255 lines
10 KiB
Python
255 lines
10 KiB
Python
# TradingAgents/graph/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 langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langgraph.prebuilt import ToolNode
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from tradingagents.agents import *
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from tradingagents.default_config import DEFAULT_CONFIG
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from tradingagents.agents.utils.memory import FinancialSituationMemory
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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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from tradingagents.dataflows.interface import set_config
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from .conditional_logic import ConditionalLogic
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from .setup import GraphSetup
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from .propagation import Propagator
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from .reflection import Reflector
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from .signal_processing import SignalProcessor
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class TradingAgentsGraph:
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"""Main class that orchestrates the trading agents framework."""
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def __init__(
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self,
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selected_analysts=["market", "social", "news", "fundamentals"],
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debug=False,
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config: Dict[str, Any] = None,
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):
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"""Initialize the 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 default config
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"""
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self.debug = debug
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self.config = config or DEFAULT_CONFIG
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# Update the interface's config
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set_config(self.config)
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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
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if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
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self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
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self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
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elif self.config["llm_provider"].lower() == "anthropic":
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self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
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self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
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elif self.config["llm_provider"].lower() == "google":
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self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
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self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
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else:
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raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
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self.toolkit = Toolkit(config=self.config)
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# Initialize memories
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self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
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self.bear_memory = FinancialSituationMemory("bear_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 = ConditionalLogic()
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self.graph_setup = GraphSetup(
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self.quick_thinking_llm,
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self.deep_thinking_llm,
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self.toolkit,
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self.tool_nodes,
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self.bull_memory,
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self.bear_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 = Propagator()
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self.reflector = Reflector(self.quick_thinking_llm)
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self.signal_processor = SignalProcessor(self.quick_thinking_llm)
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# State tracking
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self.curr_state = None
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self.ticker = 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 _create_tool_nodes(self) -> Dict[str, ToolNode]:
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"""Create tool nodes for different data sources."""
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return {
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"market": ToolNode(
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[
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# online tools
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self.toolkit.get_YFin_data_online,
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self.toolkit.get_stockstats_indicators_report_online,
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# offline tools
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self.toolkit.get_YFin_data,
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self.toolkit.get_stockstats_indicators_report,
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]
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),
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"social": ToolNode(
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[
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# online tools
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self.toolkit.get_stock_news_openai,
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# offline tools
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self.toolkit.get_reddit_stock_info,
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]
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),
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"news": ToolNode(
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[
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# online tools
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self.toolkit.get_global_news_openai,
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self.toolkit.get_google_news,
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# offline tools
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self.toolkit.get_finnhub_news,
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self.toolkit.get_reddit_news,
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]
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),
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"fundamentals": ToolNode(
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[
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# online tools
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self.toolkit.get_fundamentals_openai,
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# offline tools
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self.toolkit.get_finnhub_company_insider_sentiment,
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self.toolkit.get_finnhub_company_insider_transactions,
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self.toolkit.get_simfin_balance_sheet,
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self.toolkit.get_simfin_cashflow,
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self.toolkit.get_simfin_income_stmt,
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]
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),
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}
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def propagate(self, company_name, trade_date):
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"""Run the trading agents graph for a company on a specific date."""
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self.ticker = company_name
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# Initialize state
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init_agent_state = self.propagator.create_initial_state(
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company_name, trade_date
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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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"company_of_interest": final_state["company_of_interest"],
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"trade_date": final_state["trade_date"],
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"market_report": final_state["market_report"],
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"sentiment_report": final_state["sentiment_report"],
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"news_report": final_state["news_report"],
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"fundamentals_report": final_state["fundamentals_report"],
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"investment_debate_state": {
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"bull_history": final_state["investment_debate_state"]["bull_history"],
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"bear_history": final_state["investment_debate_state"]["bear_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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"risky_history": final_state["risk_debate_state"]["risky_history"],
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"safe_history": final_state["risk_debate_state"]["safe_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.ticker}/TradingAgentsStrategy_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.ticker}/TradingAgentsStrategy_logs/full_states_log.json",
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"w",
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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_bull_researcher(
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self.curr_state, returns_losses, self.bull_memory
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)
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self.reflector.reflect_bear_researcher(
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self.curr_state, returns_losses, self.bear_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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