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>

scripts/research/xsec/runs/XD02.py

@@ -0,0 +1,80 @@
+"""XD02 — High-skew momentum (POSITIVE sign).
+Mechanism: Score = +roll_skew(ret, 60).
+Idea: positive skew = right-tailed distribution = asset had big up-moves.
+Does positive skew predict cross-sectional outperformance in crypto alts?
+(XD01 tested negative skew; this tests the opposite hypothesis.)
+
+Grid (<= 5 runs):
+  1. majors, H=10, k=5, LS  (baseline)
+  2. all,    H=10, k=5, LS  (wider universe)
+  3. majors, H=5,  k=5, LS  (faster rebalance)
+  4. majors, H=10, k=5, LS, win=30  (shorter lookback)
+  5. majors, H=10, k=3, LS  (concentrated book)
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+# Score: positive rolling skewness of daily returns
+# Higher skew -> more right-tailed -> long this asset
+def score_skew(P, win=60):
+    return xs.roll_skew(P.ret, win)
+
+print("=" * 60)
+print("XD02 — HIGH-SKEW MOMENTUM (positive sign, does positive skew pay?)")
+print("=" * 60)
+
+# Run 1: majors, H=10, k=5, LS, win=60
+r1 = xs.study_xs("XD02-MJ-H10-k5-w60", lambda P: score_skew(P, 60),
+                  universe="majors", H=10, k=5, long_short=True)
+print(xs.fmt(r1))
+print("JSON:", xs.as_json(r1))
+print()
+
+# Run 2: all universe, H=10, k=5, LS, win=60
+r2 = xs.study_xs("XD02-ALL-H10-k5-w60", lambda P: score_skew(P, 60),
+                  universe="all", H=10, k=5, long_short=True)
+print(xs.fmt(r2))
+print("JSON:", xs.as_json(r2))
+print()
+
+# Run 3: majors, H=5, k=5, LS, win=60 (faster rebalance)
+r3 = xs.study_xs("XD02-MJ-H5-k5-w60", lambda P: score_skew(P, 60),
+                  universe="majors", H=5, k=5, long_short=True)
+print(xs.fmt(r3))
+print("JSON:", xs.as_json(r3))
+print()
+
+# Run 4: majors, H=10, k=5, LS, win=30 (shorter lookback)
+r4 = xs.study_xs("XD02-MJ-H10-k5-w30", lambda P: score_skew(P, 30),
+                  universe="majors", H=10, k=5, long_short=True)
+print(xs.fmt(r4))
+print("JSON:", xs.as_json(r4))
+print()
+
+# Run 5: majors, H=10, k=3, LS, win=60 (concentrated)
+r5 = xs.study_xs("XD02-MJ-H10-k3-w60", lambda P: score_skew(P, 60),
+                  universe="majors", H=10, k=3, long_short=True)
+print(xs.fmt(r5))
+print("JSON:", xs.as_json(r5))
+print()
+
+# Select best by: earns_slot > holdout sharpe > corr_xs01 (lower is better)
+results = [r1, r2, r3, r4, r5]
+earners = [r for r in results if r["earns_slot"]]
+if earners:
+    best = max(earners, key=lambda r: r["holdout"].get("sharpe", 0))
+else:
+    # fallback: highest holdout + positive full, then lowest xs01 corr
+    pos = [r for r in results if r["full"]["sharpe"] > 0 and r["holdout"].get("sharpe", 0) > 0]
+    if pos:
+        best = max(pos, key=lambda r: r["holdout"].get("sharpe", 0) - abs(r.get("corr_xs01") or 0))
+    else:
+        best = max(results, key=lambda r: r["holdout"].get("sharpe", -99))
+
+print("=" * 60)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))