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>
107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
"""meta_ortho — the orchestrator's decisive read on the ortho fleet.
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17/18 books "earn a slot". That cannot be 17 alphas. This asks the three questions that
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decide whether ANY of it is deployable:
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1. ARE THEY ONE BET? Mutual correlation of the books' daily returns. Relative-momentum
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variants will cluster ~1; we collapse them to de-correlated representatives.
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2. IS THE UPLIFT PERSISTENT OR JUST THE 2025-26 WINDOW? altlib.marginal_vs_tp01 fixes
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the hold-out at 2025-01-01 — exactly the window where ETH bled vs BTC and TP01 was
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weak. We re-measure the blend uplift at SEVERAL cut dates (2022/2023/2024/2025). A
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real orthogonal premium adds at every cut; a regime artifact only adds at 2025.
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3. WHAT DOES A SINGLE COMBINED SLEEVE LOOK LIKE? Equal-weight the representatives into
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one relative-value sleeve and score THAT vs TP01 (full + per-cut).
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uv run python scripts/research/ortho/meta_ortho.py
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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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import pandas as pd
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import ortholib as ol # noqa: E402
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import altlib as al # noqa: E402
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AGENTS = HERE / "agents"
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CUTS = ["2022-01-01", "2023-01-01", "2024-01-01", "2025-01-01"]
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def _book(path: Path):
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spec = importlib.util.spec_from_file_location(path.stem, path)
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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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def _sh(s: pd.Series) -> float:
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r = np.asarray(s.dropna().values, float)
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return float(np.mean(r) / np.std(r) * np.sqrt(365.25)) if len(r) > 2 and np.std(r) > 0 else 0.0
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def uplift_at(cand: pd.Series, B: pd.Series, cut: str, w: float = 0.25) -> float:
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J = pd.concat({"B": B, "C": cand}, axis=1, join="inner").dropna()
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J = J[J.index >= pd.Timestamp(cut, tz="UTC")]
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if len(J) < 30:
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return float("nan")
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return _sh((1 - w) * J["B"] + w * J["C"]) - _sh(J["B"])
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def main():
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B = al.tp01_baseline_daily()
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dailies = {}
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for p in sorted(AGENTS.glob("agent_*.py")):
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try:
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ev = ol.eval_book(_book(p))
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d = ev["daily"]
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if d.std() > 0:
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dailies[p.stem.replace("agent_", "")] = d
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except Exception as e:
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print(f" skip {p.stem}: {e}")
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names = list(dailies)
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M = pd.concat(dailies, axis=1, join="inner").dropna()
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C = M.corr()
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# greedy de-correlation: order by full-sample uplift, keep if corr<0.6 to all kept
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upf = {n: uplift_at(dailies[n], B, "2018-01-01") for n in names}
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order = sorted(names, key=lambda n: upf[n], reverse=True)
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reps = []
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for n in order:
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if all(abs(C.loc[n, r]) < 0.6 for r in reps):
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reps.append(n)
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print(f"\n ORTHO META — {len(names)} books, mutual-corr clusters -> {len(reps)} de-correlated reps")
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print(f" mean |corr| among all books = {C.abs().values[np.triu_indices(len(names),1)].mean():.2f}")
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print(f"\n DE-CORRELATED REPRESENTATIVES (corr<0.6 to each other):")
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print(f" {'book':<20}{'up_full':>8} uplift at cut: " + " ".join(c[:7] for c in CUTS))
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for n in reps:
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ups = [uplift_at(dailies[n], B, c) for c in CUTS]
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print(f" {n:<20}{upf[n]:>8.3f} " + " ".join(f"{u:>+6.2f}" for u in ups))
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# combined sleeve = equal-weight of representatives
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combo = M[reps].mean(axis=1)
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print(f"\n COMBINED relative-value sleeve (equal-weight of {len(reps)} reps):")
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print(f" {'':<20}{'up_full':>8} uplift at cut: " + " ".join(c[:7] for c in CUTS))
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ups = [uplift_at(combo, B, c) for c in CUTS]
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print(f" {'COMBO':<20}{uplift_at(combo,B,'2018-01-01'):>8.3f} " + " ".join(f"{u:>+6.2f}" for u in ups))
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print(f" combo standalone: Sharpe {_sh(combo):.2f} corr->TP01 {M[reps].mean(axis=1).corr(pd.concat({'b':B},axis=1).reindex(combo.index)['b']):.2f}")
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# also: ALL ADDS equal-weight (what 'just deploy everything' would be)
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allc = M.mean(axis=1)
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ups = [uplift_at(allc, B, c) for c in CUTS]
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print(f"\n ALL-{len(names)} equal-weight sleeve:")
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print(f" {'ALL':<20}{uplift_at(allc,B,'2018-01-01'):>8.3f} " + " ".join(f"{u:>+6.2f}" for u in ups))
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print("\n READ: a column that is +ve at 2022/2023/2024 cuts (not only 2025) = persistent.")
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print(" all-positive-only-at-2025 = the ETH/BTC-bleed regime, not a standing premium.")
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if __name__ == "__main__":
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main()
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