feat(portfolio): XS01 cross-sectional (Hyperliquid) BATTE il portafoglio -> TP01 70% + XS01 30%

Espansione universo (su input utente "storico da cerbero"): il Cerbero MCP col token MAINNET serve Hyperliquid (230 perp REALI, storia nativa dal 2024). fetch_hyperliquid.py certifica 19 alt liquidi a 1d (flat 0%, cross-venue 4-9 bps vs Binance) -> data/raw/hl_*_1d.parquet. Abilita le strategie CROSS-SECTIONAL (impossibili a 2 asset). XS01 = cross-sectional momentum market-neutral (long 5 forti / short 5 deboli su ret 30g, ogni 10g, vol-target 20%). Validato onesto: plateau (config/k/subset), fee-robusto (0.3% RT), scorrelato a TP01 (-0.06), positivo OGNI anno 2024-26, meccanismo complementare (lavora nella dispersione quando TP01 e' in cash). Diverso dal regime-luck RV bocciato (19 asset, plateau, ogni anno+). Contributo al portafoglio (outer-join + pesi rinormalizzati per sleeve a date diverse): TP01-solo FULL 1.30 / HOLD 0.31 -> TP01 70% + XS01 30%: FULL 1.41 / HOLD 1.15, DD giu', ~ogni anno+. -> XS01 BATTE il portafoglio esistente: inserito in active_sleeves. Caveat (documentati): storia XS ~2.5 anni; STAT-MODE (book 19 gambe non eseguibile a 2k -> ~20k), sleeve diagnostico/forward-monitor. portfolio.combine ora outer-join+renorm. 12 test passano. Diario 2026-06-19-hyperliquid-xsec.md. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-19 20:05:45 +00:00
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+"""GIUDICE DEI CONTENDER — valuta un segnale candidato a livello PORTAFOGLIO vs TP01.
+
+Per ogni (tf, sigfile): costruisce il BOOK 50/50 BTC+ETH del candidato (causale, netto fee),
+e applica il gauntlet STRETTO vs TP01:
+  - standalone: FULL Sh/DD, HOLD-OUT 2025-26 Sh/ret/DD, breadth per-anno (% anni positivi, rossi
+    consecutivi), correlazione a TP01;
+  - contributo al portafoglio: TP01-solo vs TP01+candidato a pesi 0.2/0.3/0.5 (Δ FULL e Δ HOLD).
+VERDETTO WINNER se: (A) batte TP01 standalone (book FULL Sh>1.30, hold-out Sh>~0.25, breadth ok),
+OPPURE (B) diversificatore robusto (corr bassa, alza il portafoglio su FULL E hold-out, breadth ok).
+
+    uv run python scripts/portfolio/verify_contender.py 1d /tmp/beat_sig_0.py 12h /tmp/beat_sig_10.py ...
+"""
+from __future__ import annotations
+import sys
+import importlib.util
+from pathlib import Path
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+import numpy as np
+import pandas as pd
+
+from scripts.analysis.research_lab import load_tf, _net_series
+from src.portfolio.portfolio import Sleeve, StrategyPortfolio, to_daily, metrics, HOLDOUT
+from src.portfolio.sleeves import tp01_sleeve
+
+TP01_FULL_SH = 1.30
+TP01_HOLD_SH = 0.31
+
+
+def load_signal(path):
+    spec = importlib.util.spec_from_file_location("csig_" + Path(path).stem, path)
+    m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m)
+    return m.signal
+
+
+def book_perbar(signal, tf) -> pd.Series:
+    s = {}
+    for a in ("BTC", "ETH"):
+        df = load_tf(a, tf)
+        net, _, _, _ = _net_series(df, np.asarray(signal(df, a, tf), float))
+        s[a] = pd.Series(net, index=pd.to_datetime(df["timestamp"], unit="ms", utc=True))
+    J = pd.concat(s, axis=1, join="inner").fillna(0.0)
+    return pd.Series(0.5 * J["BTC"].values + 0.5 * J["ETH"].values, index=J.index)
+
+
+def breadth(daily):
+    pre = daily[daily.index < HOLDOUT]
+    yr = [float((1 + g).prod() - 1) for _, g in pre.groupby(pre.index.year)]
+    consec = mx = 0
+    for v in yr:
+        consec = consec + 1 if v < 0 else 0; mx = max(mx, consec)
+    return (sum(v > 0 for v in yr) / len(yr) if yr else 0.0), mx, yr
+
+
+def main():
+    args = sys.argv[1:]
+    pairs = [(args[i], args[i + 1]) for i in range(0, len(args) - 1, 2)]
+    tp = tp01_sleeve(1.0)
+    tp_daily = tp.daily()
+    base = StrategyPortfolio([tp01_sleeve(1.0)]).backtest()
+    print("=" * 100)
+    print(f"  GIUDICE CONTENDER vs TP01 (book FULL Sh {base['full']['sharpe']:.2f} / HOLD {base['holdout']['sharpe']:.2f})")
+    print("=" * 100)
+
+    winners = []
+    for tf, sig in pairs:
+        name = Path(sig).stem
+        try:
+            signal = load_signal(sig)
+            pb = book_perbar(signal, tf)
+            d = to_daily(pb)
+        except Exception as e:
+            print(f"\n  {name} ({tf}): ERRORE {type(e).__name__}: {str(e)[:80]}"); continue
+        f = metrics(d); h = metrics(d[d.index >= HOLDOUT])
+        J = pd.concat({"tp": tp_daily, "x": d}, axis=1, join="inner").dropna()
+        corr = float(J["tp"].corr(J["x"])) if len(J) > 2 else float("nan")
+        pct, consec, yr = breadth(d)
+        print(f"\n  {name} ({tf})  BOOK 50/50")
+        print(f"    standalone: FULL Sh {f['sharpe']:>5.2f} DD {f['maxdd']*100:>4.1f}% | HOLD Sh {h['sharpe']:>5.2f} ret {h['ret']*100:>+6.1f}% DD {h['maxdd']*100:>4.1f}%"
+              f" | anni+ {pct*100:>3.0f}% rossi-consec {consec} | corr_TP01 {corr:+.2f} | turn n/a")
+        # contributo al portafoglio
+        contrib = []
+        for w in (0.2, 0.3, 0.5):
+            sl = Sleeve(name, w, lambda pb=pb: pb)
+            bt = StrategyPortfolio([tp01_sleeve(1 - w), sl]).backtest()
+            dF = bt["full"]["sharpe"] - base["full"]["sharpe"]
+            dH = bt["holdout"]["sharpe"] - base["holdout"]["sharpe"]
+            contrib.append((w, bt["full"]["sharpe"], dF, bt["holdout"]["sharpe"], dH))
+            print(f"    +TP01 w{w:.0%}: FULL {bt['full']['sharpe']:.2f} ({dF:+.2f}) | HOLD {bt['holdout']['sharpe']:.2f} ({dH:+.2f})")
+        breadth_ok = pct >= 0.6 and consec <= 1
+        standalone_beats = f["sharpe"] > TP01_FULL_SH and h["sharpe"] > 0.25 and breadth_ok
+        # diversificatore: corr<0.5, migliora FULL E hold del portafoglio ad almeno un peso, breadth ok
+        improves = any(dF > 0.05 and dH > 0.0 for _, _, dF, _, dH in contrib)
+        diversifier = (not np.isnan(corr) and corr < 0.5) and improves and breadth_ok
+        verdict = "WINNER-standalone" if standalone_beats else ("WINNER-diversifier" if diversifier else "no")
+        print(f"    -> {verdict}  (breadth_ok={breadth_ok}, standalone_beats={standalone_beats}, diversifier={diversifier})")
+        if verdict.startswith("WINNER"):
+            winners.append((name, tf, verdict))
+
+    print("\n" + "=" * 100)
+    print(f"  WINNERS: {len(winners)}")
+    for n, tf, v in winners:
+        print(f"    {n} ({tf}): {v}")
+    if not winners:
+        print("    nessuno batte TP01 con criterio onesto -> serve un'altra ondata.")
+
+
+if __name__ == "__main__":
+    main()