147 lines
6.2 KiB
Python
147 lines
6.2 KiB
Python
from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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build_optional_decision_context,
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get_language_instruction,
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summarize_structured_signal,
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truncate_prompt_text,
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use_compact_analysis_prompt,
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)
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from tradingagents.agents.utils.decision_utils import build_structured_decision
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def create_portfolio_manager(llm, memory):
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def portfolio_manager_node(state) -> dict:
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instrument_context = build_instrument_context(state["company_of_interest"])
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history = state["risk_debate_state"]["history"]
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risk_debate_state = state["risk_debate_state"]
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market_research_report = state["market_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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sentiment_report = state["sentiment_report"]
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research_plan = state["investment_plan"]
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trader_plan = state["trader_investment_plan"]
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research_structured = state.get("investment_plan_structured") or {}
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trader_structured = state.get("trader_investment_plan_structured") or {}
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portfolio_context = state.get("portfolio_context", "")
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peer_context = state.get("peer_context", "")
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decision_context = build_optional_decision_context(
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portfolio_context,
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peer_context,
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peer_context_mode=state.get("peer_context_mode", "UNSPECIFIED"),
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max_chars=550,
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)
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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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for i, rec in enumerate(past_memories, 1):
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past_memory_str += rec["recommendation"] + "\n\n"
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if use_compact_analysis_prompt():
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prompt = f"""As the Portfolio Manager, synthesize the risk debate and deliver the final rating.
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{instrument_context}
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Use exactly one rating: Buy / Overweight / Hold / Underweight / Sell.
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You already have enough evidence. Do not ask for more data and do not emit tool calls.
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Return with this exact header first:
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RATING: BUY|OVERWEIGHT|HOLD|UNDERWEIGHT|SELL
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HOLD_SUBTYPE: DEFENSIVE_HOLD|STAGED_BUY_HOLD|STANDARD_HOLD|N/A
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ENTRY_STYLE: IMMEDIATE|STAGED|WAIT_PULLBACK|EXISTING_ONLY|REDUCE|EXIT|UNKNOWN
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SAME_THEME_RANK: LEADER|UPPER|MIDDLE|LOWER|LAGGARD|UNKNOWN
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ACCOUNT_FIT: FAVORABLE|NEUTRAL|CROWDED_GROWTH|DEFENSIVE_REBALANCE|UNKNOWN
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Then return only:
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1. Executive summary
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2. Key risks
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Research plan: {truncate_prompt_text(research_plan, 500)}
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Research signal summary: {summarize_structured_signal(research_structured)}
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Trader plan: {truncate_prompt_text(trader_plan, 500)}
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Trader signal summary: {summarize_structured_signal(trader_structured)}
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Past lessons: {truncate_prompt_text(past_memory_str, 400)}
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{decision_context}
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Risk debate: {truncate_prompt_text(history, 1400)}{get_language_instruction()}"""
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else:
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prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
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{instrument_context}
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---
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**Rating Scale** (use exactly one):
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- **Buy**: Strong conviction to enter or add to position
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- **Overweight**: Favorable outlook, gradually increase exposure
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- **Hold**: Maintain current position, no action needed
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- **Underweight**: Reduce exposure, take partial profits
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- **Sell**: Exit position or avoid entry
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**Context:**
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- Research Manager's investment plan: **{research_plan}**
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- Research Manager structured signal: **{summarize_structured_signal(research_structured)}**
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- Trader's transaction proposal: **{trader_plan}**
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- Trader structured signal: **{summarize_structured_signal(trader_structured)}**
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- Lessons from past decisions: **{past_memory_str}**
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{decision_context}
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**Required Output Structure:**
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1. Start with these exact header lines:
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- `RATING: BUY|OVERWEIGHT|HOLD|UNDERWEIGHT|SELL`
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- `HOLD_SUBTYPE: DEFENSIVE_HOLD|STAGED_BUY_HOLD|STANDARD_HOLD|N/A`
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- `ENTRY_STYLE: IMMEDIATE|STAGED|WAIT_PULLBACK|EXISTING_ONLY|REDUCE|EXIT|UNKNOWN`
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- `SAME_THEME_RANK: LEADER|UPPER|MIDDLE|LOWER|LAGGARD|UNKNOWN`
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- `ACCOUNT_FIT: FAVORABLE|NEUTRAL|CROWDED_GROWTH|DEFENSIVE_REBALANCE|UNKNOWN`
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2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
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3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
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---
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**Risk Analysts Debate History:**
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{history}
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---
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Be decisive and ground every conclusion in specific evidence from the analysts.
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Do not ask for more data and do not emit tool calls.{get_language_instruction()}"""
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response = llm.invoke(prompt)
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structured_decision = build_structured_decision(
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response.content,
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fallback_candidates=(
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("trader_plan", trader_plan),
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("investment_plan", research_plan),
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),
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default_rating="HOLD",
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peer_context_mode=state.get("peer_context_mode", "UNSPECIFIED"),
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context_usage={
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"portfolio_context": bool(str(portfolio_context).strip()),
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"peer_context": bool(str(peer_context).strip()),
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},
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)
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new_risk_debate_state = {
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"judge_decision": structured_decision["report_text"],
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"history": risk_debate_state["history"],
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"aggressive_history": risk_debate_state["aggressive_history"],
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"conservative_history": risk_debate_state["conservative_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_aggressive_response": risk_debate_state["current_aggressive_response"],
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"current_conservative_response": risk_debate_state["current_conservative_response"],
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"current_neutral_response": risk_debate_state["current_neutral_response"],
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"count": risk_debate_state["count"],
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}
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return {
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"risk_debate_state": new_risk_debate_state,
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"final_trade_decision": structured_decision["rating"],
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"final_trade_decision_report": structured_decision["report_text"],
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"final_trade_decision_structured": structured_decision,
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}
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return portfolio_manager_node
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