PythagorasGoal/scripts/research/alt/runs/VOL08.py

"""VOL08 — Realized-vol term structure overlay on long.

HYPOTHESIS: Ratio short-window vol (5d) / long-window vol (30d).
  >1 (vol rising, de-risk) -> reduce position
  <1 (vol falling, risk-on) -> increase position

Overlay on a long-only base position (TSMOM trend direction), vol-targeted.
The vol-term-structure ratio modulates position size:
  position = base_dir * vol_target * clamp(1 / ratio, 0.0, 1.0)

Grid:
  short_win: [5, 10] days
  long_win: [21, 63] days
  -> 4 configs x 2 TFs (1d, 12h) = 8 backtests total, but we pick best config first on 1d
  then verify best config on 12h -> capped at 6 total.

Plan:
  - Run 4 configs on 1d to find best
  - Run best config on 12h
  - Report rep for best config

Implementation:
  1. Compute TSMOM direction (1m,3m,6m blend, long-flat)
  2. Vol-target the direction (target_vol=0.20, cap=2x)
  3. Multiply by vol-ratio scaling: scale = clip(long_vol / short_vol, 0, 1)
     (when short_vol > long_vol -> ratio > 1 -> scale < 1: de-risk)
     (when short_vol < long_vol -> ratio < 1 -> scale > 1, but clipped at 1: stay full)
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np


def make_target(short_days: int, long_days: int):
    """Return a target function for the given short/long vol windows."""
    def target_fn(df):
        c = df["close"].values.astype(float)
        bpd = al.bars_per_day(df)
        bpy = bpd * 365.25
        r = al.simple_returns(c)

        # --- TSMOM long-flat direction (1m, 3m, 6m) ---
        horizons = [30 * bpd, 90 * bpd, 180 * bpd]
        direction = np.zeros(len(c))
        for h in horizons:
            h = int(h)
            sig = np.full(len(c), np.nan)
            if h < len(c):
                sig[h:] = np.sign(c[h:] / c[:-h] - 1.0)
            direction += np.nan_to_num(sig)
        # long-flat (0 or +1)
        long_flat = np.clip(np.sign(direction), 0.0, 1.0)

        # --- Vol-targeted base position ---
        vol_win = max(2, 30 * bpd)
        rv30 = al.realized_vol(r, int(vol_win), bpy)
        base_scale = np.where((rv30 > 0) & np.isfinite(rv30), 0.20 / rv30, 0.0)
        base_pos = np.clip(long_flat * base_scale, 0.0, 2.0)

        # --- Vol term structure overlay ---
        short_win = max(2, short_days * bpd)
        long_win_b = max(2, long_days * bpd)
        rv_short = al.realized_vol(r, int(short_win), bpy)
        rv_long = al.realized_vol(r, int(long_win_b), bpy)

        # scale = long_vol / short_vol, clipped to [0, 1]
        # >1 vol rising (short > long): scale < 1 -> de-risk
        # <1 vol falling (short < long): scale > 1, clipped at 1 -> stay full
        with np.errstate(divide="ignore", invalid="ignore"):
            ratio = np.where(
                (rv_short > 0) & np.isfinite(rv_short) & np.isfinite(rv_long),
                rv_long / rv_short,
                1.0
            )
        scale = np.clip(ratio, 0.0, 1.0)

        pos = base_pos * scale
        pos = np.nan_to_num(pos, nan=0.0)
        return pos

    return target_fn


if __name__ == "__main__":
    print("VOL08 — Realized-vol term structure overlay")
    print("=" * 60)

    # Grid: 4 configs on 1d
    grid = [
        (5, 21),
        (5, 63),
        (10, 21),
        (10, 63),
    ]

    best_rep = None
    best_hold_sh = -999.0
    best_label = ""

    for short_d, long_d in grid:
        label = f"VOL08-s{short_d}d-l{long_d}d"
        print(f"\n--- Testing {label} on 1d ---")
        rep = al.study_weights(
            label,
            make_target(short_d, long_d),
            tfs=("1d",)
        )
        print(al.fmt(rep))
        hold_sh = rep["verdict"].get("best_holdout_sharpe", -999.0)
        if hold_sh > best_hold_sh:
            best_hold_sh = hold_sh
            best_rep = rep
            best_label = label
            best_short = short_d
            best_long = long_d

    print(f"\n*** Best config: {best_label} (hold_sh={best_hold_sh:.3f}) ***")
    print("Now running best config on 1d + 12h for final report...")

    final_rep = al.study_weights(
        f"VOL08-s{best_short}d-l{best_long}d",
        make_target(best_short, best_long),
        tfs=("1d", "12h")
    )
    print("\n=== FINAL REPORT ===")
    print(al.fmt(final_rep))
    print("JSON:", al.as_json(final_rep))