TradingAgents/tradingagents/strategies/multifactor.py

74 lines
2.3 KiB
Python

"""Multifactor strategy signal (§3.6 — Multifactor Models).
Combined momentum + value + quality + low-vol composite.
Reference:
Kakushadze & Serur, "151 Trading Strategies", §3.6
"""
from __future__ import annotations
from typing import Any
import numpy as np
from .base import BaseStrategy, StrategySignal
from ._data import get_ohlcv, get_info
class MultifactorStrategy(BaseStrategy):
name = "Multifactor (§3.6)"
roles = ["researcher", "risk"]
def compute(self, ticker: str, date: str, context: dict[str, Any] | None = None) -> StrategySignal | None:
df = get_ohlcv(ticker, date, context)
info = get_info(ticker, context)
if df is None or len(df) < 252 or not info:
return None
factors: list[float] = []
details: list[str] = []
close = df["Close"].values
# Momentum factor: 12-1 month return
if len(close) >= 252:
mom = (close[-21] - close[-252]) / close[-252]
factors.append(max(-1.0, min(1.0, mom)))
details.append(f"mom={mom:+.2%}")
# Value factor: inverse PE
pe = info.get("trailingPE")
if pe and pe > 0:
val = min(1.0 / pe / 0.15, 1.0) * 2 - 1
factors.append(max(-1.0, min(1.0, val)))
details.append(f"val_pe={pe:.1f}")
# Quality factor: ROE
roe = info.get("returnOnEquity")
if roe is not None:
factors.append(max(-1.0, min(1.0, roe * 2)))
details.append(f"roe={roe:.2%}")
# Low-vol factor
if len(close) >= 63:
vol = float(np.std(np.diff(np.log(close[-63:]))) * np.sqrt(252))
lv = max(-1.0, min(1.0, (0.30 - vol) / 0.30))
factors.append(lv)
details.append(f"vol={vol:.1%}")
if not factors:
return None
strength = round(sum(factors) / len(factors), 4)
strength = max(-1.0, min(1.0, strength))
direction = "bullish" if strength > 0.05 else ("bearish" if strength < -0.05 else "neutral")
return StrategySignal(
name=self.name,
ticker=ticker,
date=date,
signal_strength=strength,
direction=direction,
detail=f"{len(factors)}-factor composite: {', '.join(details)}",
)