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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"""XM08 — Momentum Consistency (Frog-in-Pan)
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Score = past_return(close, L) * fraction_of_up_days(ret, L)
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Smooth momentum beats jumpy. "Frog-in-pan" from Ang, Goetzmann, Schaefer (2012):
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consistent trends accumulating through many small daily gains dominate short sharp jumps.
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Score is higher (more long) when returns over L days are both large AND consistent.
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Grid: L=60 fixed (canonical), vary universe / H / k / long_short (<=5 calls total).
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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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# ---------------------------------------------------------------------------
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# SCORE: causal frog-in-pan
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# ---------------------------------------------------------------------------
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def fip_score(P, L=60):
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"""
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score[i, a] = past_return(close[i], L) * frac_up_days(ret[i-L+1..i], L)
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Causal: only uses ret and close up to row i.
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"""
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close = P.close # (n, A)
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ret = P.ret # (n, A) simple daily returns
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n, A = close.shape
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# past return over L days (causal)
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pr = xs.past_return(close, L) # (n, A), nan for i < L
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# fraction of positive days over rolling window L
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pos = (ret > 0).astype(float) # 1 if up day
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frac_up = xs.roll_mean(pos, L) # causal rolling mean -> (n, A)
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score = pr * frac_up
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return score
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# ---------------------------------------------------------------------------
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# GRID (<=5 calls)
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# ---------------------------------------------------------------------------
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results = []
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# 1. Base: majors, L=60, H=10, k=5, long_short
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rep1 = xs.study_xs(
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"XM08_majors_H10_k5_ls",
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lambda P: fip_score(P, 60),
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universe="majors", H=10, k=5, long_short=True, target_vol=0.20
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)
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print(xs.fmt(rep1))
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print("JSON:", xs.as_json(rep1))
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results.append(rep1)
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# 2. All assets, L=60, H=10, k=5, long_short
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rep2 = xs.study_xs(
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"XM08_all_H10_k5_ls",
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lambda P: fip_score(P, 60),
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universe="all", H=10, k=5, long_short=True, target_vol=0.20
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)
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print(xs.fmt(rep2))
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print("JSON:", xs.as_json(rep2))
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results.append(rep2)
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# 3. All assets, H=20, k=5, long_short (slower rebal)
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rep3 = xs.study_xs(
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"XM08_all_H20_k5_ls",
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lambda P: fip_score(P, 60),
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universe="all", H=20, k=5, long_short=True, target_vol=0.20
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)
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print(xs.fmt(rep3))
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print("JSON:", xs.as_json(rep3))
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results.append(rep3)
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# 4. Majors, H=10, k=5, long-only
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rep4 = xs.study_xs(
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"XM08_majors_H10_k5_lo",
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lambda P: fip_score(P, 60),
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universe="majors", H=10, k=5, long_short=False, target_vol=0.20
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)
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print(xs.fmt(rep4))
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print("JSON:", xs.as_json(rep4))
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results.append(rep4)
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# 5. All assets, H=10, k=7, long_short (wider top/bottom bucket)
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rep5 = xs.study_xs(
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"XM08_all_H10_k7_ls",
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lambda P: fip_score(P, 60),
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universe="all", H=10, k=7, long_short=True, target_vol=0.20
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)
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print(xs.fmt(rep5))
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print("JSON:", xs.as_json(rep5))
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results.append(rep5)
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# ---------------------------------------------------------------------------
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# PICK BEST
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# ---------------------------------------------------------------------------
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def score_result(r):
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"""Prefer earns_slot, then hold-out sharpe, then distinctness."""
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earns = r.get("earns_slot", False)
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ho = r.get("holdout", {}).get("sharpe", -999)
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corr = abs(r.get("corr_xs01", 1.0))
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return (int(earns), ho, -corr)
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best = max(results, key=score_result)
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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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