416 lines
15 KiB
Python
416 lines
15 KiB
Python
# TradingAgents/graph/trading_graph.py
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import os
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from copy import deepcopy
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import json
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from pathlib import Path
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from typing import Dict, Any, 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.agents import *
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from tradingagents.default_config import DEFAULT_CONFIG, normalize_llm_routing
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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.config import set_config
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# Import the new abstract tool methods from agent_utils
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from tradingagents.agents.utils.agent_utils import (
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get_stock_data,
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get_indicators,
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get_fundamentals,
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get_balance_sheet,
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get_cashflow,
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get_income_statement,
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get_economic_indicators,
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get_fed_calendar,
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get_news,
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get_insider_transactions,
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get_global_news,
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get_yield_curve,
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)
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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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ALWAYS_ON_ROLES = {
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"bull_researcher",
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"bear_researcher",
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"research_manager",
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"trader",
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"aggressive_analyst",
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"neutral_analyst",
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"conservative_analyst",
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"portfolio_manager",
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}
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QUICK_THINKING_ROLES = {
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"market",
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"social",
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"news",
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"fundamentals",
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"macro",
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"bull_researcher",
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"bear_researcher",
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"trader",
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"aggressive_analyst",
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"neutral_analyst",
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"conservative_analyst",
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}
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DEEP_THINKING_ROLES = {
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"research_manager",
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"portfolio_manager",
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}
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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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callbacks: Optional[List] = 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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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 = self._build_config(config)
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self.callbacks = callbacks or []
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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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self.quick_thinking_llm = self._create_legacy_llm("quick")
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self.deep_thinking_llm = self._create_legacy_llm("deep")
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self.role_llms = self._create_role_llms(selected_analysts)
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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.portfolio_manager_memory = FinancialSituationMemory("portfolio_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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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 = GraphSetup(
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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.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.portfolio_manager_memory,
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self.conditional_logic,
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role_llms=self.role_llms,
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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 _build_config(self, config: Optional[Dict[str, Any]]) -> Dict[str, Any]:
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"""Merge user config over defaults without mutating the shared defaults."""
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return normalize_llm_routing(self._deep_merge_dicts(DEFAULT_CONFIG, config or {}))
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def _normalize_provider(self, provider: Optional[str]) -> str:
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return (provider or "").lower()
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def _deep_merge_dicts(
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self,
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base: Dict[str, Any],
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override: Dict[str, Any],
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) -> Dict[str, Any]:
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merged = deepcopy(base)
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for key, value in override.items():
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if isinstance(value, dict) and isinstance(merged.get(key), dict):
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merged[key] = self._deep_merge_dicts(merged[key], value)
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else:
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merged[key] = deepcopy(value)
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return merged
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def _create_legacy_llm(self, thinker_depth: str):
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model_key = "deep_think_llm" if thinker_depth == "deep" else "quick_think_llm"
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provider = self._normalize_provider(self.config["llm_provider"])
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llm_kwargs = self._get_provider_kwargs(provider)
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if self.callbacks:
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llm_kwargs["callbacks"] = self.callbacks
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client = create_llm_client(
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provider=provider,
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model=self.config[model_key],
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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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return client.get_llm()
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def _create_role_llms(self, selected_analysts: List[str]) -> Dict[str, Any]:
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role_llms = {}
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llm_cache = {}
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for role in self._get_required_roles(selected_analysts):
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thinker_depth = "deep" if role in self.DEEP_THINKING_ROLES else "quick"
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llm_config = self._resolve_llm_config(role, thinker_depth)
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if self._uses_legacy_llm(llm_config, thinker_depth):
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continue
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cache_key = (
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llm_config["provider"],
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llm_config["model"],
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llm_config.get("base_url"),
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)
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if cache_key not in llm_cache:
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llm_cache[cache_key] = self._create_llm_from_config(llm_config)
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role_llms[role] = llm_cache[cache_key]
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return role_llms
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def _get_required_roles(self, selected_analysts: List[str]) -> set[str]:
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return self.ALWAYS_ON_ROLES | set(selected_analysts)
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def _create_llm_from_config(self, llm_config: Dict[str, Any]):
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llm_kwargs = self._get_provider_kwargs(llm_config["provider"])
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if self.callbacks:
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llm_kwargs["callbacks"] = self.callbacks
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client = create_llm_client(
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provider=llm_config["provider"],
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model=llm_config["model"],
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base_url=llm_config.get("base_url"),
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**llm_kwargs,
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)
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return client.get_llm()
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def _uses_legacy_llm(self, llm_config: Dict[str, Any], thinker_depth: str) -> bool:
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model_key = "deep_think_llm" if thinker_depth == "deep" else "quick_think_llm"
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return (
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llm_config["provider"] == self._normalize_provider(self.config["llm_provider"])
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and llm_config["model"] == self.config[model_key]
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and llm_config.get("base_url") == self.config.get("backend_url")
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)
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def _resolve_llm_config(
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self,
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role: str,
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thinker_depth: str,
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) -> Dict[str, Any]:
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routing = self.config.get("llm_routing") or {}
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role_routes = routing.get("roles") or {}
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model_key = "deep_think_llm" if thinker_depth == "deep" else "quick_think_llm"
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legacy_provider = self._normalize_provider(self.config["llm_provider"])
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legacy_route = {
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"provider": legacy_provider,
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"model": self.config[model_key],
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"base_url": self.config.get("backend_url"),
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}
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default_route = routing.get("default") or {}
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role_route = role_routes.get(role) or {}
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route = self._deep_merge_dicts(legacy_route, default_route)
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route = self._deep_merge_dicts(route, role_route)
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route["provider"] = self._normalize_provider(route.get("provider"))
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explicit_routed_base_url = "base_url" in default_route or "base_url" in role_route
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if route["provider"] != legacy_provider and not explicit_routed_base_url:
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route["base_url"] = None
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return route
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def _get_provider_kwargs(self, provider: Optional[str] = None) -> 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 = (provider or 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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elif provider == "anthropic":
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effort = self.config.get("anthropic_effort")
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if effort:
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kwargs["effort"] = 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 data sources using abstract methods."""
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return {
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"market": ToolNode(
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[
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# Core stock data tools
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get_stock_data,
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# Technical indicators
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get_indicators,
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]
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),
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"social": ToolNode(
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[
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# News tools for social media analysis
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get_news,
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]
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),
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"news": ToolNode(
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[
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# News and insider information
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get_news,
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get_global_news,
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get_insider_transactions,
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]
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),
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"fundamentals": ToolNode(
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[
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# Fundamental analysis tools
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get_fundamentals,
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get_balance_sheet,
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get_cashflow,
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get_income_statement,
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]
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),
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"macro": ToolNode(
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[
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# Macroeconomic analysis tools
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar,
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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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"macro_report": final_state.get("macro_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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"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.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_{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_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_portfolio_manager(
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self.curr_state, returns_losses, self.portfolio_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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