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>
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
"""XM02 — Multi-L z-blend momentum
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Score = mean of xs_zscore(past_return(close, L)) over a set of lookback windows L.
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Compare two window sets: {30,90} (XS01-like) vs {20,60,120} (extended).
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Grid: 5 study_xs calls total — vary universe / windows / H.
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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 helpers ─────────────────────────────────────────────────────────────
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def blend_mom(close, lookbacks):
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"""Mean of xs_zscore(past_return(close, L)) for each L in lookbacks."""
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scores = [xs.xs_zscore(xs.past_return(close, L)) for L in lookbacks]
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stacked = np.stack(scores, axis=2) # (n_days, n_assets, n_L)
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return np.nanmean(stacked, axis=2) # (n_days, n_assets)
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L_SHORT = [30, 90] # mirrors XS01 blend
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L_LONG = [20, 60, 120] # extended set
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L_WIDE = [20, 60, 90, 120] # even wider blend
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# ── 5 backtests ───────────────────────────────────────────────────────────────
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results = []
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# 1. XS01-equivalent blend {30,90} on ALL universe — baseline reference
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rep1 = xs.study_xs(
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"XM02-3090-all",
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lambda P: blend_mom(P.close, [30, 90]),
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universe="all", H=10, k=5, long_short=True,
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)
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print(xs.fmt(rep1))
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results.append(rep1)
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# 2. Extended blend {20,60,120} on ALL universe
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rep2 = xs.study_xs(
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"XM02-206012-all",
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lambda P: blend_mom(P.close, [20, 60, 120]),
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universe="all", H=10, k=5, long_short=True,
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)
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print(xs.fmt(rep2))
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results.append(rep2)
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# 3. Extended blend {20,60,120} on MAJORS (19 alts — XS01 universe)
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rep3 = xs.study_xs(
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"XM02-206012-majors",
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lambda P: blend_mom(P.close, [20, 60, 120]),
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universe="majors", H=10, k=5, long_short=True,
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)
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print(xs.fmt(rep3))
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results.append(rep3)
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# 4. Wide blend {20,60,90,120} on ALL, shorter rebalance H=5
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rep4 = xs.study_xs(
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"XM02-wide-H5-all",
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lambda P: blend_mom(P.close, [20, 60, 90, 120]),
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universe="all", H=5, k=5, long_short=True,
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)
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print(xs.fmt(rep4))
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results.append(rep4)
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# 5. Wide blend on ALL, longer H=20 (less turnover)
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rep5 = xs.study_xs(
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"XM02-wide-H20-all",
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lambda P: blend_mom(P.close, [20, 60, 90, 120]),
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universe="all", H=20, k=5, long_short=True,
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)
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print(xs.fmt(rep5))
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results.append(rep5)
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# ── pick BEST by: earns_slot > hold-out sharpe > distinctness ────────────────
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def _score(r):
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earns = 1 if r["earns_slot"] else 0
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verdict = 1 if r["marginal"].get("verdict") == "ADDS" else 0
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hold_sh = r["holdout"].get("sharpe", -99)
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full_sh = r["full"]["sharpe"]
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dist = 1 if (r["corr_xs01"] or 1.0) < 0.6 else 0
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return (earns, verdict, hold_sh, full_sh, dist)
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best = max(results, key=_score)
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print("\n" + "=" * 60)
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print("BEST CONFIG:")
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print(xs.fmt(best))
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print("JSON:", xs.as_json(best))
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