strategy_logic.py

def make_positions(df_feat: pd.DataFrame, regime: pd.Series, params: dict) -> pd.Series:
    """
    Same logic as before, with parameters chosen by WFO.
    """
    z_thr = params["z_thr"]
    range_size = params["range_size"]
    lowvol_size = params["lowvol_size"]
    highvol_size = params["highvol_size"]

    pos = pd.Series(0.0, index=df_feat.index)
    trend = df_feat["trend_20"].fillna(0.0)
    z = df_feat["z_20"].fillna(0.0)

    for t in pos.index:
        r = str(regime.loc[t]) if t in regime.index else "UNCERTAIN"

        if r == "TREND_UP":
            pos.loc[t] = +MAX_LEVERAGE
        elif r == "TREND_DOWN":
            pos.loc[t] = -MAX_LEVERAGE
        elif r == "LOW_VOL":
            pos.loc[t] = float(np.sign(trend.loc[t])) * (lowvol_size * MAX_LEVERAGE)
        elif r == "HIGH_VOL":
            pos.loc[t] = highvol_size * MAX_LEVERAGE
        elif r == "RANGE":
            if z.loc[t] > z_thr:
                pos.loc[t] = -(range_size * MAX_LEVERAGE)
            elif z.loc[t] < -z_thr:
                pos.loc[t] = +(range_size * MAX_LEVERAGE)
            else:
                pos.loc[t] = 0.0
        else:
            pos.loc[t] = 0.0

    # Trade next bar (no lookahead)
    return pos.shift(1).fillna(0.0)


def backtest(df_feat: pd.DataFrame, pos: pd.Series) -> pd.DataFrame:
    bt = pd.DataFrame(index=df_feat.index)
    bt["ret_log"] = df_feat["ret"].fillna(0.0)
    bt["pos"] = pos.reindex(bt.index).fillna(0.0)

    bt["gross"] = bt["pos"] * bt["ret_log"]
    bt["cost"] = (COST_BPS / 10000.0) * bt["pos"].diff().abs().fillna(0.0)
    bt["net"] = bt["gross"] - bt["cost"]

    bt["log_equity"] = bt["net"].cumsum()
    bt["equity"] = np.exp(bt["log_equity"])
    return bt