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

tradingagents/agents/researchers/bear_researcher.py

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+from langchain_core.messages import AIMessage
+import time
+import json
+
+
+def create_bear_researcher(llm, memory):
+    def bear_node(state) -> dict:
+        investment_debate_state = state["investment_debate_state"]
+        history = investment_debate_state.get("history", "")
+        bear_history = investment_debate_state.get("bear_history", "")
+
+        current_response = investment_debate_state.get("current_response", "")
+        market_research_report = state["market_report"]
+        sentiment_report = state["sentiment_report"]
+        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}"
+        past_memories = memory.get_memories(curr_situation, n_matches=2)
+
+        past_memory_str = ""
+        for i, rec in enumerate(past_memories, 1):
+            past_memory_str += rec["recommendation"] + "\n\n"
+
+        prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
+
+Key points to focus on:
+
+- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
+- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
+- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
+- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
+- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.
+
+Resources available:
+
+Market research report: {market_research_report}
+Social media sentiment report: {sentiment_report}
+Latest world affairs news: {news_report}
+Company fundamentals report: {fundamentals_report}
+Conversation history of the debate: {history}
+Last bull argument: {current_response}
+Reflections from similar situations and lessons learned: {past_memory_str}
+Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
+"""
+
+        response = llm.invoke(prompt)
+
+        argument = f"Bear Analyst: {response.content}"
+
+        new_investment_debate_state = {
+            "history": history + "\n" + argument,
+            "bear_history": bear_history + "\n" + argument,
+            "bull_history": investment_debate_state.get("bull_history", ""),
+            "current_response": argument,
+            "count": investment_debate_state["count"] + 1,
+        }
+
+        return {"investment_debate_state": new_investment_debate_state}
+
+    return bear_node

tradingagents/agents/researchers/bull_researcher.py

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+from langchain_core.messages import AIMessage
+import time
+import json
+
+
+def create_bull_researcher(llm, memory):
+    def bull_node(state) -> dict:
+        investment_debate_state = state["investment_debate_state"]
+        history = investment_debate_state.get("history", "")
+        bull_history = investment_debate_state.get("bull_history", "")
+
+        current_response = investment_debate_state.get("current_response", "")
+        market_research_report = state["market_report"]
+        sentiment_report = state["sentiment_report"]
+        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}"
+        past_memories = memory.get_memories(curr_situation, n_matches=2)
+
+        past_memory_str = ""
+        for i, rec in enumerate(past_memories, 1):
+            past_memory_str += rec["recommendation"] + "\n\n"
+
+        prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
+
+Key points to focus on:
+- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
+- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning.
+- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence.
+- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit.
+- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
+
+Resources available:
+Market research report: {market_research_report}
+Social media sentiment report: {sentiment_report}
+Latest world affairs news: {news_report}
+Company fundamentals report: {fundamentals_report}
+Conversation history of the debate: {history}
+Last bear argument: {current_response}
+Reflections from similar situations and lessons learned: {past_memory_str}
+Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
+"""
+
+        response = llm.invoke(prompt)
+
+        argument = f"Bull Analyst: {response.content}"
+
+        new_investment_debate_state = {
+            "history": history + "\n" + argument,
+            "bull_history": bull_history + "\n" + argument,
+            "bear_history": investment_debate_state.get("bear_history", ""),
+            "current_response": argument,
+            "count": investment_debate_state["count"] + 1,
+        }
+
+        return {"investment_debate_state": new_investment_debate_state}
+
+    return bull_node