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TradingAgents
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Merge 25cc0e144c into fa4d01c23a

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Octopus 2026-04-23 01:17:40 -04:00 committed by GitHub
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5 changed files with 51 additions and 16 deletions
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16
tradingagents/agents/managers/portfolio_manager.py
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@ -22,6 +22,17 @@ def create_portfolio_manager(llm, memory):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
lessons_line = (
f"- Lessons from past decisions: **{past_memory_str.strip()}**\n"
if past_memories
else ""
)
thesis_instruction = (
"3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections."
if past_memories
else "3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate."
)
prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
{instrument_context}
@ -38,12 +49,11 @@ def create_portfolio_manager(llm, memory):
**Context:**
- Research Manager's investment plan: **{research_plan}**
- Trader's transaction proposal: **{trader_plan}**
- Lessons from past decisions: **{past_memory_str}**
{lessons_line}
**Required Output Structure:**
1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
{thesis_instruction}
---

13
tradingagents/agents/managers/research_manager.py
View File

@ -20,6 +20,12 @@ def create_research_manager(llm, memory):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
past_memory_block = (
f'Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting. \n\nHere are your past reflections on mistakes:\n"{past_memory_str.strip()}"\n\n'
if past_memories
else ""
)
prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
@ -29,12 +35,7 @@ Additionally, develop a detailed investment plan for the trader. This should inc
Your Recommendation: A decisive stance supported by the most convincing arguments.
Rationale: An explanation of why these arguments lead to your conclusion.
Strategic Actions: Concrete steps for implementing the recommendation.
Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting.
Here are your past reflections on mistakes:
\"{past_memory_str}\"
{instrument_context}
{past_memory_block}{instrument_context}
Here is the debate:
Debate History:

14
tradingagents/agents/researchers/bear_researcher.py
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@ -19,6 +19,17 @@ def create_bear_researcher(llm, memory):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
memory_section = (
f"Reflections from similar situations and lessons learned: {past_memory_str.strip()}\n"
if past_memories
else ""
)
memory_instruction = (
" You must also address reflections and learn from lessons and mistakes you made in the past."
if past_memories
else ""
)
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:
@ -37,8 +48,7 @@ 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.
{memory_section}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.{memory_instruction}
"""
response = llm.invoke(prompt)

14
tradingagents/agents/researchers/bull_researcher.py
View File

@ -19,6 +19,17 @@ def create_bull_researcher(llm, memory):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
memory_section = (
f"Reflections from similar situations and lessons learned: {past_memory_str.strip()}\n"
if past_memories
else ""
)
memory_instruction = (
" You must also address reflections and learn from lessons and mistakes you made in the past."
if past_memories
else ""
)
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:
@ -35,8 +46,7 @@ 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.
{memory_section}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.{memory_instruction}
"""
response = llm.invoke(prompt)

10
tradingagents/agents/trader/trader.py
View File

@ -20,8 +20,12 @@ def create_trader(llm, memory):
if past_memories:
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
else:
past_memory_str = "No past memories found."
memory_instruction = (
f" Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str.strip()}"
if past_memories
else ""
)
context = {
"role": "user",
@ -31,7 +35,7 @@ def create_trader(llm, memory):
messages = [
{
"role": "system",
"content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}""",
"content": f"You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation.{memory_instruction}",
},
context,
]