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/XM02.py

@@ -0,0 +1,88 @@
+"""XM02 — Multi-L z-blend momentum
+Score = mean of xs_zscore(past_return(close, L)) over a set of lookback windows L.
+Compare two window sets: {30,90} (XS01-like) vs {20,60,120} (extended).
+Grid: 5 study_xs calls total — vary universe / windows / H.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+# ── score helpers ─────────────────────────────────────────────────────────────
+
+def blend_mom(close, lookbacks):
+    """Mean of xs_zscore(past_return(close, L)) for each L in lookbacks."""
+    scores = [xs.xs_zscore(xs.past_return(close, L)) for L in lookbacks]
+    stacked = np.stack(scores, axis=2)          # (n_days, n_assets, n_L)
+    return np.nanmean(stacked, axis=2)           # (n_days, n_assets)
+
+
+L_SHORT  = [30, 90]            # mirrors XS01 blend
+L_LONG   = [20, 60, 120]       # extended set
+L_WIDE   = [20, 60, 90, 120]   # even wider blend
+
+# ── 5 backtests ───────────────────────────────────────────────────────────────
+
+results = []
+
+# 1. XS01-equivalent blend {30,90} on ALL universe — baseline reference
+rep1 = xs.study_xs(
+    "XM02-3090-all",
+    lambda P: blend_mom(P.close, [30, 90]),
+    universe="all", H=10, k=5, long_short=True,
+)
+print(xs.fmt(rep1))
+results.append(rep1)
+
+# 2. Extended blend {20,60,120} on ALL universe
+rep2 = xs.study_xs(
+    "XM02-206012-all",
+    lambda P: blend_mom(P.close, [20, 60, 120]),
+    universe="all", H=10, k=5, long_short=True,
+)
+print(xs.fmt(rep2))
+results.append(rep2)
+
+# 3. Extended blend {20,60,120} on MAJORS (19 alts — XS01 universe)
+rep3 = xs.study_xs(
+    "XM02-206012-majors",
+    lambda P: blend_mom(P.close, [20, 60, 120]),
+    universe="majors", H=10, k=5, long_short=True,
+)
+print(xs.fmt(rep3))
+results.append(rep3)
+
+# 4. Wide blend {20,60,90,120} on ALL, shorter rebalance H=5
+rep4 = xs.study_xs(
+    "XM02-wide-H5-all",
+    lambda P: blend_mom(P.close, [20, 60, 90, 120]),
+    universe="all", H=5, k=5, long_short=True,
+)
+print(xs.fmt(rep4))
+results.append(rep4)
+
+# 5. Wide blend on ALL, longer H=20 (less turnover)
+rep5 = xs.study_xs(
+    "XM02-wide-H20-all",
+    lambda P: blend_mom(P.close, [20, 60, 90, 120]),
+    universe="all", H=20, k=5, long_short=True,
+)
+print(xs.fmt(rep5))
+results.append(rep5)
+
+# ── pick BEST by: earns_slot > hold-out sharpe > distinctness ────────────────
+
+def _score(r):
+    earns   = 1 if r["earns_slot"] else 0
+    verdict = 1 if r["marginal"].get("verdict") == "ADDS" else 0
+    hold_sh = r["holdout"].get("sharpe", -99)
+    full_sh = r["full"]["sharpe"]
+    dist    = 1 if (r["corr_xs01"] or 1.0) < 0.6 else 0
+    return (earns, verdict, hold_sh, full_sh, dist)
+
+best = max(results, key=_score)
+
+print("\n" + "=" * 60)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))