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https://github.com/farcasclaudiu/TradingAgents.git
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Yijia-Xiao
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.
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
"""Research Manager: turns the bull/bear debate into a structured investment plan for the trader."""
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from __future__ import annotations
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from tradingagents.agents.schemas import ResearchPlan, render_research_plan
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from tradingagents.agents.utils.agent_utils import build_instrument_context
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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_research_manager(llm):
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structured_llm = bind_structured(llm, ResearchPlan, "Research Manager")
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def research_manager_node(state) -> dict:
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instrument_context = build_instrument_context(state["company_of_interest"])
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history = state["investment_debate_state"].get("history", "")
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investment_debate_state = state["investment_debate_state"]
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prompt = f"""As the Research Manager and debate facilitator, your role is to critically evaluate this round of debate and deliver a clear, actionable investment plan for the trader.
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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 in the bull thesis; recommend taking or growing the position
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- **Overweight**: Constructive view; recommend gradually increasing exposure
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- **Hold**: Balanced view; recommend maintaining the current position
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- **Underweight**: Cautious view; recommend trimming exposure
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- **Sell**: Strong conviction in the bear thesis; recommend exiting or avoiding the position
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Commit to a clear stance whenever the debate's strongest arguments warrant one; reserve Hold for situations where the evidence on both sides is genuinely balanced.
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---
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**Debate History:**
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{history}"""
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investment_plan = 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_research_plan,
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"Research Manager",
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)
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new_investment_debate_state = {
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"judge_decision": investment_plan,
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"history": investment_debate_state.get("history", ""),
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"bear_history": investment_debate_state.get("bear_history", ""),
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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": investment_plan,
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"count": investment_debate_state["count"],
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}
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return {
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"investment_debate_state": new_investment_debate_state,
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"investment_plan": investment_plan,
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}
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return research_manager_node
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