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https://github.com/TauricResearch/TradingAgents.git
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bba147798f
Extends the canonical structured-output pattern from the Portfolio Manager to the other two decision-making agents. Each of the three agents now returns a typed Pydantic instance via llm.with_structured_output() in a single primary call, and a render helper turns the result into the same markdown shape downstream agents and saved reports already consume. - ResearchPlan: 5-tier recommendation, conversational rationale, concrete strategic actions for the trader. - TraderProposal: 3-tier action (transaction direction is naturally Buy / Hold / Sell — position sizing happens later at the Portfolio Manager), reasoning, and optional entry_price / stop_loss / position_sizing. Rendered output preserves the trailing "FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**" line for backward compatibility with the analyst stop-signal text. - PortfolioDecision: 5-tier rating, executive summary, investment thesis, optional price_target / time_horizon (unchanged). The shared try-structured-then-fallback pattern is extracted into tradingagents/agents/utils/structured.py (bind_structured + invoke_structured_or_freetext) so all three agents go through the same code path and log the same warning when a provider lacks structured output and the agent falls back to free-text generation. Net effect for users: every saved markdown report (research/manager.md, trading/trader.md, portfolio/decision.md) now has consistent section headers across runs and providers, easier to scan. Net effect for the runtime: the rating extraction round-trip is gone — the rating comes from the structured response itself, not a second LLM call. SignalProcessor was already simplified to a heuristic adapter in the previous commit. 11 new tests in tests/test_structured_agents.py cover the Trader and Research Manager render functions, structured-output happy paths, and free-text fallback. Full suite: 88 tests pass in ~2s without API keys.
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
"""Portfolio Manager: synthesises the risk-analyst debate into the final decision.
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Uses LangChain's ``with_structured_output`` so the LLM produces a typed
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``PortfolioDecision`` directly, in a single call. The result is rendered
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back to markdown for storage in ``final_trade_decision`` so memory log,
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CLI display, and saved reports continue to consume the same shape they do
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today. When a provider does not expose structured output, the agent falls
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back gracefully to free-text generation.
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"""
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from __future__ import annotations
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from tradingagents.agents.schemas import PortfolioDecision, render_pm_decision
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_language_instruction,
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)
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from tradingagents.agents.utils.structured import (
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bind_structured,
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invoke_structured_or_freetext,
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)
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def create_portfolio_manager(llm):
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structured_llm = bind_structured(llm, PortfolioDecision, "Portfolio Manager")
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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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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
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else ""
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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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**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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final_trade_decision = invoke_structured_or_freetext(
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structured_llm,
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llm,
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prompt,
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render_pm_decision,
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"Portfolio Manager",
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)
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new_risk_debate_state = {
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"judge_decision": final_trade_decision,
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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": final_trade_decision,
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}
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return portfolio_manager_node
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