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/analysis/honest_matrix.py

@@ -0,0 +1,80 @@
+"""Tabella unica consolidata: PnL% NETTO per anno, tutte le strategie a confronto.
+Colonne: DIP01(BTC) · TR01(basket) · ROT01(base) · ROT02(dual-mom) · PORTAFOGLIO.
+Ultima riga: TOT e DD full-period.
+"""
+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 scripts.analysis.honest_lab import available_assets, FEE_RT
+from scripts.analysis.honest_improve import _dd
+from scripts.analysis.honest_improve2 import (
+    dip_market_gated, _daily_equity, _norm, _tr_basket_daily,
+)
+from scripts.analysis.honest_rotation import build_panel
+
+LEV, POS = 3.0, 0.15
+
+
+def rot_daily(idx, regime_n=0, lookback=60, top_k=2, gross=0.45):
+    """equity giornaliera della rotazione, con/senza overlay dual-momentum."""
+    panel = build_panel(available_assets(), "1d")
+    cols = list(panel.columns); P = panel.values; T, N = P.shape
+    rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
+    btc = P[:, cols.index("BTC")]
+    bma = pd.Series(btc).rolling(max(regime_n, 2)).mean().values
+    use_reg = regime_n and regime_n > 1
+    cap = 1000.0; w = np.zeros(N); tl, cl = [], []
+    start = max(lookback + 1, regime_n + 1 if use_reg else 0)
+    for i in range(start, T - 1):
+        risk_on = (btc[i] > bma[i]) if (use_reg and not np.isnan(bma[i])) else True
+        mom = P[i] / P[i - lookback] - 1; order = np.argsort(mom)[::-1]
+        chosen = [j for j in order if mom[j] > 0][:top_k] if risk_on else []
+        nw = np.zeros(N)
+        for j in chosen:
+            nw[j] = gross / len(chosen)
+        cap -= cap * np.abs(nw - w).sum() * (FEE_RT / 2); w = nw
+        cap = max(cap * (1 + float(np.dot(w, rets[i + 1]))), 10.0)
+        tl.append(panel.index[i]); cl.append(cap)
+    return _norm(_daily_equity(tl, cl, idx))
+
+
+def year_pnl(eq):
+    return {int(y): (g.iloc[-1] / g.iloc[0] - 1) * 100 for y, g in _norm(eq).groupby(eq.index.year)}
+
+
+if __name__ == "__main__":
+    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
+    d = dip_market_gated("BTC", market_n=0, return_equity=True)
+    cols = {
+        "DIP01(BTC)": _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx)),
+        "TR01(bskt)": _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx)),
+        "ROT01": rot_daily(idx, regime_n=0),
+        "ROT02": rot_daily(idx, regime_n=100),
+    }
+    drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in {
+        "DIP01(BTC)": cols["DIP01(BTC)"], "TR01(bskt)": cols["TR01(bskt)"], "ROT02": cols["ROT02"]
+    }.items()})
+    cols["PORTAF."] = (1 + drets.mean(axis=1)).cumprod()
+
+    names = list(cols)
+    py = {n: year_pnl(cols[n]) for n in names}
+    years = sorted({y for n in names for y in py[n]})
+
+    print("=" * 78)
+    print("  PnL% NETTO PER ANNO — confronto strategie (leva 3x, fee 0.10% RT)")
+    print("=" * 78)
+    print(f"  {'Anno':>6s}" + "".join(f"{n:>12s}" for n in names))
+    print("  " + "-" * 72)
+    for y in years:
+        print(f"  {y:>6d}" + "".join(f"{py[n].get(y, float('nan')):>+12.0f}" if y in py[n] else f"{'-':>12s}" for n in names))
+    print("  " + "-" * 72)
+    print(f"  {'TOT%':>6s}" + "".join(f"{(cols[n].iloc[-1]/cols[n].iloc[0]-1)*100:>+12.0f}" for n in names))
+    print(f"  {'DDfull':>6s}" + "".join(f"{_dd(cols[n].values):>12.0f}" for n in names))