348 lines
13 KiB
Python
348 lines
13 KiB
Python
# TradingAgents/graph/trading_graph.py
|
|
|
|
import logging
|
|
import os
|
|
from pathlib import Path
|
|
import json
|
|
from datetime import datetime, timedelta
|
|
from typing import Dict, Any, Tuple, List, Optional
|
|
|
|
import yfinance as yf
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
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 TradingMemoryLog
|
|
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."""
|
|
|
|
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 = config or DEFAULT_CONFIG
|
|
self.callbacks = callbacks or []
|
|
|
|
# Update the interface's config
|
|
set_config(self.config)
|
|
|
|
# Create necessary directories
|
|
os.makedirs(self.config["data_cache_dir"], exist_ok=True)
|
|
os.makedirs(self.config["results_dir"], exist_ok=True)
|
|
|
|
# Initialize LLMs with provider-specific thinking configuration
|
|
llm_kwargs = self._get_provider_kwargs()
|
|
|
|
# Add callbacks to kwargs if provided (passed to LLM constructor)
|
|
if self.callbacks:
|
|
llm_kwargs["callbacks"] = self.callbacks
|
|
|
|
deep_client = create_llm_client(
|
|
provider=self.config["llm_provider"],
|
|
model=self.config["deep_think_llm"],
|
|
base_url=self.config.get("backend_url"),
|
|
**llm_kwargs,
|
|
)
|
|
quick_client = create_llm_client(
|
|
provider=self.config["llm_provider"],
|
|
model=self.config["quick_think_llm"],
|
|
base_url=self.config.get("backend_url"),
|
|
**llm_kwargs,
|
|
)
|
|
|
|
self.deep_thinking_llm = deep_client.get_llm()
|
|
self.quick_thinking_llm = quick_client.get_llm()
|
|
|
|
self.memory_log = TradingMemoryLog(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.conditional_logic,
|
|
)
|
|
|
|
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 _get_provider_kwargs(self) -> Dict[str, Any]:
|
|
"""Get provider-specific kwargs for LLM client creation."""
|
|
kwargs = {}
|
|
provider = 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 _fetch_returns(
|
|
self, ticker: str, trade_date: str, holding_days: int = 5
|
|
) -> Tuple[Optional[float], Optional[float], Optional[int]]:
|
|
"""Fetch raw and alpha return for ticker over holding_days from trade_date.
|
|
|
|
Returns (raw_return, alpha_return, actual_holding_days) or
|
|
(None, None, None) if price data is unavailable (too recent, delisted,
|
|
or network error).
|
|
"""
|
|
try:
|
|
start = datetime.strptime(trade_date, "%Y-%m-%d")
|
|
end = start + timedelta(days=holding_days + 7) # buffer for weekends/holidays
|
|
end_str = end.strftime("%Y-%m-%d")
|
|
|
|
stock = yf.Ticker(ticker).history(start=trade_date, end=end_str)
|
|
spy = yf.Ticker("SPY").history(start=trade_date, end=end_str)
|
|
|
|
if len(stock) < 2 or len(spy) < 2:
|
|
return None, None, None
|
|
|
|
actual_days = min(holding_days, len(stock) - 1, len(spy) - 1)
|
|
raw = float(
|
|
(stock["Close"].iloc[actual_days] - stock["Close"].iloc[0])
|
|
/ stock["Close"].iloc[0]
|
|
)
|
|
spy_ret = float(
|
|
(spy["Close"].iloc[actual_days] - spy["Close"].iloc[0])
|
|
/ spy["Close"].iloc[0]
|
|
)
|
|
alpha = raw - spy_ret
|
|
return raw, alpha, actual_days
|
|
except Exception as e:
|
|
logger.debug("_fetch_returns failed for %s@%s: %s", ticker, trade_date, e)
|
|
return None, None, None
|
|
|
|
def _resolve_pending_entries(self, ticker: str) -> None:
|
|
"""Resolve pending log entries for ticker at the start of a new run.
|
|
|
|
Fetches returns for each same-ticker pending entry, generates reflections,
|
|
then writes all updates in a single atomic batch write to avoid redundant I/O.
|
|
Skips entries whose price data is not yet available (too recent or delisted).
|
|
|
|
Trade-off: only same-ticker entries are resolved per run. Entries for
|
|
other tickers accumulate until that ticker is run again.
|
|
"""
|
|
pending = [e for e in self.memory_log.get_pending_entries() if e["ticker"] == ticker]
|
|
if not pending:
|
|
return
|
|
|
|
updates = []
|
|
for entry in pending:
|
|
raw, alpha, days = self._fetch_returns(ticker, entry["date"])
|
|
if raw is None:
|
|
continue # price not available yet — try again next run
|
|
reflection = self.reflector.reflect_on_final_decision(
|
|
final_decision=entry.get("decision", ""),
|
|
raw_return=raw,
|
|
alpha_return=alpha,
|
|
)
|
|
updates.append({
|
|
"ticker": ticker,
|
|
"trade_date": entry["date"],
|
|
"raw_return": raw,
|
|
"alpha_return": alpha,
|
|
"holding_days": days,
|
|
"reflection": reflection,
|
|
})
|
|
|
|
if updates:
|
|
self.memory_log.batch_update_with_outcomes(updates)
|
|
|
|
def propagate(self, company_name, trade_date):
|
|
"""Run the trading agents graph for a company on a specific date."""
|
|
|
|
self.ticker = company_name
|
|
|
|
# Resolve any pending log entries for this ticker before the pipeline runs.
|
|
# This adds the outcome + reflection from the previous run at zero latency cost.
|
|
self._resolve_pending_entries(company_name)
|
|
|
|
# Initialize state — inject memory log context for PM
|
|
past_context = self.memory_log.get_past_context(company_name)
|
|
init_agent_state = self.propagator.create_initial_state(
|
|
company_name, trade_date, past_context=past_context
|
|
)
|
|
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)
|
|
|
|
# Store decision for deferred reflection.
|
|
self.memory_log.store_decision(
|
|
ticker=company_name,
|
|
trade_date=trade_date,
|
|
final_trade_decision=final_state["final_trade_decision"],
|
|
)
|
|
|
|
# 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(self.config["results_dir"]) / self.ticker / "TradingAgentsStrategy_logs"
|
|
directory.mkdir(parents=True, exist_ok=True)
|
|
|
|
log_path = directory / f"full_states_log_{trade_date}.json"
|
|
with open(log_path, "w", encoding="utf-8") as f:
|
|
json.dump(self.log_states_dict[str(trade_date)], f, indent=4)
|
|
|
|
def process_signal(self, full_signal):
|
|
"""Process a signal to extract the core decision."""
|
|
return self.signal_processor.process_signal(full_signal)
|