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/games/session_engine.py

@@ -0,0 +1,98 @@
+"""
+Motore del gioco-SESSION: pattern ORARI intraday. Ogni giorno si osserva il movimento
+in una "fascia di controllo" [ctrl_hour, ctrl_hour+ctrl_len) e si scommette sul movimento
+della finestra SUBITO DOPO (hold ore), seguendo (trend) o fadando (reversion) la fascia.
+
+Cerca se esistono orari il cui comportamento ANTICIPA la finestra successiva, ripetibile nei
+giorni. Dati orari reali (BTC=A, ETH=B), full history. PnL per-trade additivo, fee 0.10% RT.
+"""
+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
+
+FEE_RT = 0.001
+MIN_TRADES_PER_MONTH = 10.0
+BARS_PER_MONTH = 24 * 30
+
+
+def load_session(asset: str = "BTC"):
+    df = load_data(asset, "1h").copy()
+    dt = pd.to_datetime(df["datetime"])
+    return {"close": df["close"].to_numpy(float),
+            "open": df["open"].to_numpy(float),
+            "hour": dt.dt.hour.to_numpy(),
+            "day": (dt.dt.year * 366 + dt.dt.dayofyear).to_numpy(),  # indice giorno
+            "dt": dt.to_numpy(), "n": len(df)}
+
+
+def evaluate(data, spec, sl=None, fee=FEE_RT):
+    """spec = {ctrl_hour, ctrl_len, entry_thr(%), direction, hold}. Una valutazione per giorno:
+    a fine fascia di controllo, se |ret_fascia| > entry_thr entra e tiene hold ore."""
+    c, hour = data["close"], data["hour"]
+    n = data["n"]
+    s, e = (sl if sl else (0, n))
+    ch = int(spec["ctrl_hour"]) % 24
+    cl = max(1, int(spec["ctrl_len"]))
+    thr = float(spec["entry_thr"]) / 100.0
+    hold = max(1, int(spec["hold"]))
+    sign = 1 if spec.get("direction", "trend") == "trend" else -1
+
+    # indici in cui inizia la fascia di controllo (bar all'ora ch)
+    starts = np.where(hour[s:e] == ch)[0] + s
+    rets = []
+    for st in starts:
+        be = st + cl - 1                 # ultima barra della fascia
+        ex = be + hold                   # uscita
+        if ex >= e or st == 0:
+            continue
+        ctrl_ret = c[be] / c[st - 1] - 1.0     # ritorno della fascia (causale: chiude a be)
+        if abs(ctrl_ret) < thr:
+            continue
+        d = sign * (1 if ctrl_ret > 0 else -1)  # trend segue, reversion fada
+        entry = c[be]; exit_px = c[ex]
+        net = d * (exit_px - entry) / entry - fee
+        rets.append(net)
+    rets = np.array(rets)
+    nbars = e - s
+    months = nbars / BARS_PER_MONTH
+    n_tr = len(rets)
+    tpm = n_tr / months if months > 0 else 0.0
+    if n_tr == 0:
+        return dict(n_trades=0, win_rate=0.0, pnl_pct=0.0, tpm=0.0, sharpe=0.0,
+                    avg_ret=0.0, qualified=False, fitness=-1e6)
+    win = float(np.mean(rets > 0))
+    pnl = float(np.sum(rets)) * 100
+    avg = float(np.mean(rets)) * 100
+    sharpe = float(np.mean(rets) / (np.std(rets) + 1e-12) * np.sqrt(tpm * 12)) \
+        if np.std(rets) > 0 else 0.0
+    qualified = tpm >= MIN_TRADES_PER_MONTH
+    fitness = pnl + 50.0 * win
+    if not qualified:
+        fitness = -1e6 + pnl
+    return dict(n_trades=n_tr, win_rate=win, pnl_pct=pnl, tpm=tpm, sharpe=sharpe,
+                avg_ret=avg, qualified=qualified, fitness=fitness)
+
+
+def splits3(data, train_frac=0.60, valid_frac=0.20):
+    n = data["n"]
+    c1 = int(n * train_frac); c2 = int(n * (train_frac + valid_frac))
+    return (0, c1), (c1, c2), (c2, n)
+
+
+if __name__ == "__main__":
+    d = load_session("BTC"); tr, va, te = splits3(d)
+    for ch in [0, 8, 13, 20]:
+        for dr in ["trend", "reversion"]:
+            sp = {"ctrl_hour": ch, "ctrl_len": 2, "entry_thr": 0.3, "direction": dr, "hold": 4}
+            f = evaluate(d, sp, None); o = evaluate(d, sp, te)
+            print(f"h{ch:>2} {dr:>9} len2 hold4 thr0.3 | FULL pnl{f['pnl_pct']:7.0f} win{f['win_rate']*100:3.0f} "
+                  f"tpm{f['tpm']:4.0f} Sh{f['sharpe']:5.1f} | OOS Sh{o['sharpe']:5.1f}")