Adriano Dal Pastro
0adc69a357
18 agenti su book market-neutral a 2 gambe BTC/ETH (eseguibili a $600, a differenza di XS01), giudicati sul MARGINALE vs TP01 (altlib.marginal_vs_tp01), non sullo Sharpe assoluto. Lab: ortholib.py (eval_book leak-free a 2 gambe + causalità + eseguibilità@600), ortho_score.py (giudice), meta_ortho.py (corr mutua + persistenza multi-cut), sleeve_rv.py (curated, SELECTION- BIASED, non deployare). Esito: 17/18 "ADDS" -> gonfiato dall'hold-out corto fisso-2025 (finestra ETH-bleed dove TP01 è debole). Diagnosi orchestratore: collassano a 8 bet (corr 0.43); persistenza multi-cut e selezione walk-forward smascherano i 2025-only (kalman/xs2). Scettico indipendente: basket selection-free ha uplift pre-2025 +0.027 = 49° percentile di asset-rumore corr-zero (matematica di diversificazione, non segnale); corr(Sharpe-TP01, uplift) -0.87 (è un HEDGE dei drawdown di TP01); muore a 0.30% RT. Verdetto: NIENTE in live. Resta solo TP01. Lezione: lo scorer marginale va indurito (multi-cut + null-asset-rumore + distinguere hedge da alpha). Diario 2026-06-21-ortho-tp01-relative-value.md. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
"""sleeve_rv — CURATED relative-value sleeve: equal-weight of the 4 ortho books whose
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marginal uplift to TP01 is POSITIVE AT EVERY hold-out cut (2022/23/24/25), i.e. persistent
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rather than the 2025 ETH/BTC-bleed artifact. Two price-based + two implied-vol-based, so
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the legs are mechanism-diverse (mutual corr < 0.6).
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This is ONE executable 2-leg BTC/ETH book (the averaged legs stay within the $300/asset
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cap because each sub-book is capped 0.5/leg and the mean of capped weights is capped).
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book(btc, eth) -> (w_btc, w_eth)
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Use it as a small market-neutral satellite alongside TP01 (forward-monitor first).
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"""
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from __future__ import annotations
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import importlib.util
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import sys
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from pathlib import Path
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import numpy as np
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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# the persistent-at-every-cut representatives (mechanism-diverse)
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MEMBERS = [
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"agent_14_dvol_spread",
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"agent_04_ratio_donchian",
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"agent_03_relstrength_gated",
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"agent_15_vol_premium_tilt",
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]
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def _load(name: str):
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p = HERE / "agents" / f"{name}.py"
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spec = importlib.util.spec_from_file_location(name, p)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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return mod.book
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_BOOKS = [_load(n) for n in MEMBERS]
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def book(btc, eth):
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wbs, wes = [], []
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for b in _BOOKS:
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wb, we = b(btc, eth)
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wbs.append(np.nan_to_num(np.asarray(wb, float)))
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wes.append(np.nan_to_num(np.asarray(we, float)))
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wb = np.mean(wbs, axis=0)
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we = np.mean(wes, axis=0)
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return np.clip(wb, -0.5, 0.5), np.clip(we, -0.5, 0.5)
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