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/XS06b.py

@@ -0,0 +1,88 @@
+"""XS06b — Correlation-to-market diversifier.
+
+Score = -rolling_corr(asset_ret, market_ret, 60)
+Long the assets LEAST correlated to the equal-weight market (the "divergers"),
+short the most-correlated ones. win=60 days.
+
+Idea: if cross-sectional momentum (XS01) selects by recent past return, this
+selects by structural independence from the pack — a fundamentally different
+axis. The two should be weakly correlated.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+import pandas as pd
+
+
+def score_corr_diversifier(P, win=60):
+    """Score = -rolling_corr(asset_ret, market_ret, win). Causal."""
+    n, A = P.ret.shape
+    mkt = xs.market_ret(P.ret)  # (n,) equal-weight market
+
+    out = np.full((n, A), np.nan)
+    mkt_s = pd.Series(mkt)
+
+    for a in range(A):
+        asset_s = pd.Series(P.ret[:, a])
+        # rolling correlation — pandas rolling corr is causal
+        corr = asset_s.rolling(win, min_periods=max(10, win // 2)).corr(mkt_s)
+        # score = NEGATIVE correlation: higher => less correlated => long
+        out[:, a] = -corr.values
+
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Grid: 5 study_xs calls max
+#   - vary universe (all vs majors)
+#   - vary H (rebalance freq)
+#   - vary long_short
+# ---------------------------------------------------------------------------
+
+print("=" * 70)
+print("XS06b — Correlation-to-market diversifier (score = -roll_corr_60)")
+print("=" * 70)
+
+best = None
+best_earns = False
+best_ho = -999
+
+configs = [
+    # (label_suffix, universe, H, k, long_short)
+    ("all_H10_k5_ls",   "all",     10, 5, True),
+    ("maj_H10_k5_ls",   "majors",  10, 5, True),
+    ("all_H5_k5_ls",    "all",      5, 5, True),
+    ("all_H10_k5_lo",   "all",     10, 5, False),
+    ("all_H20_k5_ls",   "all",     20, 5, True),
+]
+
+results = []
+for (suffix, universe, H, k, ls) in configs:
+    name = f"XS06b_{suffix}"
+    print(f"\n--- {name} ---")
+    rep = xs.study_xs(
+        name,
+        lambda P: score_corr_diversifier(P, win=60),
+        universe=universe,
+        H=H,
+        k=k,
+        long_short=ls,
+        target_vol=0.20,
+    )
+    print(xs.fmt(rep))
+    print("JSON:", xs.as_json(rep))
+    results.append(rep)
+
+    # track best: earns_slot first, then hold-out sharpe
+    earns = rep.get("earns_slot", False)
+    ho_sh = rep.get("holdout", {}).get("sharpe", -999)
+    if (earns and not best_earns) or (earns == best_earns and ho_sh > best_ho):
+        best = rep
+        best_earns = earns
+        best_ho = ho_sh
+
+print("\n" + "=" * 70)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))