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/XS04b.py

@@ -0,0 +1,83 @@
+"""XS04b — Ensemble z-vote cross-sectional strategy.
+
+Score = mean of xs_zscore over {momentum90, -vol30, -skew60, -beta60}.
+Each component is z-scored cross-sectionally per row, then averaged.
+Diversified signal: momentum (strong assets), low vol (stable), negative skew
+(avoid lottery stocks), low beta (idiosyncratic leaders).
+
+Grid: universe x H x k — 5 calls max.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+def score_matrix(P: xs.Panel) -> np.ndarray:
+    """Ensemble z-vote: mean of four xs_zscored components (causal)."""
+    # 1. Momentum 90d: higher = stronger recent trend
+    mom90 = xs.past_return(P.close, 90)
+    z_mom = xs.xs_zscore(mom90)
+
+    # 2. Negative vol 30d: lower vol = more stable = prefer
+    vol30 = xs.roll_std(P.ret, 30)
+    z_vol = xs.xs_zscore(-vol30)   # negative: lower vol -> higher score
+
+    # 3. Negative skew 60d: negative skew = avoid lottery/pump; prefer normal/negative-skew
+    skew60 = xs.roll_skew(P.ret, 60)
+    z_skew = xs.xs_zscore(-skew60)  # negative: lower skew -> higher score
+
+    # 4. Negative beta 60d: low-beta assets have idiosyncratic edge in cross-section
+    beta60 = xs.roll_beta(P.ret, 60)
+    z_beta = xs.xs_zscore(-beta60)  # negative: lower beta -> higher score
+
+    # Ensemble: simple mean across components (NaN-safe per cell)
+    stack = np.stack([z_mom, z_vol, z_skew, z_beta], axis=0)
+    score = np.nanmean(stack, axis=0)
+    return score
+
+
+# ── Grid (5 calls max) ──────────────────────────────────────────────────────
+results = []
+
+# 1. Majors, H=10, k=5, L/S
+r = xs.study_xs("XS04b_maj_H10_k5_ls", score_matrix, universe="majors",
+                H=10, k=5, long_short=True)
+print(xs.fmt(r)); print("JSON:", xs.as_json(r))
+results.append(r)
+
+# 2. Majors, H=5, k=5, L/S  (faster rebalance)
+r = xs.study_xs("XS04b_maj_H5_k5_ls", score_matrix, universe="majors",
+                H=5, k=5, long_short=True)
+print(xs.fmt(r)); print("JSON:", xs.as_json(r))
+results.append(r)
+
+# 3. All, H=10, k=5, L/S
+r = xs.study_xs("XS04b_all_H10_k5_ls", score_matrix, universe="all",
+                H=10, k=5, long_short=True)
+print(xs.fmt(r)); print("JSON:", xs.as_json(r))
+results.append(r)
+
+# 4. All, H=10, k=7, L/S  (wider book)
+r = xs.study_xs("XS04b_all_H10_k7_ls", score_matrix, universe="all",
+                H=10, k=7, long_short=True)
+print(xs.fmt(r)); print("JSON:", xs.as_json(r))
+results.append(r)
+
+# 5. Majors, H=10, k=5, long-only  (avoid short-side noise)
+r = xs.study_xs("XS04b_maj_H10_k5_lo", score_matrix, universe="majors",
+                H=10, k=5, long_short=False)
+print(xs.fmt(r)); print("JSON:", xs.as_json(r))
+results.append(r)
+
+# ── Pick best by: earns_slot > holdout > corr_xs01 distance ─────────────────
+def score_rep(r):
+    es = 1 if r.get("earns_slot") else 0
+    ho = r.get("holdout", {}).get("sharpe", -99)
+    dist = 1 - abs(r.get("corr_xs01", 1))  # distinctness
+    return (es, ho, dist)
+
+best = max(results, key=score_rep)
+print("\n=== BEST CONFIG ===")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))