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/XM03.py

@@ -0,0 +1,98 @@
+"""XM03 — Vol-Scaled (Risk-Adjusted) Momentum
+MECHANISM: Score = past_return(close, L) / roll_std(ret, L)
+This is a Sharpe-like signal: normalises raw momentum by the volatility of that asset
+over the same window. Should favour assets that moved up *smoothly* (high Sharpe trend)
+over those that had large one-off jumps (noisy high return).
+Grid: L in {30, 60, 90}; universe in {all, majors}; long_short True/False.
+Goal: test if risk-adjusted scoring is DISTINCT from plain XS01 momentum and ADDS to the
+live TP01+XS01+VRP01 portfolio.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+def vol_adj_momentum(P, L: int) -> np.ndarray:
+    """Causal Sharpe-like score: past_return / roll_std(ret, L).
+    Higher = long. Returns (n_days x n_assets).
+    Avoid divide-by-zero by replacing 0-vol rows with NaN -> harness treats NaN as neutral.
+    """
+    pr = xs.past_return(P.close, L)          # causal past return over L days
+    rv = xs.roll_std(P.ret, L)               # causal rolling std of daily returns
+    # Replace zeros/near-zeros with NaN to avoid Inf
+    rv_safe = np.where(rv < 1e-8, np.nan, rv)
+    score = pr / rv_safe
+    return score
+
+
+print("XM03 — Vol-Scaled (Risk-Adjusted) Momentum")
+print("=" * 60)
+
+# 1) All universe, L=30 (short horizon vol-adj)
+rep1 = xs.study_xs(
+    "XM03_ALL_L30",
+    lambda P: vol_adj_momentum(P, 30),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep1))
+print("JSON:", xs.as_json(rep1))
+print()
+
+# 2) All universe, L=60 (medium horizon)
+rep2 = xs.study_xs(
+    "XM03_ALL_L60",
+    lambda P: vol_adj_momentum(P, 60),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep2))
+print("JSON:", xs.as_json(rep2))
+print()
+
+# 3) All universe, L=90 (long horizon)
+rep3 = xs.study_xs(
+    "XM03_ALL_L90",
+    lambda P: vol_adj_momentum(P, 90),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep3))
+print("JSON:", xs.as_json(rep3))
+print()
+
+# 4) Majors only (same universe as XS01), L=60 — can vol-adj beat plain MOM on XS01 turf?
+rep4 = xs.study_xs(
+    "XM03_MAJORS_L60",
+    lambda P: vol_adj_momentum(P, 60),
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep4))
+print("JSON:", xs.as_json(rep4))
+print()
+
+# 5) All universe, L=60, long-only — does vol-adj work as selection filter?
+rep5 = xs.study_xs(
+    "XM03_ALL_L60_LO",
+    lambda P: vol_adj_momentum(P, 60),
+    universe="all", H=10, k=5, long_short=False
+)
+print(xs.fmt(rep5))
+print("JSON:", xs.as_json(rep5))
+print()
+
+
+def score_rep(r):
+    earns = int(r["earns_slot"])
+    hold_sh = r["holdout"].get("sharpe", -9)
+    full_sh = r["full"]["sharpe"]
+    corr_xs01 = r.get("corr_xs01") or 1.0
+    distinctness = 1 - abs(corr_xs01)
+    return (earns, hold_sh, full_sh, distinctness)
+
+
+all_reps = [rep1, rep2, rep3, rep4, rep5]
+best = max(all_reps, key=score_rep)
+
+print("=" * 60)
+print(f"BEST CONFIG: {best['name']}")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))