mirror of
https://github.com/farcasclaudiu/TradingAgents.git
synced 2026-06-29 01:01:33 +03:00
feat: structured-output Trader and Research Manager (#434, finishes the trio)
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.
This commit is contained in:
Yijia-Xiao
@@ -5,35 +5,24 @@ Uses LangChain's ``with_structured_output`` so the LLM produces a typed
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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 to a free-text invocation and the existing heuristic rating parser.
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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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import logging
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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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logger = logging.getLogger(__name__)
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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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# Wrap once at agent construction; if the provider does not support
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# structured output we keep ``structured_llm`` as None and use the
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# free-text fallback for every call.
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try:
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structured_llm = llm.with_structured_output(PortfolioDecision)
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except (NotImplementedError, AttributeError) as exc:
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logger.warning(
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"Portfolio Manager: provider does not support with_structured_output (%s); "
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"falling back to free-text generation",
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exc,
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)
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structured_llm = None
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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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@@ -74,7 +63,13 @@ def create_portfolio_manager(llm):
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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_pm(structured_llm, llm, prompt)
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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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@@ -95,26 +90,3 @@ Be decisive and ground every conclusion in specific evidence from the analysts.{
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}
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return portfolio_manager_node
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def _invoke_pm(structured_llm, plain_llm, prompt: str) -> str:
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"""Run the PM call and return the markdown-rendered decision.
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Tries the structured-output path first; if it fails for any reason
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(provider does not support it, model returns malformed JSON, network
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glitch on the structured endpoint), falls back to the plain free-text
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invocation so the pipeline still produces a result.
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"""
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if structured_llm is not None:
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try:
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decision = structured_llm.invoke(prompt)
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return render_pm_decision(decision)
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except Exception as exc:
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logger.warning(
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"Portfolio Manager: structured-output invocation failed (%s); "
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"retrying once as free text",
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exc,
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)
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response = plain_llm.invoke(prompt)
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return response.content
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@@ -1,8 +1,18 @@
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"""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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@@ -24,31 +34,31 @@ def create_research_manager(llm):
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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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**Required Output Structure:**
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1. **Recommendation**: State one of Buy / Overweight / Hold / Underweight / Sell.
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2. **Rationale**: Summarise the key points from both sides and explain which arguments led to this recommendation.
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3. **Strategic Actions**: Concrete steps for the trader to implement the recommendation, including position sizing guidance consistent with the rating.
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Present your analysis conversationally, as if speaking naturally to a teammate.
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---
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**Debate History:**
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{history}"""
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response = llm.invoke(prompt)
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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": response.content,
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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": response.content,
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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": response.content,
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"investment_plan": investment_plan,
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}
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return research_manager_node
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