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/XU02.py

@@ -0,0 +1,120 @@
+"""XU02 — Rebalance/Holding Sweep (Low-Vol + Momentum)
+MECHANISM: Study how holding period H and portfolio size k interact with signal quality.
+Two signals: (1) pure momentum blend (z30+z90 same as XS01), (2) low-vol rank (short volatile, long stable).
+Goal: find whether a DIFFERENT H/k pair or signal gives something DISTINCT from XS01.
+
+Hypothesis:
+  - XS01 uses H=10, k=5 (momentum). A longer H reduces turnover, captures slower signal decay.
+  - Low-vol selection (long stable alts, short volatile ones) is conceptually orthogonal to momentum.
+  - Sweep: H in {5,10,20,30}, k in {3,5,8}, signal in {momentum, low_vol}.
+  - Keep <=5 backtests: focus on the most contrasting configs.
+
+Grid (5 backtests):
+  1. MOM  H=5  k=5  — fast rebalance, same as XS01 direction, more turnover
+  2. MOM  H=30 k=5  — slow rebalance, lower turnover, tests signal persistence
+  3. LVOL H=10 k=5  — low-vol signal at standard H/k (conceptually distinct from momentum)
+  4. LVOL H=20 k=5  — low-vol with slower rebalance
+  5. LVOL H=10 k=3  — low-vol tight portfolio, more concentrated
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+print("XU02 — Rebalance/Holding Sweep (Low-Vol + Momentum)")
+print("=" * 60)
+
+
+def mom_blend(P):
+    """Z-blend of 30d and 90d momentum (same signal as XS01)."""
+    z30 = xs.xs_zscore(xs.past_return(P.close, 30))
+    z90 = xs.xs_zscore(xs.past_return(P.close, 90))
+    return np.nanmean(np.stack([z30, z90], axis=0), axis=0)
+
+
+def low_vol(P):
+    """Low-vol signal: score = -rolling_std(ret, 30). Higher score = lower vol = long.
+    Cross-sectionaly ranks alts: stable (low realized vol) go long, volatile go short."""
+    rv = xs.roll_std(P.ret, 30)
+    return -rv   # negate: higher = lower vol = prefer long
+
+
+# 1) Momentum H=5 k=5 — fast rebalance (more turnover, tests short-term signal)
+rep1 = xs.study_xs(
+    "XU02_MOM_H5k5",
+    mom_blend,
+    universe="majors", H=5, k=5, long_short=True
+)
+print(xs.fmt(rep1))
+print("JSON:", xs.as_json(rep1))
+print()
+
+# 2) Momentum H=30 k=5 — slow rebalance, lower turnover, tests signal persistence
+rep2 = xs.study_xs(
+    "XU02_MOM_H30k5",
+    mom_blend,
+    universe="majors", H=30, k=5, long_short=True
+)
+print(xs.fmt(rep2))
+print("JSON:", xs.as_json(rep2))
+print()
+
+# 3) Low-vol H=10 k=5 — standard H/k but conceptually distinct signal
+rep3 = xs.study_xs(
+    "XU02_LVOL_H10k5",
+    low_vol,
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep3))
+print("JSON:", xs.as_json(rep3))
+print()
+
+# 4) Low-vol H=20 k=5 — slower rebalance, low-vol is a structural trait (changes slowly)
+rep4 = xs.study_xs(
+    "XU02_LVOL_H20k5",
+    low_vol,
+    universe="majors", H=20, k=5, long_short=True
+)
+print(xs.fmt(rep4))
+print("JSON:", xs.as_json(rep4))
+print()
+
+# 5) Low-vol H=10 k=3 — tighter portfolio (top/bottom 3 most extreme)
+rep5 = xs.study_xs(
+    "XU02_LVOL_H10k3",
+    low_vol,
+    universe="majors", H=10, k=3, long_short=True
+)
+print(xs.fmt(rep5))
+print("JSON:", xs.as_json(rep5))
+print()
+
+# Summary
+print("=" * 60)
+print("SUMMARY: All 5 configs ranked by hold-out Sharpe")
+all_reps = [rep1, rep2, rep3, rep4, rep5]
+ranked = sorted(all_reps, key=lambda r: r["holdout"].get("sharpe", -999), reverse=True)
+for r in ranked:
+    h_sh = r["holdout"].get("sharpe", 0)
+    f_sh = r["full"]["sharpe"]
+    c_xs01 = r["corr_xs01"]
+    verdict = r["marginal"].get("verdict", "N/A")
+    earns = r["earns_slot"]
+    print(f"  {r['name']:25s} FULL={f_sh:+.2f} HOLD={h_sh:+.2f} corr_xs01={c_xs01} "
+          f"verdict={verdict} earns_slot={earns}")
+
+# Pick best by marginal robustness -> earns_slot -> hold-out -> distinctness
+best = None
+for r in ranked:
+    if r["earns_slot"]:
+        best = r
+        break
+if best is None:
+    # fallback: best hold-out with corr_xs01 < 0.6
+    candidates = [r for r in ranked if (r.get("corr_xs01") or 1.0) < 0.6 and r["holdout"].get("sharpe", 0) > 0]
+    best = candidates[0] if candidates else ranked[0]
+
+print()
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))