research(ortho): caccia all'ortogonale a TP01 — relative-value BTC/ETH reale ma NON deployabile (hedge mono-regime)

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>
2026-06-21 12:35:48 +00:00
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+"""Is the basket uplift just 'helps when TP01 is weak'? Quantify regime-conditionality."""
+from __future__ import annotations
+import importlib.util, sys
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+sys.path.insert(0, str(HERE))
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import ortholib as ol
+import altlib as al
+AGENTS = HERE / "agents"
+
+
+def _book(path):
+    spec = importlib.util.spec_from_file_location(path.stem, path)
+    mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
+    return mod.book
+
+
+def _sh(s):
+    r = np.asarray(pd.Series(s).dropna().values, float)
+    return float(np.mean(r) / np.std(r) * np.sqrt(365.25)) if len(r) > 2 and np.std(r) > 0 else 0.0
+
+
+def main():
+    B = al.tp01_baseline_daily()
+    excl = ["12_rebalance_harvest"]
+    rows = {}
+    for p in sorted(AGENTS.glob("agent_*.py")):
+        nm = p.stem.replace("agent_", "")
+        if nm in excl: continue
+        ev = ol.eval_book(_book(p))
+        if ev["daily"].std() > 0:
+            rows[nm] = ev["daily"]
+    M = pd.concat(rows, axis=1, join="inner").dropna()
+    basket = M.mean(axis=1)
+    J = pd.concat({"B": B, "C": basket}, axis=1, join="inner").dropna()
+
+    print("Per-year: TP01 own Sharpe  | basket Sharpe | basket ret | blend-uplift")
+    print(" (does the basket help SPECIFICALLY in TP01's weak years?)")
+    for y in sorted(set(J.index.year)):
+        sub = J[J.index.year == y]
+        if len(sub) < 20: continue
+        tp_sh = _sh(sub["B"]); bk_sh = _sh(sub["C"])
+        bk_ret = float((1 + sub["C"]).prod() - 1)
+        up = _sh(0.75 * sub["B"] + 0.25 * sub["C"]) - _sh(sub["B"])
+        print(f"  {y}: TP01 Sh {tp_sh:+6.2f} | basket Sh {bk_sh:+6.2f} | ret {bk_ret:+7.2%} | uplift {up:+.3f}")
+
+    # correlation: does basket-year-uplift track NEGATIVE TP01-year-Sharpe?
+    rows2 = []
+    for y in sorted(set(J.index.year)):
+        sub = J[J.index.year == y]
+        if len(sub) < 20: continue
+        rows2.append((_sh(sub["B"]), _sh(0.75*sub["B"]+0.25*sub["C"]) - _sh(sub["B"])))
+    arr = np.array(rows2)
+    if len(arr) > 2:
+        c = np.corrcoef(arr[:,0], arr[:,1])[0,1]
+        print(f"\n  corr(TP01 yearly Sharpe, basket yearly uplift) = {c:+.2f}")
+        print("  (strongly NEGATIVE => basket only helps when TP01 is weak = regime hedge, not standing alpha)")
+
+    # conditional uplift: split days by whether TP01 trailing-60d return is up or down
+    tp_trail = J["B"].rolling(60).sum()
+    up_days = J[tp_trail > 0]; dn_days = J[tp_trail <= 0]
+    for lbl, d in [("TP01 trailing-60d UP", up_days), ("TP01 trailing-60d DOWN", dn_days)]:
+        if len(d) < 30: continue
+        u = _sh(0.75*d["B"]+0.25*d["C"]) - _sh(d["B"])
+        print(f"  [{lbl}] n={len(d)}  basket Sh {_sh(d['C']):+.2f}  blend-uplift {u:+.3f}")
+
+    # ETH/BTC ratio regime: is the basket net-short ETH (i.e. is it just shorting the bleed)?
+    btc, eth = ol.aligned()
+    wbs, wes = [], []
+    for nm in rows:
+        wb, we = _book(AGENTS / f"agent_{nm}.py")(btc, eth)
+        wbs.append(np.nan_to_num(np.asarray(wb,float))); wes.append(np.nan_to_num(np.asarray(we,float)))
+    wb = np.clip(np.mean(wbs,axis=0),-0.5,0.5); we = np.clip(np.mean(wes,axis=0),-0.5,0.5)
+    idx = pd.DatetimeIndex(btc["dt"])
+    wbS = pd.Series(wb, index=idx); weS = pd.Series(we, index=idx)
+    print("\n  Mean basket leg weights per year (is it structurally short ETH / long BTC?):")
+    print("    year   w_btc    w_eth   (positive=long)")
+    for y in sorted(set(idx.year)):
+        m = idx.year == y
+        print(f"    {y}:  {wbS[m].mean():+.3f}  {weS[m].mean():+.3f}")
+    print(f"\n  FULL mean: w_btc {wbS.mean():+.3f}  w_eth {weS.mean():+.3f}")
+    print("  (a persistent long-BTC/short-ETH tilt = it's a static ETH-bleed short, not RV alpha)")
+
+
+if __name__ == "__main__":
+    main()