# TradingAgents/graph/trading_graph.py import os from copy import deepcopy from typing import Dict, Any, List, Optional from langgraph.prebuilt import ToolNode from tradingagents.llm_clients import create_llm_client 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 # Import the new abstract tool methods from agent_utils 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_transactions, get_global_news ) from .conditional_logic import ConditionalLogic from .setup import GraphSetup from .propagation import Propagator from .reflection import Reflector from .signal_processing import SignalProcessor class TradingAgentsGraph: """Main class that orchestrates the trading agents framework.""" QUICK_THINKING_ROLES = { "market", "social", "news", "fundamentals", "bull_researcher", "bear_researcher", "trader", "aggressive_analyst", "neutral_analyst", "conservative_analyst", } DEEP_THINKING_ROLES = { "research_manager", "portfolio_manager", } def __init__( self, selected_analysts=["market", "social", "news", "fundamentals"], debug=False, config: Dict[str, Any] = None, callbacks: Optional[List] = None, ): """Initialize the trading agents graph and components. Args: selected_analysts: List of analyst types to include debug: Whether to run in debug mode config: Configuration dictionary. If None, uses default config callbacks: Optional list of callback handlers (e.g., for tracking LLM/tool stats) """ self.debug = debug self.config = self._build_config(config) self.callbacks = callbacks or [] # Update the interface's config set_config(self.config) # Create necessary directories os.makedirs( os.path.join(self.config["project_dir"], "dataflows/data_cache"), exist_ok=True, ) self.quick_thinking_llm = self._create_legacy_llm("quick") self.deep_thinking_llm = self._create_legacy_llm("deep") self.role_llms = self._create_role_llms() # Initialize memories 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.portfolio_manager_memory = FinancialSituationMemory("portfolio_manager_memory", self.config) # Create tool nodes self.tool_nodes = self._create_tool_nodes() # Initialize components self.conditional_logic = ConditionalLogic( max_debate_rounds=self.config["max_debate_rounds"], max_risk_discuss_rounds=self.config["max_risk_discuss_rounds"], ) 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.portfolio_manager_memory, self.conditional_logic, role_llms=self.role_llms, ) self.propagator = Propagator() self.reflector = Reflector(self.quick_thinking_llm) self.signal_processor = SignalProcessor(self.quick_thinking_llm) # State tracking self.curr_state = None self.ticker = None self.log_states_dict = {} # date to full state dict # Set up the graph self.graph = self.graph_setup.setup_graph(selected_analysts) def _build_config(self, config: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Merge user config over defaults without mutating the shared defaults.""" return self._deep_merge_dicts(DEFAULT_CONFIG, config or {}) def _deep_merge_dicts( self, base: Dict[str, Any], override: Dict[str, Any], ) -> Dict[str, Any]: merged = deepcopy(base) for key, value in override.items(): if isinstance(value, dict) and isinstance(merged.get(key), dict): merged[key] = self._deep_merge_dicts(merged[key], value) else: merged[key] = deepcopy(value) return merged def _create_legacy_llm(self, thinker_depth: str): model_key = "deep_think_llm" if thinker_depth == "deep" else "quick_think_llm" provider = self.config["llm_provider"] llm_kwargs = self._get_provider_kwargs(provider) if self.callbacks: llm_kwargs["callbacks"] = self.callbacks client = create_llm_client( provider=provider, model=self.config[model_key], base_url=self.config.get("backend_url"), **llm_kwargs, ) return client.get_llm() def _create_role_llms(self) -> Dict[str, Any]: role_llms = {} for role in self.QUICK_THINKING_ROLES | self.DEEP_THINKING_ROLES: thinker_depth = "deep" if role in self.DEEP_THINKING_ROLES else "quick" role_llms[role] = self._create_routed_llm(role, thinker_depth) return role_llms def _create_routed_llm(self, role: str, thinker_depth: str): llm_config = self._resolve_llm_config(role, thinker_depth) llm_kwargs = self._get_provider_kwargs(llm_config["provider"]) if self.callbacks: llm_kwargs["callbacks"] = self.callbacks client = create_llm_client( provider=llm_config["provider"], model=llm_config["model"], base_url=llm_config.get("base_url"), **llm_kwargs, ) return client.get_llm() def _resolve_llm_config( self, role: str, thinker_depth: str, ) -> Dict[str, Any]: routing = self.config.get("llm_routing") or {} role_routes = routing.get("roles") or {} route = role_routes.get(role) or routing.get("default") or {} model_key = "deep_think_llm" if thinker_depth == "deep" else "quick_think_llm" provider = route.get("provider", self.config["llm_provider"]).lower() return { "provider": provider, "model": route.get("model", self.config[model_key]), "base_url": route.get("base_url", self.config.get("backend_url")), } def _get_provider_kwargs(self, provider: Optional[str] = None) -> Dict[str, Any]: """Get provider-specific kwargs for LLM client creation.""" kwargs = {} provider = (provider or self.config.get("llm_provider", "")).lower() if provider == "google": thinking_level = self.config.get("google_thinking_level") if thinking_level: kwargs["thinking_level"] = thinking_level elif provider == "openai": reasoning_effort = self.config.get("openai_reasoning_effort") if reasoning_effort: kwargs["reasoning_effort"] = reasoning_effort elif provider == "anthropic": effort = self.config.get("anthropic_effort") if effort: kwargs["effort"] = effort return kwargs def _create_tool_nodes(self) -> Dict[str, ToolNode]: """Create tool nodes for different data sources using abstract methods.""" return { "market": ToolNode( [ # Core stock data tools get_stock_data, # Technical indicators get_indicators, ] ), "social": ToolNode( [ # News tools for social media analysis get_news, ] ), "news": ToolNode( [ # News and insider information get_news, get_global_news, get_insider_transactions, ] ), "fundamentals": ToolNode( [ # Fundamental analysis tools get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, ] ), } def propagate(self, company_name, trade_date): """Run the trading agents graph for a company on a specific date.""" self.ticker = company_name # Initialize state init_agent_state = self.propagator.create_initial_state( company_name, trade_date ) args = self.propagator.get_graph_args() if self.debug: # Debug mode with tracing 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: # Standard mode without tracing final_state = self.graph.invoke(init_agent_state, **args) # Store current state for reflection self.curr_state = final_state # Log state self._log_state(trade_date, final_state) # Return decision and processed signal return final_state, self.process_signal(final_state["final_trade_decision"]) def _log_state(self, trade_date, final_state): """Log the final state to a JSON file.""" 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": { "aggressive_history": final_state["risk_debate_state"]["aggressive_history"], "conservative_history": final_state["risk_debate_state"]["conservative_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"], } # Save to file directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/") directory.mkdir(parents=True, exist_ok=True) with open( f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json", "w", encoding="utf-8", ) as f: json.dump(self.log_states_dict, f, indent=4) def reflect_and_remember(self, returns_losses): """Reflect on decisions and update memory based on returns.""" 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_portfolio_manager( self.curr_state, returns_losses, self.portfolio_manager_memory ) def process_signal(self, full_signal): """Process a signal to extract the core decision.""" return self.signal_processor.process_signal(full_signal)