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/XM06.py

@@ -0,0 +1,79 @@
+"""XM06 — 52-day-high proximity (closeness-to-recent-high momentum).
+
+IDEA: Score = close / rolling_max(high, W)  [closeness to recent high].
+Assets near their recent high are "in momentum"; rank them cross-sectionally.
+W in {60, 90}. Causal: rolling_max up through bar i only.
+
+Grid: 5 calls max
+  1. W=60, majors, H=10, k=5, L/S
+  2. W=90, majors, H=10, k=5, L/S
+  3. W=60, all,    H=10, k=5, L/S   (best-W on wider universe)
+  4. W=60, all,    H=5,  k=5, L/S   (faster rebalance)
+  5. W=60, all,    H=10, k=7, L/S   (wider book)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+def score_proximity(P, W):
+    """Causal: close[i] / max(high[i-W+1 .. i]). Higher = closer to recent high = long."""
+    n, A = P.close.shape
+    out = np.full((n, A), np.nan)
+    # rolling max of high, causal window [i-W+1 .. i]
+    high_df = __import__("pandas").DataFrame(P.high)
+    roll_max = high_df.rolling(W, min_periods=max(2, W // 2)).max().values
+    # proximity ratio: close / recent_high (always in (0,1] if no gap-up above window)
+    out = P.close / roll_max
+    return out
+
+
+# ---- run grid ----
+
+results = []
+
+# 1. W=60, majors, H=10, k=5, L/S
+rep1 = xs.study_xs("XM06_W60_majors", lambda P: score_proximity(P, 60),
+                   universe="majors", H=10, k=5, long_short=True)
+print(xs.fmt(rep1))
+results.append(rep1)
+
+# 2. W=90, majors, H=10, k=5, L/S
+rep2 = xs.study_xs("XM06_W90_majors", lambda P: score_proximity(P, 90),
+                   universe="majors", H=10, k=5, long_short=True)
+print(xs.fmt(rep2))
+results.append(rep2)
+
+# 3. W=60, all, H=10, k=5, L/S
+rep3 = xs.study_xs("XM06_W60_all", lambda P: score_proximity(P, 60),
+                   universe="all", H=10, k=5, long_short=True)
+print(xs.fmt(rep3))
+results.append(rep3)
+
+# 4. W=60, all, H=5, k=5, L/S  (faster rebalance)
+rep4 = xs.study_xs("XM06_W60_all_H5", lambda P: score_proximity(P, 60),
+                   universe="all", H=5, k=5, long_short=True)
+print(xs.fmt(rep4))
+results.append(rep4)
+
+# 5. W=60, all, H=10, k=7, L/S  (wider book)
+rep5 = xs.study_xs("XM06_W60_all_k7", lambda P: score_proximity(P, 60),
+                   universe="all", H=10, k=7, long_short=True)
+print(xs.fmt(rep5))
+results.append(rep5)
+
+# ---- pick best by: earns_slot > hold-out sharpe > distinctness ----
+def score_result(r):
+    earns = 1 if r["earns_slot"] else 0
+    adds = 1 if r["marginal"].get("verdict") == "ADDS" else 0
+    hold_sh = r["holdout"].get("sharpe", -999)
+    full_sh = r["full"]["sharpe"]
+    corr_xs01 = abs(r.get("corr_xs01") or 1.0)
+    distinct = 1 if corr_xs01 < 0.6 else 0
+    return (earns, adds, hold_sh, full_sh, distinct)
+
+best = max(results, key=score_result)
+print("\n=== BEST CONFIG ===")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))