvement:
+   - For any incorrect decisions, propose revisions to maximize returns.
+   - 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).
+
+3. Summary:
+   - Summarize the lessons learned from the successes and mistakes.
+   - Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
+
+4. Query:
+   - Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
+   - Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
+
+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.
+"""
+
+    def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
+        """Extract the current market situation from the state."""
+        curr_market_report = current_state["market_report"]
+        curr_sentiment_report = current_state["sentiment_report"]
+        curr_news_report = current_state["news_report"]
+        curr_fundamentals_report = current_state["fundamentals_report"]
+
+        return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}"
+
+    def _reflect_on_component(
+        self, component_type: str, report: str, situation: str, returns_losses
+    ) -> str:
+        """Generate reflection for a component."""
+        messages = [
+            ("system", self.reflection_system_prompt),
+            (
+                "human",
+                f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
+            ),
+        ]
+
+        result = self.quick_thinking_llm.invoke(messages).content
+        return result
+
+    def reflect_bull_researcher(self, current_state, returns_losses, bull_memory):
+        """Reflect on bull researcher's analysis and update memory."""
+        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
+        )
+        bull_memory.add_situations([(situation, result)])
+
+    def reflect_bear_researcher(self, current_state, returns_losses, bear_memory):
+        """Reflect on bear researcher's analysis and update memory."""
+        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
+        )
+        bear_memory.add_situations([(situation, result)])
+
+    def reflect_trader(self, current_state, returns_losses, trader_memory):
+        """Reflect on trader's decision and update memory."""
+        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
+        )
+        trader_memory.add_situations([(situation, result)])
+
+    def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory):
+        """Reflect on investment judge's decision and update memory."""
+        situation = self._extract_current_situation(current_state)
+        judge_decision = current_state["investment_debate_state"]["judge_decision"]
+
+        result = self._reflect_on_component(
+            "INVEST JUDGE", judge_decision, situation, returns_losses
+        )
+        invest_judge_memory.add_situations([(situation, result)])
+
+    def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory):
+        """Reflect on risk manager's decision and update memory."""
+        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
+        )
+        risk_manager_memory.add_situations([(situation, result)])