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>
90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
"""XV06 — Low Vol-of-Vol (stability of volatility)
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Score = -roll_std(roll_std(ret, inner_win), outer_win)
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Idea: assets whose volatility is most STABLE (predictable) are preferred long;
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assets with high vol-of-vol (erratic/spiky volatility) are shorted.
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Lower vol-of-vol = higher score = long bias.
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Canonical: inner_win=10, outer_win=30.
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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_xv06(P, inner=10, outer=30):
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"""Score = -roll_std(roll_std(ret, inner), outer).
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Causal: each row uses only past data (rolling windows, no future leakage).
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Higher score = lower vol-of-vol = more stable volatility = preferred long.
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"""
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# inner rolling std: daily vol estimate
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inner_vol = xs.roll_std(P.ret, inner)
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# outer rolling std of that vol: vol-of-vol
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vov = xs.roll_std(inner_vol, outer)
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# negate: lower vov = higher score = long
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return -vov
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# --- Grid: 5 studies max ---
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# 1) Canonical: inner=10, outer=30, all universe, H=10, k=5, L/S
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rep1 = xs.study_xs(
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"XV06-i10-o30-H10-k5-LS",
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lambda P: score_xv06(P, inner=10, outer=30),
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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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print("JSON:", xs.as_json(rep1))
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# 2) Majors only (19 assets, better liquidity)
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rep2 = xs.study_xs(
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"XV06-i10-o30-H10-k5-LS-majors",
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lambda P: score_xv06(P, inner=10, outer=30),
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universe="majors", H=10, k=5, long_short=True
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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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# 3) Wider outer window: inner=10, outer=60
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rep3 = xs.study_xs(
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"XV06-i10-o60-H10-k5-LS",
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lambda P: score_xv06(P, inner=10, outer=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(rep3))
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print("JSON:", xs.as_json(rep3))
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# 4) Faster rebalance H=5
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rep4 = xs.study_xs(
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"XV06-i10-o30-H5-k5-LS",
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lambda P: score_xv06(P, inner=10, outer=30),
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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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print("JSON:", xs.as_json(rep4))
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# 5) More concentrated k=3
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rep5 = xs.study_xs(
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"XV06-i10-o30-H10-k3-LS",
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lambda P: score_xv06(P, inner=10, outer=30),
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universe="all", H=10, k=3, long_short=True
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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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# Pick best by: earns_slot first, then holdout Sharpe, then distinctness
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reps = [rep1, rep2, rep3, rep4, rep5]
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earners = [r for r in reps if r["earns_slot"]]
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if earners:
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best = max(earners, key=lambda r: r["holdout"].get("sharpe", -999))
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else:
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# fallback: positive full + hold-out + corr_xs01 < 0.6
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candidates = [r for r in reps if r["full"]["sharpe"] > 0 and r["holdout"].get("sharpe", 0) > 0
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and (r.get("corr_xs01") or 1.0) < 0.6]
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if candidates:
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best = max(candidates, key=lambda r: r["holdout"].get("sharpe", -999))
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else:
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best = max(reps, key=lambda r: r["full"]["sharpe"])
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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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