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/XR03.py

@@ -0,0 +1,92 @@
+"""XR03 — Residual Short-Term Reversal
+Score = -(sum of residual_return over last L days)
+Idiosyncratic reversal: removes market beta before computing the short-term reversal signal.
+L in {3, 5}; beta window fixed at 60d.
+
+Grid (<= 5 study_xs calls):
+  1. L=3, majors, H=5, k=5, long_short=True
+  2. L=5, majors, H=5, k=5, long_short=True
+  3. L=3, all, H=5, k=5, long_short=True
+  4. L=5, all, H=5, k=5, long_short=True
+  5. Best-L from above, all, H=10, k=5, long_short=True
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+BETA_WIN = 60  # rolling beta window for residual computation
+
+
+def score_xr03(P, L):
+    """Causal residual reversal score.
+    residual[i] = ret[i] - beta_rolling[i] * market_ret[i]
+    score[i] = -sum(residual[i-L+1 .. i])
+    HIGHER score = more negative recent idio returns = long (reversal)
+    """
+    res = xs.residual_return(P.ret, BETA_WIN)  # (n_days, n_assets)
+    # rolling sum of last L residuals (causal: sum of rows [i-L+1..i])
+    res_sum = xs.roll_mean(res, L) * L  # roll_mean * L = roll_sum
+    # reversal: negative of cumulative idio return
+    score = -res_sum
+    return score
+
+
+# --- Grid ---
+results = []
+
+# 1. L=3, majors
+r1 = xs.study_xs("XR03_L3_maj", lambda P: score_xr03(P, 3),
+                 universe="majors", H=5, k=5, long_short=True)
+results.append(r1)
+print("=== XR03 L3 majors H5 ===")
+print(xs.fmt(r1))
+
+# 2. L=5, majors
+r2 = xs.study_xs("XR03_L5_maj", lambda P: score_xr03(P, 5),
+                 universe="majors", H=5, k=5, long_short=True)
+results.append(r2)
+print("=== XR03 L5 majors H5 ===")
+print(xs.fmt(r2))
+
+# 3. L=3, all
+r3 = xs.study_xs("XR03_L3_all", lambda P: score_xr03(P, 3),
+                 universe="all", H=5, k=5, long_short=True)
+results.append(r3)
+print("=== XR03 L3 all H5 ===")
+print(xs.fmt(r3))
+
+# 4. L=5, all
+r4 = xs.study_xs("XR03_L5_all", lambda P: score_xr03(P, 5),
+                 universe="all", H=5, k=5, long_short=True)
+results.append(r4)
+print("=== XR03 L5 all H5 ===")
+print(xs.fmt(r4))
+
+# 5. Best-L (by hold-out sharpe) with H=10
+# pick best L from runs 1-4
+best_so_far = max(results, key=lambda r: r["holdout"]["sharpe"])
+best_L = 3 if "L3" in best_so_far["name"] else 5
+best_univ = "all" if "all" in best_so_far["name"] else "majors"
+
+r5 = xs.study_xs(f"XR03_L{best_L}_{best_univ}_H10",
+                 lambda P: score_xr03(P, best_L),
+                 universe=best_univ, H=10, k=5, long_short=True)
+results.append(r5)
+print(f"=== XR03 L{best_L} {best_univ} H10 ===")
+print(xs.fmt(r5))
+
+# --- Pick best overall by marginal robustness then hold-out ---
+def score_key(r):
+    earns = r.get("earns_slot", False)
+    oos = r.get("marginal", {}).get("robust_oos", False)
+    verdict = r.get("marginal", {}).get("verdict", "")
+    adds = verdict == "ADDS"
+    hold = r["holdout"]["sharpe"]
+    full = r["full"]["sharpe"]
+    return (int(earns), int(adds), int(oos), hold, full)
+
+best = max(results, key=score_key)
+print("\n========== BEST CONFIG ==========")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))