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/XS01b.py

@@ -0,0 +1,58 @@
+"""XS01b — Double-sort Momentum × Low-Vol
+Score = xs_zscore(past_return(close, 60)) + xs_zscore(-roll_std(ret, 30))
+Combines cross-sectional momentum with low-vol preference (lower realized vol = higher score).
+Grid: universe x H x k variations, <=5 total backtests.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+# --- score factory ---
+def score_mom_lowvol(mom_L=60, vol_win=30):
+    """Double-sort: momentum z + low-vol z. Both causal (data <= close[i])."""
+    def _score(P):
+        mom = xs.xs_zscore(xs.past_return(P.close, mom_L))
+        # low vol = higher score -> negate std
+        lowvol = xs.xs_zscore(-xs.roll_std(P.ret, vol_win))
+        return mom + lowvol
+    return _score
+
+
+# Grid (<=5 calls total):
+# 1. Baseline: majors H10 k5 LS  (19 assets, closest to XS01 universe)
+# 2. All universe H10 k5 LS
+# 3. All universe H5 k5 LS  (faster rebalance)
+# 4. Majors H10 k5 LS with longer mom window (90d) to differ from XS01
+# 5. All universe H10 k7 LS (wider book)
+
+configs = [
+    dict(name="XS01b-MAJ-H10-k5",    universe="majors", H=10, k=5,  long_short=True,  fn=score_mom_lowvol(60,30)),
+    dict(name="XS01b-ALL-H10-k5",    universe="all",    H=10, k=5,  long_short=True,  fn=score_mom_lowvol(60,30)),
+    dict(name="XS01b-ALL-H5-k5",     universe="all",    H=5,  k=5,  long_short=True,  fn=score_mom_lowvol(60,30)),
+    dict(name="XS01b-MAJ-H10-MOM90", universe="majors", H=10, k=5,  long_short=True,  fn=score_mom_lowvol(90,30)),
+    dict(name="XS01b-ALL-H10-k7",    universe="all",    H=10, k=7,  long_short=True,  fn=score_mom_lowvol(60,30)),
+]
+
+results = []
+for cfg in configs:
+    print(f"\nRunning {cfg['name']} ...")
+    fn = cfg.pop("fn")
+    rep = xs.study_xs(score_fn=fn, **cfg)
+    results.append(rep)
+    print(xs.fmt(rep))
+    print()
+
+# --- pick best: prefer earns_slot, then hold-out sharpe, then corr_xs01 < 0.6
+def score_result(r):
+    earns = 1 if r["earns_slot"] else 0
+    hold_sh = r["holdout"].get("sharpe", -99)
+    full_sh = r["full"]["sharpe"]
+    distinct = 1 if (r["corr_xs01"] or 1.0) < 0.6 else 0
+    return (earns, hold_sh, full_sh, distinct)
+
+best = max(results, key=score_result)
+print("\n" + "="*60)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))