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>
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
"""XM06 — 52-day-high proximity (closeness-to-recent-high momentum).
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IDEA: Score = close / rolling_max(high, W) [closeness to recent high].
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Assets near their recent high are "in momentum"; rank them cross-sectionally.
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W in {60, 90}. Causal: rolling_max up through bar i only.
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Grid: 5 calls max
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1. W=60, majors, H=10, k=5, L/S
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2. W=90, majors, H=10, k=5, L/S
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3. W=60, all, H=10, k=5, L/S (best-W on wider universe)
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4. W=60, all, H=5, k=5, L/S (faster rebalance)
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5. W=60, all, H=10, k=7, L/S (wider book)
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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_proximity(P, W):
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"""Causal: close[i] / max(high[i-W+1 .. i]). Higher = closer to recent high = long."""
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n, A = P.close.shape
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out = np.full((n, A), np.nan)
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# rolling max of high, causal window [i-W+1 .. i]
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high_df = __import__("pandas").DataFrame(P.high)
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roll_max = high_df.rolling(W, min_periods=max(2, W // 2)).max().values
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# proximity ratio: close / recent_high (always in (0,1] if no gap-up above window)
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out = P.close / roll_max
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return out
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# ---- run grid ----
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results = []
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# 1. W=60, majors, H=10, k=5, L/S
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rep1 = xs.study_xs("XM06_W60_majors", lambda P: score_proximity(P, 60),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(rep1))
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results.append(rep1)
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# 2. W=90, majors, H=10, k=5, L/S
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rep2 = xs.study_xs("XM06_W90_majors", lambda P: score_proximity(P, 90),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(rep2))
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results.append(rep2)
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# 3. W=60, all, H=10, k=5, L/S
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rep3 = xs.study_xs("XM06_W60_all", lambda P: score_proximity(P, 60),
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universe="all", H=10, k=5, long_short=True)
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print(xs.fmt(rep3))
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results.append(rep3)
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# 4. W=60, all, H=5, k=5, L/S (faster rebalance)
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rep4 = xs.study_xs("XM06_W60_all_H5", lambda P: score_proximity(P, 60),
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universe="all", H=5, k=5, long_short=True)
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print(xs.fmt(rep4))
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results.append(rep4)
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# 5. W=60, all, H=10, k=7, L/S (wider book)
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rep5 = xs.study_xs("XM06_W60_all_k7", lambda P: score_proximity(P, 60),
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universe="all", H=10, k=7, long_short=True)
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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_result(r):
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earns = 1 if r["earns_slot"] else 0
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adds = 1 if r["marginal"].get("verdict") == "ADDS" else 0
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hold_sh = r["holdout"].get("sharpe", -999)
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full_sh = r["full"]["sharpe"]
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corr_xs01 = abs(r.get("corr_xs01") or 1.0)
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distinct = 1 if corr_xs01 < 0.6 else 0
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return (earns, adds, hold_sh, full_sh, distinct)
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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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