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>
95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
"""XVa3 — Price-to-high value (mean reversion from recent highs).
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IDEA: Score = -(close / rolling_max(close, W))
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Long the most beaten-down assets vs their rolling high (W=90).
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Negative sign: lower ratio (more beaten down) -> higher score -> long.
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CAUSAL: rolling_max at row i uses only data[i-W+1 .. i] (pandas rolling handles this).
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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_pth(close, W):
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"""Price-to-high score: -(close / rolling_max(close, W)), causal."""
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import pandas as pd
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df = pd.DataFrame(close)
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roll_max = df.rolling(W, min_periods=W // 2).max().values
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ratio = close / np.where(roll_max > 0, roll_max, np.nan)
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return -ratio # lower ratio (more beaten down) -> higher score -> long
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# --- Grid: 5 backtests total ---
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# Config 1: canonical W=90, H=10, k=5, long-short, all universe
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r1 = xs.study_xs(
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"XVa3-W90-H10-k5-LS-all",
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lambda P: score_pth(P.close, 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(r1))
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print("JSON:", xs.as_json(r1))
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print()
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# Config 2: W=60 (shorter lookback), H=10, k=5, long-short, all universe
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r2 = xs.study_xs(
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"XVa3-W60-H10-k5-LS-all",
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lambda P: score_pth(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(r2))
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print("JSON:", xs.as_json(r2))
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print()
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# Config 3: W=90, H=5 (faster rebalance), k=5, long-short, all
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r3 = xs.study_xs(
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"XVa3-W90-H5-k5-LS-all",
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lambda P: score_pth(P.close, 90),
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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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print()
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# Config 4: W=90, H=10, k=5, majors only (more liquid)
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r4 = xs.study_xs(
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"XVa3-W90-H10-k5-LS-majors",
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lambda P: score_pth(P.close, 90),
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universe="majors", H=10, 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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print()
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# Config 5: W=120 (longer lookback), H=10, k=5, long-short, all
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r5 = xs.study_xs(
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"XVa3-W120-H10-k5-LS-all",
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lambda P: score_pth(P.close, 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(r5))
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print("JSON:", xs.as_json(r5))
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print()
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# --- Pick best config ---
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# Prefer: earns_slot first, then holdout sharpe, then distinctness
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results = [r1, r2, r3, r4, r5]
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earns = [r for r in results if r["earns_slot"]]
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if earns:
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best = max(earns, key=lambda r: r["holdout"].get("sharpe", -999))
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else:
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# Fall back to positive full+hold, distinct from XS01
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candidates = [r for r in results
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if r["full"]["sharpe"] > 0
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and r["holdout"].get("sharpe", 0) > 0
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and (r["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(results, key=lambda r: r["holdout"].get("sharpe", -999))
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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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