TradingAgents/tradingagents/strategies/sector_rotation.py

73 lines
2.2 KiB
Python

"""Sector Rotation strategy signal (§4.1 — Sector Rotation).
Compares ticker's sector performance to broad market using relative strength.
Reference:
Kakushadze & Serur, "151 Trading Strategies", §4.1
"""
from __future__ import annotations
import logging
from typing import Any
import numpy as np
from .base import BaseStrategy, StrategySignal
from ._data import get_ohlcv, get_info
logger = logging.getLogger(__name__)
# Sector ETF proxies
_SECTOR_ETFS: dict[str, str] = {
"Technology": "XLK",
"Healthcare": "XLV",
"Financial Services": "XLF",
"Financials": "XLF",
"Consumer Cyclical": "XLY",
"Consumer Defensive": "XLP",
"Energy": "XLE",
"Industrials": "XLI",
"Basic Materials": "XLB",
"Utilities": "XLU",
"Real Estate": "XLRE",
"Communication Services": "XLC",
}
class SectorRotationStrategy(BaseStrategy):
name = "Sector Rotation (§4.1)"
roles = ["market", "researcher"]
def compute(self, ticker: str, date: str, context: dict[str, Any] | None = None) -> StrategySignal | None:
info = get_info(ticker, context)
if not info:
return None
sector = info.get("sector", "")
etf = _SECTOR_ETFS.get(sector)
if not etf:
return None
sector_df = get_ohlcv(etf, date)
spy_df = get_ohlcv("SPY", date)
if sector_df is None or spy_df is None or len(sector_df) < 63 or len(spy_df) < 63:
return None
# 3-month relative strength: sector ETF vs SPY
sec_ret = (sector_df["Close"].values[-1] - sector_df["Close"].values[-63]) / sector_df["Close"].values[-63]
spy_ret = (spy_df["Close"].values[-1] - spy_df["Close"].values[-63]) / spy_df["Close"].values[-63]
rel = sec_ret - spy_ret
strength = max(-1.0, min(1.0, rel * 5))
direction = "bullish" if strength > 0.1 else ("bearish" if strength < -0.1 else "neutral")
return StrategySignal(
name=self.name,
ticker=ticker,
date=date,
signal_strength=round(strength, 4),
direction=direction,
detail=f"{sector} ({etf}) 63d relative strength vs SPY: {rel:+.2%}",
)