mirror of
https://github.com/TauricResearch/TradingAgents.git
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d7b40a2a5c
Agents had no ground-truth ticker→company mapping, so the market analyst could pattern-match a price chart to the wrong company (e.g. TOTDY read as "TotalEnergies"), and every downstream agent inherited the bad framing. Resolve identity once at run start via a cached, fail-open yfinance lookup and inject company/sector/exchange into the shared instrument context that all twelve agents consume, with an explicit do-not-substitute instruction. Resolution runs on both the propagate() and CLI entry points. Also replaces the bare "Continue" message-clear placeholder, which some OpenAI-compatible providers interpreted as the user task, with a context-anchored placeholder carrying the resolved identity and date. #814 #888
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
# TradingAgents/graph/propagation.py
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from typing import Dict, Any, List, Optional
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from tradingagents.agents.utils.agent_states import (
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AgentState,
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InvestDebateState,
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RiskDebateState,
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)
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class Propagator:
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"""Handles state initialization and propagation through the graph."""
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def __init__(self, max_recur_limit=100):
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"""Initialize with configuration parameters."""
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self.max_recur_limit = max_recur_limit
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def create_initial_state(
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self,
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company_name: str,
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trade_date: str,
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asset_type: str = "stock",
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past_context: str = "",
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instrument_context: str = "",
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) -> Dict[str, Any]:
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"""Create the initial state for the agent graph.
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``instrument_context`` is the deterministic ticker-identity string
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resolved once at run start (see
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``TradingAgentsGraph.resolve_instrument_context``). When empty, agents
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fall back to ticker-only context via
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``get_instrument_context_from_state``.
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"""
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return {
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"messages": [("human", company_name)],
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"company_of_interest": company_name,
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"asset_type": asset_type,
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"instrument_context": instrument_context,
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"trade_date": str(trade_date),
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"past_context": past_context,
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"investment_debate_state": InvestDebateState(
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{
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"bull_history": "",
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"bear_history": "",
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"history": "",
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"current_response": "",
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"judge_decision": "",
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"count": 0,
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}
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),
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"risk_debate_state": RiskDebateState(
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{
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"aggressive_history": "",
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"conservative_history": "",
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"neutral_history": "",
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"history": "",
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"latest_speaker": "",
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"current_aggressive_response": "",
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"current_conservative_response": "",
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"current_neutral_response": "",
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"judge_decision": "",
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"count": 0,
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}
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),
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"market_report": "",
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"fundamentals_report": "",
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"sentiment_report": "",
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"news_report": "",
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}
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def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str, Any]:
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"""Get arguments for the graph invocation.
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Args:
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callbacks: Optional list of callback handlers for tool execution tracking.
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Note: LLM callbacks are handled separately via LLM constructor.
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"""
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config = {"recursion_limit": self.max_recur_limit}
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if callbacks:
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config["callbacks"] = callbacks
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return {
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"stream_mode": "values",
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"config": config,
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}
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