129 lines
5.0 KiB
Python
129 lines
5.0 KiB
Python
"""Portfolio Manager Decision Agent.
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Pure reasoning LLM agent (no tools). Synthesizes risk metrics, holding
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reviews, and prioritized candidates into a structured investment decision.
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Pattern: ``create_pm_decision_agent(llm)`` → closure (macro_synthesis pattern).
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"""
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from __future__ import annotations
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import json
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import logging
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.json_utils import extract_json
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logger = logging.getLogger(__name__)
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def create_pm_decision_agent(llm, config: dict | None = None):
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"""Create a PM decision agent node.
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Args:
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llm: A LangChain chat model instance (deep_think recommended).
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config: Portfolio configuration dictionary containing constraints.
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Returns:
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A node function ``pm_decision_node(state)`` compatible with LangGraph.
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"""
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cfg = config or {}
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constraints_str = (
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f"- Max position size: {cfg.get('max_position_pct', 0.15):.0%}\n"
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f"- Max sector exposure: {cfg.get('max_sector_pct', 0.35):.0%}\n"
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f"- Minimum cash reserve: {cfg.get('min_cash_pct', 0.05):.0%}\n"
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f"- Max total positions: {cfg.get('max_positions', 15)}\n"
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)
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def pm_decision_node(state):
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analysis_date = state.get("analysis_date") or ""
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portfolio_data_str = state.get("portfolio_data") or "{}"
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risk_metrics_str = state.get("risk_metrics") or "{}"
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holding_reviews_str = state.get("holding_reviews") or "{}"
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prioritized_candidates_str = state.get("prioritized_candidates") or "[]"
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context = f"""## Portfolio Constraints
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{constraints_str}
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## Portfolio Data
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{portfolio_data_str}
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## Risk Metrics
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{risk_metrics_str}
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## Holding Reviews
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{holding_reviews_str}
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## Prioritized Candidates
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{prioritized_candidates_str}
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"""
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system_message = (
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"You are a portfolio manager making final investment decisions. "
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"Given the constraints, risk metrics, holding reviews, and prioritized investment candidates, "
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"produce a structured JSON investment decision. "
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"## CONSTRAINTS COMPLIANCE:\n"
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"You MUST ensure your suggested buys and position sizes adhere to the portfolio constraints. "
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"If a high-conviction candidate would exceed the max position size or sector limit, "
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"YOU MUST adjust the suggested 'shares' downward to fit within the limit. "
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"Do not suggest buys that you know will be rejected by the risk engine.\n\n"
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"Consider: reducing risk where metrics are poor, acting on SELL recommendations, "
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"and adding positions in high-conviction candidates that pass constraints. "
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"For every BUY you MUST set a stop_loss price (maximum acceptable loss level, "
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"typically 5-15% below entry) and a take_profit price (expected sell target, "
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"typically 10-30% above entry based on your thesis). "
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"Output ONLY valid JSON matching this exact schema:\n"
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"{\n"
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' "sells": [{"ticker": "...", "shares": 0.0, "rationale": "..."}],\n'
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' "buys": [{"ticker": "...", "shares": 0.0, "price_target": 0.0, '
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'"stop_loss": 0.0, "take_profit": 0.0, '
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'"sector": "...", "rationale": "...", "thesis": "..."}],\n'
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' "holds": [{"ticker": "...", "rationale": "..."}],\n'
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' "cash_reserve_pct": 0.10,\n'
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' "portfolio_thesis": "...",\n'
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' "risk_summary": "..."\n'
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"}\n\n"
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"IMPORTANT: Output ONLY valid JSON. Start your response with '{' and end with '}'. "
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"Do NOT use markdown code fences. Do NOT include any explanation or preamble before or after the JSON.\n\n"
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f"{context}"
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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" For your reference, the current date is {current_date}.",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names="none")
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prompt = prompt.partial(current_date=analysis_date)
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chain = prompt | llm
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result = chain.invoke(state["messages"])
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raw = result.content or "{}"
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try:
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parsed = extract_json(raw)
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decision_str = json.dumps(parsed)
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except (ValueError, json.JSONDecodeError):
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logger.warning(
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"pm_decision_agent: could not extract JSON; storing raw (first 200): %s",
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raw[:200],
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)
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decision_str = raw
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return {
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"messages": [result],
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"pm_decision": decision_str,
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"sender": "pm_decision_agent",
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}
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return pm_decision_node
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