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/XD01.py

@@ -0,0 +1,109 @@
+"""XD01 — Low-skew / anti-lottery cross-sectional strategy.
+
+Score = -roll_skew(ret, 60):  short high-skew "lottery" alts, long low-skew alts.
+Rationale: lottery-preference premium — investors overpay for positive-skew assets
+(right-tail lottery tickets), so they should earn lower returns; negative-skew assets
+are underpriced relative to their systematic risk.
+
+Grid (<=5 calls):
+  1. Baseline: "majors" (19 XS01 universe), H=10, k=5, L/S
+  2. Wider universe: "all" (~49 alts), H=10, k=5, L/S
+  3. Vary rebalance period: "all", H=5, k=5, L/S (more frequent)
+  4. Vary top-k: "all", H=10, k=7, L/S (more diversified)
+  5. Combined: -skew60 + -skew30 blend (multi-horizon), "all", H=10, k=5, L/S
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+SKEW_WIN = 60   # lookback for rolling skew (days)
+SKEW_WIN2 = 30  # shorter lookback for blend
+
+
+def score_anti_lottery(P, win=SKEW_WIN):
+    """Anti-lottery score: negate rolling skew so LOW-skew assets score HIGH (long)."""
+    sk = xs.roll_skew(P.ret, win)   # (n_days x n_assets); higher skew = lottery
+    return -sk                       # higher = lower skew = long
+
+
+def score_anti_lottery_blend(P, w1=SKEW_WIN, w2=SKEW_WIN2):
+    """Multi-horizon blend of negated skews (cross-sectionally z-scored before blend)."""
+    sk1 = xs.xs_zscore(-xs.roll_skew(P.ret, w1))
+    sk2 = xs.xs_zscore(-xs.roll_skew(P.ret, w2))
+    return 0.5 * sk1 + 0.5 * sk2
+
+
+if __name__ == "__main__":
+    results = []
+
+    # --- Run 1: majors universe, H=10, k=5, L/S ---
+    print("Running XD01-v1: majors, H=10, k=5, L/S ...")
+    rep1 = xs.study_xs(
+        "XD01-v1-majors",
+        lambda P: score_anti_lottery(P, 60),
+        universe="majors",
+        H=10, k=5, long_short=True,
+    )
+    print(xs.fmt(rep1))
+    results.append(rep1)
+
+    # --- Run 2: all universe, H=10, k=5, L/S ---
+    print("\nRunning XD01-v2: all, H=10, k=5, L/S ...")
+    rep2 = xs.study_xs(
+        "XD01-v2-all",
+        lambda P: score_anti_lottery(P, 60),
+        universe="all",
+        H=10, k=5, long_short=True,
+    )
+    print(xs.fmt(rep2))
+    results.append(rep2)
+
+    # --- Run 3: all, H=5 (more frequent rebalance), k=5, L/S ---
+    print("\nRunning XD01-v3: all, H=5, k=5, L/S ...")
+    rep3 = xs.study_xs(
+        "XD01-v3-H5",
+        lambda P: score_anti_lottery(P, 60),
+        universe="all",
+        H=5, k=5, long_short=True,
+    )
+    print(xs.fmt(rep3))
+    results.append(rep3)
+
+    # --- Run 4: all, H=10, k=7, L/S (more diversified) ---
+    print("\nRunning XD01-v4: all, H=10, k=7, L/S ...")
+    rep4 = xs.study_xs(
+        "XD01-v4-k7",
+        lambda P: score_anti_lottery(P, 60),
+        universe="all",
+        H=10, k=7, long_short=True,
+    )
+    print(xs.fmt(rep4))
+    results.append(rep4)
+
+    # --- Run 5: blend multi-horizon skew, all, H=10, k=5, L/S ---
+    print("\nRunning XD01-v5: blend skew30+60, all, H=10, k=5, L/S ...")
+    rep5 = xs.study_xs(
+        "XD01-v5-blend",
+        lambda P: score_anti_lottery_blend(P, 60, 30),
+        universe="all",
+        H=10, k=5, long_short=True,
+    )
+    print(xs.fmt(rep5))
+    results.append(rep5)
+
+    # --- Pick best config by: earns_slot > holdout sharpe > full sharpe > distinctness ---
+    def rank_key(r):
+        earns = int(r["earns_slot"])
+        h_sh = r["holdout"].get("sharpe", -99)
+        f_sh = r["full"]["sharpe"]
+        distinct = 1.0 - abs(r["corr_xs01"] or 1.0)  # higher = more distinct
+        verdict_score = {"ADDS": 3, "NEUTRAL": 2, "DILUTES": 1, "REDUNDANT": 0, "N/A": 0}.get(
+            r["marginal"].get("verdict", "N/A"), 0)
+        return (earns, verdict_score, h_sh, f_sh, distinct)
+
+    best = max(results, key=rank_key)
+    print("\n" + "=" * 60)
+    print("BEST CONFIG:")
+    print(xs.fmt(best))
+    print("JSON:", xs.as_json(best))