# strategies/tsm.py
# Source: Moskowitz, Ooi, Pedersen (2012) — JFE 104(2)
# Replication fidelity: 4/5 user criteria, 3/5 test regimes
# Decision log: see decisions/tsm.yaml

from edge.signals import lookback_return, realized_vol
from edge.data import futures_universe
from edge.strategy import Strategy, register
import numpy as np


@register("tsm")
class TimeSeriesMomentum(Strategy):
    """Time series momentum across cross-asset futures.

    Long if 12m return positive, short otherwise. Position sized
    to target 40% annualized vol per instrument.
    """

    lookback: str = "12M"
    rebalance: str = "monthly"
    vol_target_pct: float = 40.0
    vol_window: int = 60  # resolved from decision log

    def universe(self):
        return futures_universe(asset_classes=["eq", "fi", "fx", "cm"])

    def signal(self, prices):
        r = lookback_return(prices, self.lookback)
        return np.sign(r)

    def position(self, prices, signal):
        sigma = realized_vol(prices, window=self.vol_window)
        return signal * (self.vol_target_pct / sigma)