chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita

Reset del progetto su fondamenta verificate dopo la scoperta che l'intera
libreria "validata OOS" era artefatto di feed contaminato (print fantasma del
feed Cerbero TESTNET + storico Binance/USDT).

- Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e
  CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia
  (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample
  (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE
  50-82% barre flat; XRP/BNB non certificabili).
- Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni
  portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST
  con segnale residuo, da ri-validare in isolamento.
- Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio,
  runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/
  portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/
  (preservati, non cancellati). Diario consolidato in un unico documento.
- Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal +
  src/backtest/engine + load_data; tool dati certificati (rebuild_history,
  certify_feed, audit_feed, multi_source_check).
- Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico
  (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Old/scripts/strategies/XS01_cross_sectional.py

@@ -0,0 +1,105 @@
+"""XS01 — Cross-Sectional Reversion (market-neutral su 8 cripto). FAMIGLIA NUOVA.
+
+Distinta dai pairs PR01 (pairwise) e dai fade (single-asset): ogni HOLD ore classifica
+gli 8 asset per rendimento su LB ore e va LONG i perdenti relativi / SHORT i vincenti
+(peso ∝ -(ret - media_cross-section)), market-neutral gross 1. Cattura il FATTORE
+reversione cross-sezionale. Scorrelato (~0) da pairs e fade -> diversificatore.
+
+Engine ONESTO (no look-ahead, verificato): pesi a barra i da close[<=i]; ingresso a
+close[i], uscita a close[i+HOLD]; roll NON sovrapposto (riallinea ogni HOLD barre).
+Fee = 0.10% RT/book (turnover gross 1 -> 2*fee_rt). PnL su capitale composto (pos, lev).
+
+Validazione (sessione 2026-06-09, lb48 hold12, fee 0.10% RT, OOS ultimo 30%):
+  FULL Sharpe ~3.3 / OOS ~3.4, plateau lb 12-72 x hold 6-24 (OOS 2-3.9), 4/5 anni+.
+  Decorrelato (-0.006 da PR01 ETH/BTC). Cost-sensitive: muore ~0.35% RT/book.
+  Gate PORT06: +XS01 -> OOS Sharpe 9.66->10.07, FULL DD 3.68->3.46 (OOS DD +0.06pp a mezza size).
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.data.downloader import load_data
+
+UNIVERSE = ["BTC", "ETH", "LTC", "ADA", "SOL", "BNB", "XRP", "DOGE"]
+FEE_RT, LEV, POS, OOS_FRAC = 0.0005, 3.0, 0.15, 0.30
+LB, HOLD = 48, 12
+
+
+def aligned_panel(assets=UNIVERSE, tf="1h"):
+    dfs = {a: load_data(a, tf)[["timestamp", "close"]].rename(columns={"close": a}).set_index("timestamp")
+           for a in assets}
+    M = pd.concat(dfs.values(), axis=1, join="inner").sort_index()
+    return M[assets]
+
+
+def xsec_sim(assets=UNIVERSE, tf="1h", lb=LB, hold=HOLD, fee_rt=FEE_RT, lev=LEV,
+             pos=POS, split_frac=0.0):
+    M = aligned_panel(assets, tf)
+    C = M[assets].values
+    ts = pd.to_datetime(M.index, unit="ms", utc=True)
+    n = len(C); logC = np.log(C)
+    split = int(n * split_frac)
+    cap = peak = 1000.0; dd = 0.0
+    trades = wins = 0; rets = []; yearly = {}; yearly_n = {}
+    eq_ts, eq_v = [], []
+    last = -1; i = max(lb, split)
+    fee = 2 * fee_rt                       # gross 1 -> turnover 2 (entra+esce)
+    while i < n - hold:
+        if i <= last:
+            i += 1; continue
+        dm = (logC[i] - logC[i - lb]); dm = dm - dm.mean()
+        w = -dm; gw = np.sum(np.abs(w))
+        if gw < 1e-9:
+            i += 1; continue
+        w = w / gw                         # market-neutral, gross 1
+        book = float(np.sum(w * (logC[i + hold] - logC[i])))
+        net = book - fee
+        cap = max(cap + cap * pos * lev * net, 10.0)
+        peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
+        trades += 1; wins += net > 0; rets.append(net * pos); last = i + hold
+        eq_ts.append(ts[i + hold]); eq_v.append(cap)
+        yearly[ts[i].year] = yearly.get(ts[i].year, 0.0) + net * 100
+        yearly_n[ts[i].year] = yearly_n.get(ts[i].year, 0) + 1
+        i += 1
+    yrs_span = (ts[-1] - ts[max(split, 0)]).days / 365.25 or 1
+    sharpe = 0.0
+    if len(rets) > 1 and np.std(rets) > 0:
+        sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades / yrs_span))
+    ret_tot = (cap / 1000 - 1) * 100
+    cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
+    return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot,
+                cagr=cagr, dd=dd * 100, sharpe=sharpe, yearly=yearly, yearly_n=yearly_n,
+                eq_ts=eq_ts, eq_v=eq_v)
+
+
+def check_no_lookahead():
+    M = aligned_panel(); logC = np.log(M.values); i = 1000
+    a = (logC[i] - logC[i - LB])
+    Cp = logC.copy(); Cp[i + 1:] += 0.5
+    b = (Cp[i] - Cp[i - LB])
+    print(f"  no-look-ahead: segnale invariato col futuro perturbato -> "
+          f"{'OK' if np.allclose(a, b) else 'VIOLAZIONE'}")
+
+
+def run():
+    print("=" * 84)
+    print("  XS01 — Cross-Sectional Reversion (8 asset, market-neutral) | netto fee 0.10% RT/book")
+    print("=" * 84)
+    check_no_lookahead()
+    f = xsec_sim()
+    o = xsec_sim(split_frac=1 - OOS_FRAC)
+    yrs = f["yearly"]; pos_y = sum(1 for v in yrs.values() if v > 0)
+    print(f"  trade {f['trades']} | win {f['win']:.1f}% | CAGR {f['cagr']:.0f}% | DD {f['dd']:.0f}% | "
+          f"Sharpe FULL {f['sharpe']:.2f} / OOS {o['sharpe']:.2f} | anni+ {pos_y}/{len(yrs)}")
+    print("  per anno:", "  ".join(f"{y}:{v:+.0f}%" for y, v in sorted(yrs.items())))
+
+
+if __name__ == "__main__":
+    run()