292 lines
11 KiB
Python
292 lines
11 KiB
Python
# TradingAgents/prediction_market/graph/pm_trading_graph.py
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import os
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from pathlib import Path
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import json
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from datetime import date
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from typing import Dict, Any, Tuple, List, Optional
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from langgraph.prebuilt import ToolNode
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from tradingagents.llm_clients import create_llm_client
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from tradingagents.prediction_market.agents import *
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from tradingagents.prediction_market.pm_config import PM_DEFAULT_CONFIG
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from tradingagents.agents.utils.memory import FinancialSituationMemory
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from tradingagents.prediction_market.agents.utils.pm_agent_states import (
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PMAgentState,
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PMInvestDebateState,
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PMRiskDebateState,
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)
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# Import PM tool functions
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from tradingagents.prediction_market.agents.utils.pm_agent_utils import (
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get_market_info,
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get_market_price_history,
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get_order_book,
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get_resolution_criteria,
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get_event_context,
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get_related_markets,
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search_markets,
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get_news,
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get_global_news,
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)
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from .conditional_logic import PMConditionalLogic
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from .setup import PMGraphSetup
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from .propagation import PMPropagator
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from .reflection import PMReflector
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from .signal_processing import PMSignalProcessor
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class PMTradingAgentsGraph:
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"""Main class that orchestrates the prediction market trading agents framework."""
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def __init__(
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self,
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selected_analysts=["event", "odds", "information", "sentiment"],
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debug=False,
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config: Dict[str, Any] = None,
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callbacks: Optional[List] = None,
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):
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"""Initialize the prediction market trading agents graph and components.
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Args:
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selected_analysts: List of analyst types to include
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debug: Whether to run in debug mode
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config: Configuration dictionary. If None, uses PM default config
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callbacks: Optional list of callback handlers (e.g., for tracking LLM/tool stats)
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"""
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self.debug = debug
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self.config = config or PM_DEFAULT_CONFIG
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self.callbacks = callbacks or []
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# Create necessary directories
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os.makedirs(
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os.path.join(self.config["project_dir"], "dataflows/data_cache"),
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exist_ok=True,
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)
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# Initialize LLMs with provider-specific thinking configuration
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llm_kwargs = self._get_provider_kwargs()
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# Add callbacks to kwargs if provided (passed to LLM constructor)
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if self.callbacks:
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llm_kwargs["callbacks"] = self.callbacks
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deep_client = create_llm_client(
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provider=self.config["llm_provider"],
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model=self.config["deep_think_llm"],
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base_url=self.config.get("backend_url"),
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**llm_kwargs,
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)
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quick_client = create_llm_client(
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provider=self.config["llm_provider"],
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model=self.config["quick_think_llm"],
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base_url=self.config.get("backend_url"),
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**llm_kwargs,
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)
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self.deep_thinking_llm = deep_client.get_llm()
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self.quick_thinking_llm = quick_client.get_llm()
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# Initialize memories
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self.yes_memory = FinancialSituationMemory("yes_memory", self.config)
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self.no_memory = FinancialSituationMemory("no_memory", self.config)
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self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
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self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
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self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
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# Create tool nodes
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self.tool_nodes = self._create_tool_nodes()
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# Initialize components
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self.conditional_logic = PMConditionalLogic(
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max_debate_rounds=self.config["max_debate_rounds"],
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max_risk_discuss_rounds=self.config["max_risk_discuss_rounds"],
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)
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self.graph_setup = PMGraphSetup(
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self.quick_thinking_llm,
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self.deep_thinking_llm,
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self.tool_nodes,
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self.yes_memory,
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self.no_memory,
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self.trader_memory,
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self.invest_judge_memory,
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self.risk_manager_memory,
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self.conditional_logic,
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)
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self.propagator = PMPropagator()
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self.reflector = PMReflector(self.quick_thinking_llm)
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self.signal_processor = PMSignalProcessor(self.quick_thinking_llm)
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# State tracking
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self.curr_state = None
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self.market_id = None
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self.log_states_dict = {} # date to full state dict
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# Set up the graph
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self.graph = self.graph_setup.setup_graph(selected_analysts)
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def _get_provider_kwargs(self) -> Dict[str, Any]:
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"""Get provider-specific kwargs for LLM client creation."""
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kwargs = {}
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provider = self.config.get("llm_provider", "").lower()
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if provider == "google":
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thinking_level = self.config.get("google_thinking_level")
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if thinking_level:
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kwargs["thinking_level"] = thinking_level
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elif provider == "openai":
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reasoning_effort = self.config.get("openai_reasoning_effort")
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if reasoning_effort:
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kwargs["reasoning_effort"] = reasoning_effort
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return kwargs
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def _create_tool_nodes(self) -> Dict[str, ToolNode]:
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"""Create tool nodes for different prediction market data sources."""
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return {
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"event": ToolNode(
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[
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# Event context and resolution
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get_market_info,
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get_resolution_criteria,
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get_event_context,
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]
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),
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"odds": ToolNode(
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[
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# Price, order book, and market data
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get_market_info,
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get_market_price_history,
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get_order_book,
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]
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),
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"information": ToolNode(
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[
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# News and related markets
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get_news,
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get_global_news,
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get_related_markets,
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search_markets,
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]
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),
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"sentiment": ToolNode(
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[
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# News for sentiment analysis
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get_news,
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get_global_news,
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]
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),
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}
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def propagate(self, market_id, trade_date, market_question=""):
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"""Run the prediction market trading agents graph for a market on a specific date.
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Args:
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market_id: The Polymarket condition ID or market identifier
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trade_date: The date of analysis
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market_question: Optional full text of the market question
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"""
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self.market_id = market_id
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# Initialize state
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init_agent_state = self.propagator.create_initial_state(
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market_id, trade_date, market_question
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)
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args = self.propagator.get_graph_args()
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if self.debug:
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# Debug mode with tracing
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trace = []
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for chunk in self.graph.stream(init_agent_state, **args):
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if len(chunk["messages"]) == 0:
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pass
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else:
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chunk["messages"][-1].pretty_print()
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trace.append(chunk)
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final_state = trace[-1]
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else:
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# Standard mode without tracing
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final_state = self.graph.invoke(init_agent_state, **args)
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# Store current state for reflection
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self.curr_state = final_state
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# Log state
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self._log_state(trade_date, final_state)
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# Return decision and processed signal
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return final_state, self.process_signal(final_state["final_trade_decision"])
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def _log_state(self, trade_date, final_state):
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"""Log the final state to a JSON file."""
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self.log_states_dict[str(trade_date)] = {
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"market_id": final_state["market_id"],
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"market_question": final_state["market_question"],
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"trade_date": final_state["trade_date"],
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"event_report": final_state["event_report"],
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"odds_report": final_state["odds_report"],
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"information_report": final_state["information_report"],
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"sentiment_report": final_state["sentiment_report"],
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"investment_debate_state": {
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"yes_history": final_state["investment_debate_state"]["yes_history"],
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"no_history": final_state["investment_debate_state"]["no_history"],
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"history": final_state["investment_debate_state"]["history"],
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"current_response": final_state["investment_debate_state"][
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"current_response"
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],
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"judge_decision": final_state["investment_debate_state"][
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"judge_decision"
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],
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},
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"trader_investment_decision": final_state["trader_investment_plan"],
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"risk_debate_state": {
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"aggressive_history": final_state["risk_debate_state"]["aggressive_history"],
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"conservative_history": final_state["risk_debate_state"]["conservative_history"],
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"neutral_history": final_state["risk_debate_state"]["neutral_history"],
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"history": final_state["risk_debate_state"]["history"],
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"judge_decision": final_state["risk_debate_state"]["judge_decision"],
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},
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"investment_plan": final_state["investment_plan"],
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"final_trade_decision": final_state["final_trade_decision"],
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}
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# Save to file
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directory = Path(f"eval_results/{self.market_id}/PMTradingAgentsStrategy_logs/")
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directory.mkdir(parents=True, exist_ok=True)
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with open(
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f"eval_results/{self.market_id}/PMTradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
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"w",
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encoding="utf-8",
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) as f:
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json.dump(self.log_states_dict, f, indent=4)
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def reflect_and_remember(self, returns_losses):
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"""Reflect on decisions and update memory based on returns."""
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self.reflector.reflect_yes_researcher(
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self.curr_state, returns_losses, self.yes_memory
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)
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self.reflector.reflect_no_researcher(
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self.curr_state, returns_losses, self.no_memory
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)
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self.reflector.reflect_trader(
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self.curr_state, returns_losses, self.trader_memory
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)
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self.reflector.reflect_invest_judge(
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self.curr_state, returns_losses, self.invest_judge_memory
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)
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self.reflector.reflect_risk_manager(
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self.curr_state, returns_losses, self.risk_manager_memory
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)
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def process_signal(self, full_signal):
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"""Process a signal to extract the core decision."""
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return self.signal_processor.process_signal(full_signal)
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