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>
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Adriano Dal Pastro
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"""XL04 [LIQ] — Dollar-volume momentum.
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Score = past_return of dollar-volume (close * volume) over W=30 days.
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Idea: assets gaining LIQUIDITY / ATTENTION relative to peers will outperform.
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This is the OPPOSITE of XL03 (which went long LOW dollar-volume names).
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Mechanism:
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dvol[i] = close[i] * vol[i] (daily dollar volume)
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score[i] = dvol[i] / dvol[i-W] - 1 (W-day return of dollar volume)
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-> long assets whose dollar volume is GROWING the fastest
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Grid (<=5 runs):
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1. baseline: universe=all, H=10, k=5, long_short=True, W=30
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2. shorter window W=10 (faster attention signal)
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3. longer window W=60 (more stable)
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4. majors universe (19 XS01 assets — check distinctness from XS01)
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5. long-only version (long attention gainers, no shorting attention losers)
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
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import xslib as xs
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import numpy as np
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# --- score factory -----------------------------------------------------------
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def dvol_momentum_score(P, W=30):
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"""Score = W-day past return of dollar volume (close * volume).
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CAUSAL: dvol_return[i] uses dvol[i] / dvol[i-W] - 1.
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Higher score = dollar volume growing faster = LONG.
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"""
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dvol = P.close * P.vol # (n, A) daily dollar volume
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score = np.full_like(dvol, np.nan)
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# past_return style: score[i] = dvol[i] / dvol[i-W] - 1
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# guard: if dvol[i-W] == 0 -> NaN
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denom = dvol[:-W] # dvol[i-W]
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numer = dvol[W:] # dvol[i]
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with np.errstate(invalid="ignore", divide="ignore"):
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ratio = np.where(denom > 0, numer / denom - 1.0, np.nan)
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score[W:] = ratio
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return score
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# --- grid -------------------------------------------------------------------
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print("=" * 70)
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print("XL04 [LIQ] Dollar-volume momentum — grid search")
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print("=" * 70)
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results = []
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# Run 1: baseline (all, H=10, k=5, LS, W=30)
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r1 = xs.study_xs("XL04-W30-all-LS",
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lambda P: dvol_momentum_score(P, 30),
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universe="all", H=10, k=5, long_short=True)
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print(xs.fmt(r1))
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print("JSON:", xs.as_json(r1))
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results.append(r1)
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# Run 2: shorter window W=10 (faster attention surge)
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r2 = xs.study_xs("XL04-W10-all-LS",
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lambda P: dvol_momentum_score(P, 10),
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universe="all", H=10, k=5, long_short=True)
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print(xs.fmt(r2))
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print("JSON:", xs.as_json(r2))
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results.append(r2)
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# Run 3: longer window W=60 (sustained attention)
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r3 = xs.study_xs("XL04-W60-all-LS",
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lambda P: dvol_momentum_score(P, 60),
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universe="all", H=10, k=5, long_short=True)
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print(xs.fmt(r3))
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print("JSON:", xs.as_json(r3))
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results.append(r3)
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# Run 4: majors universe only (19 XS01 assets)
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r4 = xs.study_xs("XL04-W30-majors-LS",
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lambda P: dvol_momentum_score(P, 30),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(r4))
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print("JSON:", xs.as_json(r4))
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results.append(r4)
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# Run 5: long-only (attention gainers only, no shorting losers)
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r5 = xs.study_xs("XL04-W30-all-LO",
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lambda P: dvol_momentum_score(P, 30),
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universe="all", H=10, k=5, long_short=False)
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print(xs.fmt(r5))
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print("JSON:", xs.as_json(r5))
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results.append(r5)
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# --- pick best config -------------------------------------------------------
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print("\n" + "=" * 70)
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print("SUMMARY — best by: earns_slot first, then hold-out Sharpe, then distinctness from XS01")
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print("=" * 70)
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def rank_key(r):
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earns = int(r["earns_slot"])
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hold_sh = r["holdout"].get("sharpe", -9) or -9
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xs01_corr = abs(r.get("corr_xs01") or 1.0)
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full_sh = r["full"].get("sharpe", -9) or -9
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return (earns, hold_sh, full_sh, -xs01_corr)
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best = max(results, key=rank_key)
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print(f"\nBEST CONFIG: {best['name']}")
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print(xs.fmt(best))
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print("\nJSON (best):", xs.as_json(best))
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