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/XR05.py

@@ -0,0 +1,78 @@
+"""XR05 — Overreaction Reversal (mid-horizon)
+IDEA: Score = -past_return(close, L) for L in {20, 30}.
+Assets that ran up the most over the past 20-30 days are SHORTED (expected to mean-revert);
+assets that dropped the most are LONGED. Pure cross-sectional contrarian on multi-week moves.
+
+Grid (<= 5 calls):
+  1. L=20, H=10, k=5, LS, universe=majors
+  2. L=30, H=10, k=5, LS, universe=majors
+  3. L=20, H=5,  k=5, LS, universe=majors  (faster rebal)
+  4. blend -PR20 and -PR30 (mean z-score), H=10, k=5, LS, universe=majors
+  5. blend -PR20 and -PR30, H=10, k=5, LS, universe=all  (broader universe)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+# ------------------------------------------------------------------
+# Score helpers (causal: close[i] only uses data up to bar i)
+# ------------------------------------------------------------------
+def score_rev20(P):
+    return -xs.past_return(P.close, 20)
+
+def score_rev30(P):
+    return -xs.past_return(P.close, 30)
+
+def score_rev20_fast(P):
+    return -xs.past_return(P.close, 20)
+
+def score_blend_majors(P):
+    z20 = xs.xs_zscore(-xs.past_return(P.close, 20))
+    z30 = xs.xs_zscore(-xs.past_return(P.close, 30))
+    return (z20 + z30) / 2.0
+
+def score_blend_all(P):
+    z20 = xs.xs_zscore(-xs.past_return(P.close, 20))
+    z30 = xs.xs_zscore(-xs.past_return(P.close, 30))
+    return (z20 + z30) / 2.0
+
+# ------------------------------------------------------------------
+# Grid
+# ------------------------------------------------------------------
+configs = [
+    dict(name="XR05-REV20-H10-k5-majors",  fn=score_rev20,      universe="majors", H=10, k=5, long_short=True),
+    dict(name="XR05-REV30-H10-k5-majors",  fn=score_rev30,      universe="majors", H=10, k=5, long_short=True),
+    dict(name="XR05-REV20-H5-k5-majors",   fn=score_rev20_fast, universe="majors", H=5,  k=5, long_short=True),
+    dict(name="XR05-BLENDz-H10-k5-majors", fn=score_blend_majors, universe="majors", H=10, k=5, long_short=True),
+    dict(name="XR05-BLENDz-H10-k5-all",    fn=score_blend_all,  universe="all",    H=10, k=5, long_short=True),
+]
+
+results = []
+for c in configs:
+    print(f"\nRunning {c['name']} ...")
+    rep = xs.study_xs(
+        c["name"],
+        c["fn"],
+        universe=c["universe"],
+        H=c["H"],
+        k=c["k"],
+        long_short=c["long_short"],
+    )
+    print(xs.fmt(rep))
+    results.append(rep)
+
+# ------------------------------------------------------------------
+# Pick best config: earns_slot first, then hold-out sharpe, then distinctness
+# ------------------------------------------------------------------
+def _sort_key(r):
+    earns = int(r["earns_slot"])
+    hold_sh = r["holdout"].get("sharpe", -99)
+    corr_xs01 = abs(r["corr_xs01"] or 1.0)
+    return (earns, hold_sh, -corr_xs01)
+
+best = max(results, key=_sort_key)
+print("\n" + "="*60)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))