mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2026-06-16 21:06:15 +03:00
db059034a2
Surface live, market-implied probabilities for forward-looking events (Fed decisions, recession, elections, geopolitics, crypto) to the news analyst via a new get_prediction_markets tool and a prediction_markets vendor category. Backed by Polymarket's public Gamma API (no key). Results are filtered to open, forward-looking markets (closed and past-dated events excluded), ranked by traded volume, and rendered with implied probability, volume, resolution date, and the recent move. External errors degrade to a clear unavailable message rather than interrupting the analyst.
473 lines
19 KiB
Python
473 lines
19 KiB
Python
# TradingAgents/graph/trading_graph.py
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import logging
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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 datetime, timedelta
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from typing import Dict, Any, Tuple, List, Optional
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import yfinance as yf
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logger = logging.getLogger(__name__)
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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
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from tradingagents.agents.utils.memory import TradingMemoryLog
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from tradingagents.dataflows.utils import safe_ticker_component
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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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build_instrument_context,
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resolve_instrument_identity,
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get_stock_data,
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get_indicators,
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get_verified_market_snapshot,
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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_news,
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get_insider_transactions,
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get_global_news,
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get_macro_indicators,
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get_prediction_markets
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)
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from .checkpointer import checkpoint_step, clear_checkpoint, get_checkpointer, thread_id
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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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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 = config or DEFAULT_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(self.config["data_cache_dir"], exist_ok=True)
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os.makedirs(self.config["results_dir"], exist_ok=True)
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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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self.memory_log = TradingMemoryLog(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.conditional_logic,
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analyst_concurrency_limit=self.config.get("analyst_concurrency_limit", 1),
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)
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self.propagator = Propagator(
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max_recur_limit=self.config.get("max_recur_limit", 100),
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)
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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: keep the workflow for recompilation with a checkpointer.
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self.workflow = self.graph_setup.setup_graph(selected_analysts)
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self.graph = self.workflow.compile()
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self._checkpointer_ctx = None
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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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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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# Sampling temperature is cross-provider: forward it whenever set.
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# float() here so a value coming from a TRADINGAGENTS_TEMPERATURE env
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# string ("0.2") works the same as a programmatic float.
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temperature = self.config.get("temperature")
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if temperature is not None and temperature != "":
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kwargs["temperature"] = float(temperature)
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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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# Deterministic verification snapshot (bound to the analyst
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# LLM and required by its prompt; must be executable here or
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# the call fails and the model reports it "unavailable").
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get_verified_market_snapshot,
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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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get_macro_indicators,
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get_prediction_markets,
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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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}
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def _resolve_benchmark(self, ticker: str) -> str:
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"""Pick the benchmark ticker for alpha calculation against ``ticker``.
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``config["benchmark_ticker"]`` overrides everything when set; otherwise
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the suffix map matches the ticker's exchange suffix (e.g. ``.T`` for
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Tokyo). US-listed tickers without a dotted suffix fall through to the
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empty-suffix entry (SPY by default). Unrecognised suffixes (including
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US tickers with dots like ``BRK.B``) also fall back to the empty-suffix
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entry, which is the right default because the alpha calculation works
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in USD.
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"""
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explicit = self.config.get("benchmark_ticker")
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if explicit:
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return explicit
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benchmark_map = self.config.get("benchmark_map", {})
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ticker_upper = ticker.upper()
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for suffix, benchmark in benchmark_map.items():
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if suffix and ticker_upper.endswith(suffix.upper()):
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return benchmark
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return benchmark_map.get("", "SPY")
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def _fetch_returns(
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self, ticker: str, trade_date: str, holding_days: int = 5,
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benchmark: str = "SPY",
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) -> Tuple[Optional[float], Optional[float], Optional[int]]:
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"""Fetch raw and alpha return for ticker over holding_days from trade_date.
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``benchmark`` is the index used as the alpha baseline (resolved by the
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caller via ``_resolve_benchmark``). Returns ``(raw_return, alpha_return,
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actual_holding_days)`` or ``(None, None, None)`` if price data is
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unavailable (too recent, delisted, or network error).
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"""
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from tradingagents.dataflows.symbol_utils import normalize_symbol
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try:
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start = datetime.strptime(trade_date, "%Y-%m-%d")
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end = start + timedelta(days=holding_days + 7) # buffer for weekends/holidays
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end_str = end.strftime("%Y-%m-%d")
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# Normalize so the realized-return lookup hits the same instrument
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# the analysis priced (e.g. XAUUSD -> GC=F) (#984). The benchmark is
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# already a canonical Yahoo symbol from ``_resolve_benchmark``.
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stock = yf.Ticker(normalize_symbol(ticker)).history(start=trade_date, end=end_str)
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bench = yf.Ticker(benchmark).history(start=trade_date, end=end_str)
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if len(stock) < 2 or len(bench) < 2:
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return None, None, None
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actual_days = min(holding_days, len(stock) - 1, len(bench) - 1)
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raw = float(
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(stock["Close"].iloc[actual_days] - stock["Close"].iloc[0])
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/ stock["Close"].iloc[0]
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)
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bench_ret = float(
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(bench["Close"].iloc[actual_days] - bench["Close"].iloc[0])
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/ bench["Close"].iloc[0]
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)
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alpha = raw - bench_ret
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return raw, alpha, actual_days
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except Exception as e:
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logger.warning(
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"Could not resolve outcome for %s on %s vs %s (will retry next run): %s",
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ticker, trade_date, benchmark, e,
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)
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return None, None, None
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def _resolve_pending_entries(self, ticker: str) -> None:
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"""Resolve pending log entries for ticker at the start of a new run.
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Fetches returns for each same-ticker pending entry, generates reflections,
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then writes all updates in a single atomic batch write to avoid redundant I/O.
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Skips entries whose price data is not yet available (too recent or delisted).
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Trade-off: only same-ticker entries are resolved per run. Entries for
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other tickers accumulate until that ticker is run again.
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"""
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pending = [e for e in self.memory_log.get_pending_entries() if e["ticker"] == ticker]
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if not pending:
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return
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benchmark = self._resolve_benchmark(ticker)
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updates = []
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for entry in pending:
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raw, alpha, days = self._fetch_returns(
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ticker, entry["date"], benchmark=benchmark,
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)
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if raw is None:
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continue # price not available yet — try again next run
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reflection = self.reflector.reflect_on_final_decision(
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final_decision=entry.get("decision", ""),
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raw_return=raw,
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alpha_return=alpha,
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benchmark_name=benchmark,
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)
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updates.append({
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"ticker": ticker,
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"trade_date": entry["date"],
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"raw_return": raw,
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"alpha_return": alpha,
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"holding_days": days,
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"reflection": reflection,
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})
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if updates:
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self.memory_log.batch_update_with_outcomes(updates)
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def resolve_instrument_context(self, ticker: str, asset_type: str = "stock") -> str:
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"""Resolve ticker identity once and return the full instrument context.
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Deterministic yfinance lookup (cached, fail-open) injected into a
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context string so every agent anchors to the real company instead of
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hallucinating one from the price chart (#814). Both the propagate()
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path and the CLI call this so the resolved identity reaches the whole
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graph regardless of entry point.
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"""
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identity = resolve_instrument_identity(ticker)
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return build_instrument_context(ticker, asset_type, identity)
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def propagate(self, company_name, trade_date, asset_type: str = "stock"):
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"""Run the trading agents graph for a company on a specific date.
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``asset_type`` selects between the stock pipeline (default) and the
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crypto pipeline (``"crypto"``) shipped in #567 — the CLI auto-detects
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from the ticker; programmatic callers pass it explicitly. When
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``checkpoint_enabled`` is set in config, the graph is recompiled with
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a per-ticker SqliteSaver so a crashed run can resume from the last
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successful node on a subsequent invocation with the same ticker+date.
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"""
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self.ticker = company_name
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# Resolve any pending memory-log entries for this ticker before the pipeline runs.
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self._resolve_pending_entries(company_name)
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# Recompile with a checkpointer if the user opted in.
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if self.config.get("checkpoint_enabled"):
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self._checkpointer_ctx = get_checkpointer(
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self.config["data_cache_dir"], company_name
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)
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saver = self._checkpointer_ctx.__enter__()
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self.graph = self.workflow.compile(checkpointer=saver)
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step = checkpoint_step(
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self.config["data_cache_dir"], company_name, str(trade_date)
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)
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if step is not None:
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logger.info(
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"Resuming from step %d for %s on %s", step, company_name, trade_date
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)
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else:
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logger.info("Starting fresh for %s on %s", company_name, trade_date)
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try:
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return self._run_graph(company_name, trade_date, asset_type=asset_type)
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finally:
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if self._checkpointer_ctx is not None:
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self._checkpointer_ctx.__exit__(None, None, None)
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self._checkpointer_ctx = None
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self.graph = self.workflow.compile()
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def _run_graph(self, company_name, trade_date, asset_type: str = "stock"):
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"""Execute the graph and write the resulting state to disk and memory log."""
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# Initialize state — inject memory log context for PM and the
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# deterministically resolved instrument identity for all agents.
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past_context = self.memory_log.get_past_context(company_name)
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instrument_context = self.resolve_instrument_context(company_name, asset_type)
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init_agent_state = self.propagator.create_initial_state(
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company_name,
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trade_date,
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asset_type=asset_type,
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past_context=past_context,
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instrument_context=instrument_context,
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)
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args = self.propagator.get_graph_args()
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# Inject thread_id so same ticker+date resumes, different date starts fresh.
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if self.config.get("checkpoint_enabled"):
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tid = thread_id(company_name, str(trade_date))
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args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = tid
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if self.debug:
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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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# Streamed chunks are per-node deltas. Merge them so the returned
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# state matches what graph.invoke() yields in the non-debug path.
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final_state = {}
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for chunk in trace:
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final_state.update(chunk)
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else:
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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 to disk.
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self._log_state(trade_date, final_state)
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# Store decision for deferred reflection on the next same-ticker run.
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self.memory_log.store_decision(
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ticker=company_name,
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trade_date=trade_date,
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final_trade_decision=final_state["final_trade_decision"],
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)
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# Clear checkpoint on successful completion to avoid stale state.
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if self.config.get("checkpoint_enabled"):
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clear_checkpoint(
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self.config["data_cache_dir"], company_name, str(trade_date)
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)
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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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"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. Reject ticker values that would escape the
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# results directory when joined as a path component.
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safe_ticker = safe_ticker_component(self.ticker)
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directory = Path(self.config["results_dir"]) / safe_ticker / "TradingAgentsStrategy_logs"
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directory.mkdir(parents=True, exist_ok=True)
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log_path = directory / f"full_states_log_{trade_date}.json"
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with open(log_path, "w", encoding="utf-8") as f:
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json.dump(self.log_states_dict[str(trade_date)], f, indent=4)
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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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