"""XL03 [LIQ] — Low-turnover anomaly.

Score = -roll_mean(close * volume, 30) : long low dollar-volume names.
Idea: low-liquidity assets carry a liquidity premium and may outperform
high-liquidity names on a risk-adjusted basis.

Grid (<=5 runs):
  1. baseline: universe=all, H=10, k=5, long_short=True, win=30
  2. shorter window win=10 (faster signal)
  3. longer window win=60 (more stable ranking)
  4. long-only version (long low-liq only, no shorting high-liq names)
  5. majors universe (check if effect holds in liquid-only subspace)
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
import xslib as xs
import numpy as np

# --- score factory -----------------------------------------------------------

def liq_score(P, win=30):
    """Score = -roll_mean(close * dollar_vol, win).
    CAUSAL: roll_mean at row i uses data[i-win+1..i].
    Higher score = LOWER liquidity = LONG.
    """
    dollar_vol = P.close * P.vol          # (n, A) daily dollar volume
    avg_dvol = xs.roll_mean(dollar_vol, win)  # rolling mean, causal
    return -avg_dvol                      # negate: lower dvol -> higher score -> long


# --- grid -------------------------------------------------------------------

print("=" * 70)
print("XL03 [LIQ] Low-turnover anomaly — grid search")
print("=" * 70)

results = []

# Run 1: baseline (all, H=10, k=5, LS, win=30)
r1 = xs.study_xs("XL03-w30-all-LS",
                 lambda P: liq_score(P, 30),
                 universe="all", H=10, k=5, long_short=True)
print(xs.fmt(r1))
print("JSON:", xs.as_json(r1))
results.append(r1)

# Run 2: shorter window win=10
r2 = xs.study_xs("XL03-w10-all-LS",
                 lambda P: liq_score(P, 10),
                 universe="all", H=10, k=5, long_short=True)
print(xs.fmt(r2))
print("JSON:", xs.as_json(r2))
results.append(r2)

# Run 3: longer window win=60
r3 = xs.study_xs("XL03-w60-all-LS",
                 lambda P: liq_score(P, 60),
                 universe="all", H=10, k=5, long_short=True)
print(xs.fmt(r3))
print("JSON:", xs.as_json(r3))
results.append(r3)

# Run 4: long-only (long low-liq, no short)
r4 = xs.study_xs("XL03-w30-all-LO",
                 lambda P: liq_score(P, 30),
                 universe="all", H=10, k=5, long_short=False)
print(xs.fmt(r4))
print("JSON:", xs.as_json(r4))
results.append(r4)

# Run 5: majors universe only
r5 = xs.study_xs("XL03-w30-majors-LS",
                 lambda P: liq_score(P, 30),
                 universe="majors", H=10, k=5, long_short=True)
print(xs.fmt(r5))
print("JSON:", xs.as_json(r5))
results.append(r5)

# --- pick best config -------------------------------------------------------
print("\n" + "=" * 70)
print("SUMMARY — best by: earns_slot first, then hold-out Sharpe, then corr_xs01 < 0.6")
print("=" * 70)

def rank_key(r):
    earns = int(r["earns_slot"])
    hold_sh = r["holdout"].get("sharpe", -9) or -9
    xs01_corr = abs(r.get("corr_xs01") or 1.0)
    return (earns, hold_sh, -xs01_corr)

best = max(results, key=rank_key)
print(f"\nBEST CONFIG: {best['name']}")
print(xs.fmt(best))
print("\nJSON (best):", xs.as_json(best))