"""XM06 — 52-day-high proximity (closeness-to-recent-high momentum).

IDEA: Score = close / rolling_max(high, W)  [closeness to recent high].
Assets near their recent high are "in momentum"; rank them cross-sectionally.
W in {60, 90}. Causal: rolling_max up through bar i only.

Grid: 5 calls max
  1. W=60, majors, H=10, k=5, L/S
  2. W=90, majors, H=10, k=5, L/S
  3. W=60, all,    H=10, k=5, L/S   (best-W on wider universe)
  4. W=60, all,    H=5,  k=5, L/S   (faster rebalance)
  5. W=60, all,    H=10, k=7, L/S   (wider book)
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
import xslib as xs
import numpy as np


def score_proximity(P, W):
    """Causal: close[i] / max(high[i-W+1 .. i]). Higher = closer to recent high = long."""
    n, A = P.close.shape
    out = np.full((n, A), np.nan)
    # rolling max of high, causal window [i-W+1 .. i]
    high_df = __import__("pandas").DataFrame(P.high)
    roll_max = high_df.rolling(W, min_periods=max(2, W // 2)).max().values
    # proximity ratio: close / recent_high (always in (0,1] if no gap-up above window)
    out = P.close / roll_max
    return out


# ---- run grid ----

results = []

# 1. W=60, majors, H=10, k=5, L/S
rep1 = xs.study_xs("XM06_W60_majors", lambda P: score_proximity(P, 60),
                   universe="majors", H=10, k=5, long_short=True)
print(xs.fmt(rep1))
results.append(rep1)

# 2. W=90, majors, H=10, k=5, L/S
rep2 = xs.study_xs("XM06_W90_majors", lambda P: score_proximity(P, 90),
                   universe="majors", H=10, k=5, long_short=True)
print(xs.fmt(rep2))
results.append(rep2)

# 3. W=60, all, H=10, k=5, L/S
rep3 = xs.study_xs("XM06_W60_all", lambda P: score_proximity(P, 60),
                   universe="all", H=10, k=5, long_short=True)
print(xs.fmt(rep3))
results.append(rep3)

# 4. W=60, all, H=5, k=5, L/S  (faster rebalance)
rep4 = xs.study_xs("XM06_W60_all_H5", lambda P: score_proximity(P, 60),
                   universe="all", H=5, k=5, long_short=True)
print(xs.fmt(rep4))
results.append(rep4)

# 5. W=60, all, H=10, k=7, L/S  (wider book)
rep5 = xs.study_xs("XM06_W60_all_k7", lambda P: score_proximity(P, 60),
                   universe="all", H=10, k=7, long_short=True)
print(xs.fmt(rep5))
results.append(rep5)

# ---- pick best by: earns_slot > hold-out sharpe > distinctness ----
def score_result(r):
    earns = 1 if r["earns_slot"] else 0
    adds = 1 if r["marginal"].get("verdict") == "ADDS" else 0
    hold_sh = r["holdout"].get("sharpe", -999)
    full_sh = r["full"]["sharpe"]
    corr_xs01 = abs(r.get("corr_xs01") or 1.0)
    distinct = 1 if corr_xs01 < 0.6 else 0
    return (earns, adds, hold_sh, full_sh, distinct)

best = max(results, key=score_result)
print("\n=== BEST CONFIG ===")
print(xs.fmt(best))
print("JSON:", xs.as_json(best))
