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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"""XR04 — Volume-shock reversal.
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IDEA: Long recent losers that ALSO had a volume spike.
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score = -past_return(L) * (volume_z > 1)
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The intuition: large volume + price drop signals capitulation/panic selling.
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The oversold name with elevated volume is more likely to bounce vs a name
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that drifted down quietly. L=3 is the suggested lookback.
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Grid (<=5 calls):
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1. majors H=5 k=3 LS=True L=3 (baseline config)
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2. majors H=5 k=5 LS=True L=3 (more positions)
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3. all H=5 k=5 LS=True L=3 (wider universe)
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4. majors H=3 k=3 LS=True L=5 (slightly longer reversal)
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5. majors H=5 k=3 LS=True L=3 with volume_z threshold = 0.5 (lower bar)
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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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def vol_shock_reversal_score(P, L=3, vz_thresh=1.0):
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"""score = -past_return(L) when volume_z > vz_thresh, else 0.
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Higher score = more reversal candidate with volume spike.
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Causally computed: all data at row i uses data <=i."""
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ret_L = xs.past_return(P.close, L) # (n, A)
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vz = xs.volume_z(P.vol, 20) # rolling 20d volume z-score
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# Only count the reversal signal when volume is elevated
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mask = vz > vz_thresh # bool (n, A)
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score = np.where(mask, -ret_L, 0.0)
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# Where no data (NaN), set to NaN so harness skips
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score = np.where(np.isfinite(ret_L) & np.isfinite(vz), score, np.nan)
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return score
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results = []
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# 1. Baseline: majors, H=5, k=3, L=3, vz_thresh=1.0
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r1 = xs.study_xs(
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"XR04-majors-H5k3-L3",
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lambda P: vol_shock_reversal_score(P, L=3, vz_thresh=1.0),
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universe="majors", H=5, k=3, long_short=True
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)
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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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# 2. More positions: majors, H=5, k=5, L=3
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r2 = xs.study_xs(
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"XR04-majors-H5k5-L3",
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lambda P: vol_shock_reversal_score(P, L=3, vz_thresh=1.0),
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universe="majors", H=5, k=5, long_short=True
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)
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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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# 3. Wider universe: all, H=5, k=5, L=3
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r3 = xs.study_xs(
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"XR04-all-H5k5-L3",
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lambda P: vol_shock_reversal_score(P, L=3, vz_thresh=1.0),
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universe="all", H=5, k=5, long_short=True
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)
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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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# 4. Slightly longer reversal window: majors, H=3, k=3, L=5
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r4 = xs.study_xs(
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"XR04-majors-H3k3-L5",
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lambda P: vol_shock_reversal_score(P, L=5, vz_thresh=1.0),
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universe="majors", H=3, k=3, long_short=True
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)
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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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# 5. Lower volume threshold: majors, H=5, k=3, L=3, vz_thresh=0.5
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r5 = xs.study_xs(
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"XR04-majors-H5k3-L3-vz05",
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lambda P: vol_shock_reversal_score(P, L=3, vz_thresh=0.5),
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universe="majors", H=5, k=3, long_short=True
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)
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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 by: earns_slot first, then hold-out sharpe, then distinctness
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def rank_key(r):
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earns = int(r.get("earns_slot", False))
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h_sh = r["holdout"].get("sharpe", -99)
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f_sh = r["full"].get("sharpe", -99)
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corr_xs01 = r.get("corr_xs01") or 1.0
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distinct = 1 if corr_xs01 < 0.6 else 0
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return (earns, h_sh, f_sh, distinct)
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best = max(results, key=rank_key)
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print("\n=== BEST CONFIG ===")
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print(xs.fmt(best))
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print("JSON:", xs.as_json(best))
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