Adriano Dal Pastro
9612560479
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>
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
"""XVa1 — Distance-from-MA value signal.
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Score = -(close / roll_mean(close, W) - 1)
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Long assets furthest BELOW their rolling MA (cheap / mean-reverting).
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Short assets furthest ABOVE their rolling MA (expensive).
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Grid: W in {60, 100}, universe all/majors, H in {10, 20}.
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Max 5 study_xs calls.
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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 score_val(close, W):
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"""Causal value score: -(close / MA - 1). Higher = more below MA = long."""
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ma = xs.roll_mean(close, W)
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return -(close / ma - 1.0)
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results = []
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# Config 1: W=60, all assets, H=10, k=5, LS
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r1 = xs.study_xs(
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"XVa1-W60-all-H10",
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lambda P: score_val(P.close, 60),
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universe="all", H=10, k=5, 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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# Config 2: W=100, all assets, H=10, k=5, LS
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r2 = xs.study_xs(
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"XVa1-W100-all-H10",
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lambda P: score_val(P.close, 100),
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universe="all", H=10, 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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# Config 3: W=60, majors, H=10, k=5, LS
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r3 = xs.study_xs(
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"XVa1-W60-majors-H10",
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lambda P: score_val(P.close, 60),
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universe="majors", H=10, 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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# Config 4: W=60, all assets, H=20, k=5, LS (slower rebal)
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r4 = xs.study_xs(
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"XVa1-W60-all-H20",
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lambda P: score_val(P.close, 60),
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universe="all", H=20, k=5, 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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# Config 5: W=100, all assets, H=20, k=5, LS
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r5 = xs.study_xs(
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"XVa1-W100-all-H20",
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lambda P: score_val(P.close, 100),
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universe="all", H=20, k=5, 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 full Sharpe
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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", -99)
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full_sh = r["full"].get("sharpe", -99)
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return (earns, hold_sh, full_sh)
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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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