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/XVa1.py

@@ -0,0 +1,85 @@
+"""XVa1 — Distance-from-MA value signal.
+
+Score = -(close / roll_mean(close, W) - 1)
+Long assets furthest BELOW their rolling MA (cheap / mean-reverting).
+Short assets furthest ABOVE their rolling MA (expensive).
+
+Grid: W in {60, 100}, universe all/majors, H in {10, 20}.
+Max 5 study_xs calls.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+def score_val(close, W):
+    """Causal value score: -(close / MA - 1). Higher = more below MA = long."""
+    ma = xs.roll_mean(close, W)
+    return -(close / ma - 1.0)
+
+
+results = []
+
+# Config 1: W=60, all assets, H=10, k=5, LS
+r1 = xs.study_xs(
+    "XVa1-W60-all-H10",
+    lambda P: score_val(P.close, 60),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(r1))
+print("JSON:", xs.as_json(r1))
+results.append(r1)
+
+# Config 2: W=100, all assets, H=10, k=5, LS
+r2 = xs.study_xs(
+    "XVa1-W100-all-H10",
+    lambda P: score_val(P.close, 100),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(r2))
+print("JSON:", xs.as_json(r2))
+results.append(r2)
+
+# Config 3: W=60, majors, H=10, k=5, LS
+r3 = xs.study_xs(
+    "XVa1-W60-majors-H10",
+    lambda P: score_val(P.close, 60),
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(r3))
+print("JSON:", xs.as_json(r3))
+results.append(r3)
+
+# Config 4: W=60, all assets, H=20, k=5, LS (slower rebal)
+r4 = xs.study_xs(
+    "XVa1-W60-all-H20",
+    lambda P: score_val(P.close, 60),
+    universe="all", H=20, k=5, long_short=True
+)
+print(xs.fmt(r4))
+print("JSON:", xs.as_json(r4))
+results.append(r4)
+
+# Config 5: W=100, all assets, H=20, k=5, LS
+r5 = xs.study_xs(
+    "XVa1-W100-all-H20",
+    lambda P: score_val(P.close, 100),
+    universe="all", H=20, k=5, long_short=True
+)
+print(xs.fmt(r5))
+print("JSON:", xs.as_json(r5))
+results.append(r5)
+
+# Pick best by: earns_slot first, then hold-out Sharpe, then full Sharpe
+def rank_key(r):
+    earns = int(r["earns_slot"])
+    hold_sh = r["holdout"].get("sharpe", -99)
+    full_sh = r["full"].get("sharpe", -99)
+    return (earns, hold_sh, full_sh)
+
+best = max(results, key=rank_key)
+print("\n=== BEST CONFIG ===")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))