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/XM10.py

@@ -0,0 +1,127 @@
+"""XM10 — Rank-weighted continuous momentum (demeaned xs_rank).
+
+MECHANISM: Instead of top-k/bottom-k binary selection, weight ALL assets
+proportionally to their demeaned cross-sectional rank of past return.
+rank_i in [0,1] -> demeaned rank = rank_i - 0.5 -> scores in [-0.5, +0.5].
+L=60 (lookback ~2 months). Continuous book approximated via large k (A//2)
+and fine score (continuous rank, not discrete order).
+
+The study_xs() engine still uses top-k/bottom-k for the actual rebalance,
+but by setting k=A//2 (half the universe) and using xs_rank as the score,
+the effective weight profile is nearly linear across the full distribution.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+# -----------------------------------------------------------------------
+# Score: demeaned cross-sectional rank of 60-day past return
+# Higher score = longer weight. Causal: uses data up to and including bar i.
+# -----------------------------------------------------------------------
+L = 60  # lookback
+
+
+def score_rank_mom(P, L=60):
+    """Continuous rank-weighted momentum score.
+    xs_rank -> [0,1]; demean -> [-0.5, +0.5] so it's symmetric long/short.
+    """
+    pr = xs.past_return(P.close, L)   # (n_days, n_assets)
+    ranked = xs.xs_rank(pr)           # [0,1] cross-sectionally per row
+    return ranked - 0.5               # demeaned: positive = long
+
+
+# -----------------------------------------------------------------------
+# Small grid: 5 studies
+#   1) majors, H=10, k=large (9 ~ A//2 of 19)
+#   2) all, H=10, k=large (24 ~ A//2 of 49)
+#   3) all, H=5,  k=large (24) — faster rebalance
+#   4) all, H=10, k=large (24), L=30 — shorter lookback
+#   5) all, H=20, k=large (24) — slower rebalance
+# -----------------------------------------------------------------------
+
+results = []
+
+print("=== XM10 Rank-Weighted Continuous Momentum ===\n")
+
+# 1) Majors universe, H=10, k=9 (A//2 of 19)
+r1 = xs.study_xs(
+    "XM10-majors-H10-k9-L60",
+    lambda P: score_rank_mom(P, L=60),
+    universe="majors",
+    H=10, k=9, long_short=True
+)
+print(xs.fmt(r1))
+print("JSON:", xs.as_json(r1))
+print()
+results.append(r1)
+
+# 2) All (49 alts), H=10, k=24 (A//2)
+r2 = xs.study_xs(
+    "XM10-all-H10-k24-L60",
+    lambda P: score_rank_mom(P, L=60),
+    universe="all",
+    H=10, k=24, long_short=True
+)
+print(xs.fmt(r2))
+print("JSON:", xs.as_json(r2))
+print()
+results.append(r2)
+
+# 3) All, H=5, k=24 — faster rebalance
+r3 = xs.study_xs(
+    "XM10-all-H5-k24-L60",
+    lambda P: score_rank_mom(P, L=60),
+    universe="all",
+    H=5, k=24, long_short=True
+)
+print(xs.fmt(r3))
+print("JSON:", xs.as_json(r3))
+print()
+results.append(r3)
+
+# 4) All, H=10, k=24, L=30 shorter lookback
+r4 = xs.study_xs(
+    "XM10-all-H10-k24-L30",
+    lambda P: score_rank_mom(P, L=30),
+    universe="all",
+    H=10, k=24, long_short=True
+)
+print(xs.fmt(r4))
+print("JSON:", xs.as_json(r4))
+print()
+results.append(r4)
+
+# 5) All, H=20, k=24, L=60 slower rebalance
+r5 = xs.study_xs(
+    "XM10-all-H20-k24-L60",
+    lambda P: score_rank_mom(P, L=60),
+    universe="all",
+    H=20, k=24, long_short=True
+)
+print(xs.fmt(r5))
+print("JSON:", xs.as_json(r5))
+print()
+results.append(r5)
+
+# -----------------------------------------------------------------------
+# Pick best by: earns_slot first, then hold-out sharpe, then distinctness
+# -----------------------------------------------------------------------
+def score_result(r):
+    es = int(r.get("earns_slot", False))
+    hold_sh = r["holdout"].get("sharpe", -99)
+    corr_xs01 = abs(r.get("corr_xs01") or 1.0)
+    distinctness = 1.0 - corr_xs01  # higher is more distinct
+    # marginal verdict
+    verdict = r["marginal"].get("verdict", "")
+    verdict_score = {"ADDS": 3, "NEUTRAL": 1, "DILUTES": 0, "REDUNDANT": 0, "N/A": 0}.get(verdict, 0)
+    return (es, verdict_score, hold_sh, distinctness)
+
+results_sorted = sorted(results, key=score_result, reverse=True)
+best = results_sorted[0]
+
+print("\n" + "=" * 60)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))