research(xsec): sweep cross-sectional su Hyperliquid (43 script/257 config) + verifica avversariale

Nuova harness condivisa xslib.py (panel HL certificato, score per-asset causale, book long-k/short-k vol-targeted leak-free) + 43 script in runs/ su 11 famiglie (MOM/REV/VOL/ DIST/LIQ/VAL/STRUCT/UNIV). Scoring = earns_slot (full>0 AND hold-out>0 AND marginal ADDS al portafoglio live AND corr XS01<0.6, con jackknife drop-one-month). Find: 42/257 config earns_slot=True, ma TUTTE con corr TP01 -0.2..-0.4 e PnL ~solo 2025. Verify (verify_survivors.py, 3 scettici deterministici): - S1 redundancy: cluster low-vol = UNA scommessa (XV01=XU02=1.00, XV02/XV03 r 0.44-0.67); XM09/XL02/XS06b/XR02 distinti (corr media off-diag +0.20). - S2 short-beta: cluster low-vol carica 0.44-0.70 su short-market -> NON market-neutral, e' un tilt short-alt-beta di regime. XM09(0.08)/XR02(-0.21) NON short-beta. - S3 per-anno: cluster low-vol decade (XV01/XU02 2026 -0.09); XL02 morto (2025 -0.14, 2026 -0.43); XM09 (0.82/0.50/0.74) e XR02 (0.84/0.40/2.68) positivi in tutti e 3 gli anni. Esito: nessuna sleeve nuova. Cluster low-vol RIGETTATO (regime-bet), XL02 RIGETTATO (overfit). 2 LEAD genuini (XM09 trend-gated x-sec momentum, XR02 reversal vol-gated) -> forward-monitor, non deployabili (panel 2.5y regime unico + STAT-MODE esecuzione). Portafoglio live invariato. Incluso anche options_vrp_managed.py (A/B VRP01 hold-to-expiry vs gestione attiva del doc credit-spread): la gestione attiva DISTRUGGE l'edge (combo FULL managed Sh -1.29 vs HtE +0.96, il delta-exit taglia i vincenti) -> scartata, VRP01 resta hold-to-expiry. Diari: 2026-06-20-xsec-strategies-sweep.md, 2026-06-20-vrp-active-management.md. gitignore: data/paper_portfolio/ (stato runtime paper) + scripts/research/xsec/runs/out/ (output rigenerabile). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-20 21:36:57 +00:00
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+"""XL01 — Amihud Illiquidity Premium (cross-sectional).
+
+Score = rolling mean of |ret| / (close * volume) over W days (Amihud ratio).
+Higher score = more illiquid.
+
+We test both signs:
+  - Long illiquid (higher score = long): illiquidity premium hypothesis
+  - Short illiquid (higher score = short): liquidity premium, more liquid = better
+
+Grid (<=5 calls):
+  1. LS W=30, all universe
+  2. LS W=30, majors
+  3. LS W=30, short sign (liquidity premium, flip sign)
+  4. LS W=30, H=20 (slower rebal), all universe
+  5. LS W=60, all universe
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+def amihud_score(close, vol, ret, W=30):
+    """Amihud illiquidity ratio: mean(|ret| / (close * volume)) over W days.
+    Higher = more illiquid.
+    Values at bar i use data <= i (causal).
+    """
+    # dollar volume = close * volume (notional traded)
+    dollar_vol = close * vol  # (n, A)
+    # |return| / dollar_vol
+    abs_ret = np.abs(ret)  # (n, A)
+    # avoid division by zero
+    dv_safe = np.where(dollar_vol > 0, dollar_vol, np.nan)
+    amihud_raw = abs_ret / dv_safe  # (n, A)
+    # rolling mean (causal)
+    score = xs.roll_mean(amihud_raw, W)
+    return score
+
+
+def score_illiquid(W=30):
+    """Long illiquid (high Amihud = illiquid -> buy)."""
+    def fn(P):
+        return amihud_score(P.close, P.vol, P.ret, W=W)
+    return fn
+
+
+def score_liquid(W=30):
+    """Long liquid (flip sign: low Amihud = liquid -> buy)."""
+    def fn(P):
+        return -amihud_score(P.close, P.vol, P.ret, W=W)
+    return fn
+
+
+if __name__ == "__main__":
+    print("XL01 — Amihud Illiquidity Premium")
+    print("="*60)
+
+    # 1. Baseline: long illiquid, W=30, all universe
+    print("\n[1] Long ILLIQUID, W=30, universe=all, H=10, k=5, LS")
+    r1 = xs.study_xs("XL01-ILL-30-all", score_illiquid(30),
+                     universe="all", H=10, k=5, long_short=True)
+    print(xs.fmt(r1))
+    print("JSON:", xs.as_json(r1))
+
+    # 2. Long illiquid, W=30, majors only
+    print("\n[2] Long ILLIQUID, W=30, universe=majors, H=10, k=5, LS")
+    r2 = xs.study_xs("XL01-ILL-30-maj", score_illiquid(30),
+                     universe="majors", H=10, k=5, long_short=True)
+    print(xs.fmt(r2))
+    print("JSON:", xs.as_json(r2))
+
+    # 3. Long LIQUID (flip sign), W=30, all universe
+    print("\n[3] Long LIQUID (flip sign), W=30, universe=all, H=10, k=5, LS")
+    r3 = xs.study_xs("XL01-LIQ-30-all", score_liquid(30),
+                     universe="all", H=10, k=5, long_short=True)
+    print(xs.fmt(r3))
+    print("JSON:", xs.as_json(r3))
+
+    # 4. Long illiquid, W=30, H=20 (slower rebal), all
+    print("\n[4] Long ILLIQUID, W=30, universe=all, H=20, k=5, LS")
+    r4 = xs.study_xs("XL01-ILL-30-H20", score_illiquid(30),
+                     universe="all", H=20, k=5, long_short=True)
+    print(xs.fmt(r4))
+    print("JSON:", xs.as_json(r4))
+
+    # 5. Long illiquid, W=60, all universe
+    print("\n[5] Long ILLIQUID, W=60, universe=all, H=10, k=5, LS")
+    r5 = xs.study_xs("XL01-ILL-60-all", score_illiquid(60),
+                     universe="all", H=10, k=5, long_short=True)
+    print(xs.fmt(r5))
+    print("JSON:", xs.as_json(r5))
+
+    # Summary
+    results = [r1, r2, r3, r4, r5]
+    print("\n" + "="*60)
+    print("SUMMARY — pick best by: earns_slot > holdout > distinctness")
+    for r in results:
+        es = r["earns_slot"]
+        fsh = r["full"]["sharpe"]
+        hsh = r["holdout"].get("sharpe", 0)
+        cxs = r["corr_xs01"]
+        v = r["marginal"]["verdict"]
+        print(f"  {r['name']:30s}  FULL={fsh:+.2f}  HOLD={hsh:+.2f}  corr_xs01={cxs}  "
+              f"verdict={v}  earns_slot={es}")
+
+    # Pick best: prefer earns_slot, then hold sharpe
+    best = max(results, key=lambda r: (
+        r["earns_slot"],
+        r["holdout"].get("sharpe", -99),
+        r["full"]["sharpe"],
+    ))
+    print(f"\nBEST CONFIG: {best['name']}")
+    print(xs.fmt(best))
+    print("BEST JSON:", xs.as_json(best))