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/XL02.py

@@ -0,0 +1,118 @@
+"""XL02 [LIQ] — Volume-trend momentum
+IDEA: Score = volume_z(vol, 30) combined with positive return (rising-volume winners).
+Assets with above-average volume AND positive momentum rank highest.
+Assets with above-average volume AND negative momentum rank lowest (i.e., short).
+
+Mechanism intuition:
+  - Volume surge signals conviction / participation.
+  - When paired with rising price (trend direction) it confirms breakout.
+  - When paired with falling price it confirms distribution / breakdown.
+  - Pure volume without price direction is ambiguous (could be capitulation or breakout).
+
+Score variants explored (<=5 total):
+  1. vol_z(30) * ret(10)   -- product: vol-amplified short-term return
+  2. vol_z(30) * ret(30)   -- product: vol-amplified medium return
+  3. blend:  0.5*xs_z(vol_z*ret10) + 0.5*xs_z(ret30)  -- add momentum anchor
+  4. Same blend but long-only (avoid short vol-breakdown which may just be panic)
+  5. vol_z(60) * ret(20)   -- wider lookback, majors universe
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+# ── Score factory ────────────────────────────────────────────────────────────
+
+def score_vol_trend(P, vol_win=30, ret_win=10):
+    """product: volume_z * past_return — higher = rising on high volume"""
+    vz = xs.volume_z(P.vol, vol_win)        # (n, A) causal
+    rr = xs.past_return(P.close, ret_win)   # (n, A) causal
+    score = vz * rr
+    return score
+
+
+def score_vol_trend_blend(P, vol_win=30, ret_win_short=10, ret_win_long=30, w_blend=0.5):
+    """Blend vol*ret with standalone momentum to add a stable anchor"""
+    vz = xs.volume_z(P.vol, vol_win)
+    rr_short = xs.past_return(P.close, ret_win_short)
+    rr_long  = xs.past_return(P.close, ret_win_long)
+    signal1 = xs.xs_zscore(vz * rr_short)
+    signal2 = xs.xs_zscore(rr_long)
+    return w_blend * signal1 + (1 - w_blend) * signal2
+
+
+# ── Grid (5 calls) ───────────────────────────────────────────────────────────
+
+results = []
+
+# 1. vol_z(30) * ret(10)  — LS, all universe
+r1 = xs.study_xs(
+    "XL02-vz30r10",
+    lambda P: score_vol_trend(P, vol_win=30, ret_win=10),
+    universe="all", H=10, k=5, long_short=True,
+)
+results.append(r1)
+print(xs.fmt(r1))
+print("JSON:", xs.as_json(r1))
+print()
+
+# 2. vol_z(30) * ret(30)  — LS, all universe
+r2 = xs.study_xs(
+    "XL02-vz30r30",
+    lambda P: score_vol_trend(P, vol_win=30, ret_win=30),
+    universe="all", H=10, k=5, long_short=True,
+)
+results.append(r2)
+print(xs.fmt(r2))
+print("JSON:", xs.as_json(r2))
+print()
+
+# 3. blend: vol*ret(10) + mom(30) — LS, all universe
+r3 = xs.study_xs(
+    "XL02-blend",
+    lambda P: score_vol_trend_blend(P, vol_win=30, ret_win_short=10, ret_win_long=30, w_blend=0.5),
+    universe="all", H=10, k=5, long_short=True,
+)
+results.append(r3)
+print(xs.fmt(r3))
+print("JSON:", xs.as_json(r3))
+print()
+
+# 4. blend long-only (avoid shorting high-vol breakdowns)
+r4 = xs.study_xs(
+    "XL02-blend-LO",
+    lambda P: score_vol_trend_blend(P, vol_win=30, ret_win_short=10, ret_win_long=30, w_blend=0.5),
+    universe="all", H=10, k=5, long_short=False,
+)
+results.append(r4)
+print(xs.fmt(r4))
+print("JSON:", xs.as_json(r4))
+print()
+
+# 5. vol_z(60) * ret(20) — majors universe, tighter
+r5 = xs.study_xs(
+    "XL02-vz60r20-maj",
+    lambda P: score_vol_trend(P, vol_win=60, ret_win=20),
+    universe="majors", H=10, k=5, long_short=True,
+)
+results.append(r5)
+print(xs.fmt(r5))
+print("JSON:", xs.as_json(r5))
+print()
+
+# ── Pick best ────────────────────────────────────────────────────────────────
+def score_result(r):
+    """Higher is better: prefer earns_slot, then hold-out, then full."""
+    m = r["marginal"]
+    earns = int(r["earns_slot"]) * 10
+    hold_sh = r["holdout"].get("sharpe", -99)
+    full_sh = r["full"]["sharpe"]
+    distinct = 1 if (r["corr_xs01"] or 1.0) < 0.6 else 0
+    return earns + distinct + hold_sh + 0.3 * full_sh
+
+best = max(results, key=score_result)
+print("=" * 60)
+print(f"BEST CONFIG: {best['name']}")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))