"""XVa3 — Price-to-high value (mean reversion from recent highs).

IDEA: Score = -(close / rolling_max(close, W))
      Long the most beaten-down assets vs their rolling high (W=90).
      Negative sign: lower ratio (more beaten down) -> higher score -> long.

CAUSAL: rolling_max at row i uses only data[i-W+1 .. i] (pandas rolling handles this).
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
import xslib as xs
import numpy as np


def score_pth(close, W):
    """Price-to-high score: -(close / rolling_max(close, W)), causal."""
    import pandas as pd
    df = pd.DataFrame(close)
    roll_max = df.rolling(W, min_periods=W // 2).max().values
    ratio = close / np.where(roll_max > 0, roll_max, np.nan)
    return -ratio  # lower ratio (more beaten down) -> higher score -> long


# --- Grid: 5 backtests total ---
# Config 1: canonical W=90, H=10, k=5, long-short, all universe
r1 = xs.study_xs(
    "XVa3-W90-H10-k5-LS-all",
    lambda P: score_pth(P.close, 90),
    universe="all", H=10, k=5, long_short=True,
)
print(xs.fmt(r1))
print("JSON:", xs.as_json(r1))
print()

# Config 2: W=60 (shorter lookback), H=10, k=5, long-short, all universe
r2 = xs.study_xs(
    "XVa3-W60-H10-k5-LS-all",
    lambda P: score_pth(P.close, 60),
    universe="all", H=10, k=5, long_short=True,
)
print(xs.fmt(r2))
print("JSON:", xs.as_json(r2))
print()

# Config 3: W=90, H=5 (faster rebalance), k=5, long-short, all
r3 = xs.study_xs(
    "XVa3-W90-H5-k5-LS-all",
    lambda P: score_pth(P.close, 90),
    universe="all", H=5, k=5, long_short=True,
)
print(xs.fmt(r3))
print("JSON:", xs.as_json(r3))
print()

# Config 4: W=90, H=10, k=5, majors only (more liquid)
r4 = xs.study_xs(
    "XVa3-W90-H10-k5-LS-majors",
    lambda P: score_pth(P.close, 90),
    universe="majors", H=10, k=5, long_short=True,
)
print(xs.fmt(r4))
print("JSON:", xs.as_json(r4))
print()

# Config 5: W=120 (longer lookback), H=10, k=5, long-short, all
r5 = xs.study_xs(
    "XVa3-W120-H10-k5-LS-all",
    lambda P: score_pth(P.close, 120),
    universe="all", H=10, k=5, long_short=True,
)
print(xs.fmt(r5))
print("JSON:", xs.as_json(r5))
print()

# --- Pick best config ---
# Prefer: earns_slot first, then holdout sharpe, then distinctness
results = [r1, r2, r3, r4, r5]
earns = [r for r in results if r["earns_slot"]]
if earns:
    best = max(earns, key=lambda r: r["holdout"].get("sharpe", -999))
else:
    # Fall back to positive full+hold, distinct from XS01
    candidates = [r for r in results
                  if r["full"]["sharpe"] > 0
                  and r["holdout"].get("sharpe", 0) > 0
                  and (r["corr_xs01"] or 1.0) < 0.6]
    if candidates:
        best = max(candidates, key=lambda r: r["holdout"].get("sharpe", -999))
    else:
        best = max(results, key=lambda r: r["holdout"].get("sharpe", -999))

print("=== BEST CONFIG ===")
print(xs.fmt(best))
print("JSON:", xs.as_json(best))