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/XM05.py

@@ -0,0 +1,98 @@
+"""XM05 — Momentum Acceleration
+MECHANISM: Score = past_return(close, L_short) - past_return(close, L_long)
+           i.e. is momentum ACCELERATING? The idea: assets that are outperforming
+           recently vs. their longer-run momentum are gaining momentum -> rank them
+           high. Assets that were strong long-term but are slowing down -> rank low.
+           L_short=20, L_long=60 (canonical config).
+
+Grid: vary universe (all/majors), H (5/10), and L_short param
+      to find the best config within <=5 backtests.
+
+Distinctness target: if score is correlated to raw momentum (XS01), it's just XS01.
+If acceleration captures something different (regime change, reversal of leaders), it
+could be distinct and add to portfolio.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+print("XM05 — Momentum Acceleration (L_short - L_long)")
+print("=" * 60)
+
+
+def mom_accel(close, L_short, L_long):
+    """Score = short-term return minus long-term return (causal). Higher = accelerating."""
+    r_short = xs.past_return(close, L_short)
+    r_long  = xs.past_return(close, L_long)
+    return r_short - r_long
+
+
+# --- 5 targeted backtests ---
+
+# 1) Canonical config: all universe, L_short=20, L_long=60, H=10, LS
+rep1 = xs.study_xs(
+    "XM05_ALL_20_60",
+    lambda P: mom_accel(P.close, 20, 60),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep1))
+print("JSON:", xs.as_json(rep1))
+print()
+
+# 2) Majors universe (19 XS01 assets), same canonical L_short=20, L_long=60
+rep2 = xs.study_xs(
+    "XM05_MAJ_20_60",
+    lambda P: mom_accel(P.close, 20, 60),
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep2))
+print("JSON:", xs.as_json(rep2))
+print()
+
+# 3) All universe, shorter window: L_short=10, L_long=30 (faster acceleration signal)
+rep3 = xs.study_xs(
+    "XM05_ALL_10_30",
+    lambda P: mom_accel(P.close, 10, 30),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep3))
+print("JSON:", xs.as_json(rep3))
+print()
+
+# 4) All universe, L_short=20, L_long=60, longer holding period H=20
+rep4 = xs.study_xs(
+    "XM05_ALL_20_60_H20",
+    lambda P: mom_accel(P.close, 20, 60),
+    universe="all", H=20, k=5, long_short=True
+)
+print(xs.fmt(rep4))
+print("JSON:", xs.as_json(rep4))
+print()
+
+# 5) All universe, longer windows: L_short=30, L_long=90 (medium-term acceleration)
+rep5 = xs.study_xs(
+    "XM05_ALL_30_90",
+    lambda P: mom_accel(P.close, 30, 90),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep5))
+print("JSON:", xs.as_json(rep5))
+print()
+
+# Pick best: prefer earns_slot, then hold-out sharpe, then distinctness from XS01
+all_reps = [rep1, rep2, rep3, rep4, rep5]
+
+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)  # higher = more distinct
+    return (earns, hold_sh, full_sh, distinctness)
+
+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))