mirror of
https://github.com/farcasclaudiu/TradingAgents.git
synced 2026-06-22 07:01:21 +03:00
Yijia-Xiao
ebd2e12e67
The previous per-agent BM25 memory was effectively dead code — its only caller was a commented-out line in main.py. Replace it with a single append-only markdown decision log driven by the propagate() lifecycle. Lifecycle: - store_decision() appends a pending entry at the end of every run - _resolve_pending_entries() runs at the start of the next same-ticker run, fetches yfinance returns + alpha vs SPY, and writes one LLM reflection per resolved entry through an atomic temp-file rename - Portfolio Manager consumes state["past_context"] (5 most recent same-ticker entries plus 3 cross-ticker reflection-only excerpts) Storage at ~/.tradingagents/memory/trading_memory.md (override: TRADINGAGENTS_MEMORY_LOG_PATH). Tag schema: - Pending: [YYYY-MM-DD | TICKER | Rating | pending] - Resolved: [YYYY-MM-DD | TICKER | Rating | +X.X% | +Y.Y% | Nd] Removes rank-bm25 dependency and the legacy reflect_and_remember() plumbing across reflection.py, trading_graph.py, and the agent factories. 49 new tests in tests/test_memory_log.py cover the storage, deferred reflection, prompt injection, and legacy-removal paths. Full suite (58 tests) passes in under 2 seconds without API keys.
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction
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def create_portfolio_manager(llm):
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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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past_context = state.get("past_context", "")
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lessons_line = (
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f"- Lessons from prior decisions and outcomes:\n{past_context}\n"
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if past_context else ""
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)
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thesis_instruction = (
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"3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and the lessons from prior decisions."
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if past_context
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else "3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate."
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)
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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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- Trader's transaction proposal: **{trader_plan}**
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{lessons_line}
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**Required Output Structure:**
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1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
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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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{thesis_instruction}
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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.{get_language_instruction()}"""
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response = llm.invoke(prompt)
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new_risk_debate_state = {
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"judge_decision": response.content,
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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": response.content,
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}
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return portfolio_manager_node
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