# -*- coding: utf-8 -*- # TradingAgentsX/graph/trading_graph.py import os from pathlib import Path import json from datetime import date from typing import Dict, Any, Tuple, List, Optional # 匯入各種 LLM 的聊天模型 from langchain_openai import ChatOpenAI from langchain_anthropic import ChatAnthropic from langchain_google_genai import ChatGoogleGenerativeAI # 從 langgraph 匯入 ToolNode，用於將工具轉換為圖中的節點 from langgraph.prebuilt import ToolNode # 匯入專案內部的代理、設定和狀態管理模組 from tradingagents.agents import * from tradingagents.default_config import DEFAULT_CONFIG from tradingagents.agents.utils.memory import FinancialSituationMemory from tradingagents.agents.utils.agent_states import ( AgentState, InvestDebateState, RiskDebateState, ) from tradingagents.dataflows.config import set_config # 從 agent_utils 匯入新的抽象工具方法 from tradingagents.agents.utils.agent_utils import ( get_stock_data, get_indicators, get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_news, get_insider_sentiment, get_insider_transactions, get_global_news ) # 匯入圖的其他組件 from .conditional_logic import ConditionalLogic from .setup import GraphSetup from .propagation import Propagator from .reflection import Reflector from .signal_processing import SignalProcessor class TradingAgentsXGraph: """ 協調交易代理框架的主要類別。 這個類別整合了所有組件，包括 LLM、記憶體、工具和圖的邏輯， 以執行一個完整的金融分析和交易決策流程。 """ def __init__( self, selected_analysts=["market", "social", "news", "fundamentals"], debug=False, config: Dict[str, Any] = None, ): """ 初始化交易代理圖和組件。 Args: selected_analysts (list): 要包含的分析師類型列表。 debug (bool): 是否以除錯模式執行。 config (Dict[str, Any]): 設定字典。如果為 None，則使用預設設定。 """ self.debug = debug self.config = config or DEFAULT_CONFIG # 更新介面的設定 set_config(self.config) # 建立必要的目錄 os.makedirs( os.path.join(self.config["project_dir"], "dataflows/data_cache"), exist_ok=True, ) # 初始化 LLM # 初始化 LLM provider = self.config["llm_provider"].lower() # Get base URLs from config, defaulting to backend_url for backward compatibility deep_base_url = self.config.get("deep_think_base_url", self.config.get("backend_url")) quick_base_url = self.config.get("quick_think_base_url", self.config.get("backend_url")) # Get API keys from config deep_api_key = self.config.get("deep_think_api_key", os.getenv("OPENAI_API_KEY")) quick_api_key = self.config.get("quick_think_api_key", os.getenv("OPENAI_API_KEY")) # Helper to initialize LLM based on URL/Provider def _create_llm(model: str, base_url: str, api_key: str): # Use ChatOpenAI for all providers including Anthropic's OpenAI-compatible endpoint # Anthropic's /v1 endpoint is OpenAI-compatible, so we use ChatOpenAI return ChatOpenAI( model=model, base_url=base_url, openai_api_key=api_key, max_tokens=16000 # Prevent report truncation ) # Initialize LLMs independently print(f"DEBUG: Initializing Deep Thinking LLM: Model={self.config['deep_think_llm']}, BaseURL={deep_base_url}, Key={deep_api_key[:10]}...") self.deep_thinking_llm = _create_llm( self.config["deep_think_llm"], deep_base_url, deep_api_key ) print(f"DEBUG: Initializing Quick Thinking LLM: Model={self.config['quick_think_llm']}, BaseURL={quick_base_url}, Key={quick_api_key[:10]}...") self.quick_thinking_llm = _create_llm( self.config["quick_think_llm"], quick_base_url, quick_api_key ) # 初始化記憶體 self.bull_memory = FinancialSituationMemory("bull_memory", self.config) self.bear_memory = FinancialSituationMemory("bear_memory", self.config) self.trader_memory = FinancialSituationMemory("trader_memory", self.config) self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config) self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config) # 建立工具節點 self.tool_nodes = self._create_tool_nodes() # 初始化組件 self.conditional_logic = ConditionalLogic() self.graph_setup = GraphSetup( self.quick_thinking_llm, self.deep_thinking_llm, self.tool_nodes, self.bull_memory, self.bear_memory, self.trader_memory, self.invest_judge_memory, self.risk_manager_memory, self.conditional_logic, ) self.propagator = Propagator() self.reflector = Reflector(self.quick_thinking_llm) self.signal_processor = SignalProcessor(self.quick_thinking_llm) # 狀態追蹤 self.curr_state = None self.ticker = None self.log_states_dict = {} # 日期到完整狀態字典的映射 # 設定圖 self.graph = self.graph_setup.setup_graph(selected_analysts) def _create_tool_nodes(self) -> Dict[str, ToolNode]: """使用抽象方法為不同的資料來源建立工具節點。""" return { "market": ToolNode( [ # 核心股票數據工具 get_stock_data, # 技術指標 get_indicators, ] ), "social": ToolNode( [ # 用於社群媒體分析的新聞工具 get_news, ] ), "news": ToolNode( [ # 新聞和內部資訊 get_news, get_global_news, get_insider_sentiment, get_insider_transactions, ] ), "fundamentals": ToolNode( [ # 基本面分析工具 get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, ] ), } def propagate(self, company_name, trade_date): """ 在特定日期為某家公司執行交易代理圖。 Args: company_name (str): 公司名稱或股票代碼。 trade_date (str): 交易日期。 Returns: tuple: 包含最終狀態和處理後信號的元組。 """ # 防呆：將股票代碼轉換為大寫並去除空白 company_name = company_name.strip().upper() self.ticker = company_name # 初始化狀態 init_agent_state = self.propagator.create_initial_state( company_name, trade_date ) args = self.propagator.get_graph_args() if self.debug: # 帶有追蹤的除錯模式 trace = [] for chunk in self.graph.stream(init_agent_state, **args): if len(chunk["messages"]) == 0: pass else: chunk["messages"][-1].pretty_print() trace.append(chunk) final_state = trace[-1] else: # 不帶追蹤的標準模式 final_state = self.graph.invoke(init_agent_state, **args) # 儲存當前狀態以供反思 self.curr_state = final_state # 記錄狀態 self._log_state(trade_date, final_state) # 返回決策和處理後的信號 return final_state, self.process_signal(final_state["final_trade_decision"]) def _log_state(self, trade_date, final_state): """將最終狀態記錄到 JSON 檔案中。""" self.log_states_dict[str(trade_date)] = { "company_of_interest": final_state["company_of_interest"], "trade_date": final_state["trade_date"], "market_report": final_state["market_report"], "sentiment_report": final_state["sentiment_report"], "news_report": final_state["news_report"], "fundamentals_report": final_state["fundamentals_report"], "investment_debate_state": { "bull_history": final_state["investment_debate_state"]["bull_history"], "bear_history": final_state["investment_debate_state"]["bear_history"], "history": final_state["investment_debate_state"]["history"], "current_response": final_state["investment_debate_state"][ "current_response" ], "judge_decision": final_state["investment_debate_state"][ "judge_decision" ], }, "trader_investment_decision": final_state["trader_investment_plan"], "risk_debate_state": { "risky_history": final_state["risk_debate_state"]["risky_history"], "safe_history": final_state["risk_debate_state"]["safe_history"], "neutral_history": final_state["risk_debate_state"]["neutral_history"], "history": final_state["risk_debate_state"]["history"], "judge_decision": final_state["risk_debate_state"]["judge_decision"], }, "investment_plan": final_state["investment_plan"], "final_trade_decision": final_state["final_trade_decision"], } # 儲存到檔案 directory = Path(f"eval_results/{self.ticker}/TradingAgentsXStrategy_logs/") directory.mkdir(parents=True, exist_ok=True) with open( f"eval_results/{self.ticker}/TradingAgentsXStrategy_logs/full_states_log_{trade_date}.json", "w", ) as f: json.dump(self.log_states_dict, f, indent=4) def reflect_and_remember(self, returns_losses): """ 根據回報反思決策並更新記憶。 這個方法會觸發對每個相關代理的決策進行反思的過程。 """ self.reflector.reflect_bull_researcher( self.curr_state, returns_losses, self.bull_memory ) self.reflector.reflect_bear_researcher( self.curr_state, returns_losses, self.bear_memory ) self.reflector.reflect_trader( self.curr_state, returns_losses, self.trader_memory ) self.reflector.reflect_invest_judge( self.curr_state, returns_losses, self.invest_judge_memory ) self.reflector.reflect_risk_manager( self.curr_state, returns_losses, self.risk_manager_memory ) def process_signal(self, full_signal): """ 處理信號以提取核心決策。 將原始的 LLM 輸出轉換為標準化的交易信號（例如，BUY, SELL, HOLD）。 """ return self.signal_processor.process_signal(full_signal)