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diff --git a/src/backtest/harness.py b/src/backtest/harness.py
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+++ b/src/backtest/harness.py
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+"""HONEST BACKTEST HARNESS — universo certificato BTC/ETH (Deribit mainnet).
+
+Foundation per la ricerca post-reset (2026-06-19). Tutte le strategie nuove devono
+usare QUESTO harness per garantire:
+  1. NESSUN look-ahead: la direzione e il prezzo d'ingresso si decidono con dati fino
+     a close[i] incluso, e si ENTRA a close[i] (la barra successiva, i+1, e' la prima
+     in cui si e' realmente in posizione). L'exit intrabar guarda high/low di i+1..
+  2. Fee realistiche Deribit: 0.10% round-trip (taker) di default.
+  3. Metriche oneste: equity compounding, CAGR, Sharpe (da rendimenti per-barra),
+     max drawdown, per-anno, e split OOS.
+
+Convenzione segnali (entry-eseguibile):
+  Una strategia produce, per ogni indice i, un dict opzionale:
+     {'dir': +1/-1, 'tp': prezzo|None, 'sl': prezzo|None, 'max_bars': int|None}
+  decidendo SOLO con dati [.. i] (close[i] incluso). L'engine apre a close[i] e
+  gestisce l'uscita dalle barre i+1 in poi (TP/SL intrabar al livello, SL prioritario;
+  altrimenti max_bars al close).
+
+Uso tipico:
+    from src.backtest.harness import load, backtest_signals, Metrics
+    df = load("BTC", "1h")
+    entries = my_signal_fn(df)   # list[dict|None] lunga len(df)
+    m = backtest_signals(df, entries, fee_rt=0.001, leverage=1.0)
+    m.print_summary("MYSTRAT BTC 1h")
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+import pandas as pd
+
+from src.data.downloader import load_data
+
+CERTIFIED = {"BTC", "ETH"}
+
+
+def load(asset: str, tf: str) -> pd.DataFrame:
+    """Carica un feed certificato. Solleva su asset non certificato (guardrail fisico)."""
+    if asset.upper() not in CERTIFIED:
+        raise ValueError(f"Asset non certificato: {asset}. Universo = {CERTIFIED}.")
+    df = load_data(asset, tf).reset_index(drop=True)
+    df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    return df
+
+
+# ---------------------------------------------------------------------------
+# Metriche
+# ---------------------------------------------------------------------------
+@dataclass
+class Metrics:
+    asset: str = ""
+    tf: str = ""
+    n_trades: int = 0
+    wins: int = 0
+    net_return: float = 0.0          # ritorno totale frazionale (final/initial - 1)
+    cagr: float = 0.0
+    sharpe: float = 0.0              # annualizzato dai rendimenti per-barra dell'equity
+    max_dd: float = 0.0              # frazione (0.10 = 10%)
+    time_in_market: float = 0.0      # frazione barre in posizione
+    avg_bars: float = 0.0
+    final_capital: float = 0.0
+    initial_capital: float = 0.0
+    bars_per_year: float = 0.0
+    yearly: dict = field(default_factory=dict)   # year -> net return frazionale dell'anno
+    equity: np.ndarray = field(default_factory=lambda: np.array([]))
+    eq_index: pd.DatetimeIndex | None = None
+
+    @property
+    def win_rate(self) -> float:
+        return self.wins / self.n_trades * 100 if self.n_trades else 0.0
+
+    @property
+    def profit_per_day_on(self, capital: float = 2000.0) -> float:  # placeholder
+        return 0.0
+
+    def daily_profit(self, capital: float = 2000.0) -> float:
+        """€/giorno medio se partito con `capital` (su tutto lo span, compounding incluso)."""
+        if self.eq_index is None or len(self.equity) < 2:
+            return 0.0
+        idx = self.eq_index
+        days = (idx.iloc[-1] - idx.iloc[0]).total_seconds() / 86400 if hasattr(idx, "iloc") \
+            else (idx[-1] - idx[0]).total_seconds() / 86400
+        if days <= 0:
+            return 0.0
+        final = capital * (self.final_capital / self.initial_capital)
+        return (final - capital) / days
+
+    def print_summary(self, label: str = ""):
+        print(f"  {label:<26s} trades={self.n_trades:>5d} wr={self.win_rate:>4.1f}% "
+              f"ret={self.net_return*100:>+8.0f}% CAGR={self.cagr*100:>+6.1f}% "
+              f"Sharpe={self.sharpe:>5.2f} DD={self.max_dd*100:>4.1f}% "
+              f"mkt={self.time_in_market*100:>4.0f}% €/d(2k)={self.daily_profit(2000):>+6.2f}")
+
+    def print_yearly(self):
+        for y in sorted(self.yearly):
+            print(f"    {y}: {self.yearly[y]*100:>+7.1f}%")
+
+
+def _sharpe(equity: np.ndarray, bars_per_year: float) -> float:
+    if len(equity) < 3:
+        return 0.0
+    r = np.diff(equity) / equity[:-1]
+    r = r[np.isfinite(r)]
+    if len(r) == 0 or np.std(r) == 0:
+        return 0.0
+    return float(np.mean(r) / np.std(r) * np.sqrt(bars_per_year))
+
+
+def _max_dd(equity: np.ndarray) -> float:
+    peak = np.maximum.accumulate(equity)
+    dd = (peak - equity) / peak
+    return float(np.max(dd)) if len(dd) else 0.0
+
+
+def backtest_signals(
+    df: pd.DataFrame,
+    entries: list,
+    fee_rt: float = 0.001,
+    leverage: float = 1.0,
+    position_size: float = 1.0,
+    initial_capital: float = 1000.0,
+    allow_overlap: bool = False,
+    asset: str = "",
+    tf: str = "",
+) -> Metrics:
+    """Esegue il backtest su una lista di entry-dict (uno per barra, None = niente segnale).
+
+    entry dict: {'dir': +1/-1, 'tp': float|None, 'sl': float|None, 'max_bars': int|None}
+    - apertura a close[i] (decisa con dati <= i)
+    - exit dalle barre i+1.. : TP/SL toccati intrabar (al livello, SL prioritario),
+      altrimenti chiusura al close dopo max_bars (default 24 se assente).
+    - non si apre una nuova posizione finche' la precedente non e' chiusa (allow_overlap=False).
+    - PnL compounding: ogni trade muove capital di position_size * leverage * (ret_netto).
+    """
+    c = df["close"].values.astype(float)
+    h = df["high"].values.astype(float)
+    l = df["low"].values.astype(float)
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    capital = float(initial_capital)
+    equity = np.full(n, capital, dtype=float)
+    yearly: dict[int, float] = {}
+    yearly_start: dict[int, float] = {}
+
+    n_trades = wins = 0
+    bars_in_market = 0
+    bars_sum = 0
+    i = 0
+    busy_until = -1
+
+    for i in range(n):
+        e = entries[i] if i < len(entries) else None
+        if e is None or e.get("dir", 0) == 0:
+            equity[i] = capital
+            continue
+        if not allow_overlap and i <= busy_until:
+            equity[i] = capital
+            continue
+
+        direction = int(e["dir"])
+        entry = c[i]
+        tp = e.get("tp")
+        sl = e.get("sl")
+        max_bars = int(e.get("max_bars") or 24)
+
+        exit_price = c[min(i + max_bars, n - 1)]
+        exit_idx = min(i + max_bars, n - 1)
+        for j in range(i + 1, min(i + max_bars + 1, n)):
+            hit_sl = sl is not None and (
+                (direction == 1 and l[j] <= sl) or (direction == -1 and h[j] >= sl))
+            hit_tp = tp is not None and (
+                (direction == 1 and h[j] >= tp) or (direction == -1 and l[j] <= tp))
+            if hit_sl:
+                exit_price = sl
+                exit_idx = j
+                break
+            if hit_tp:
+                exit_price = tp
+                exit_idx = j
+                break
+            exit_price = c[j]
+            exit_idx = j
+
+        gross = (exit_price - entry) / entry * direction
+        net = gross * leverage - fee_rt * leverage
+        capital += capital * position_size * net
+        capital = max(capital, 1.0)
+
+        year = ts.iloc[i].year
+        if year not in yearly_start:
+            yearly_start[year] = capital / (1 + position_size * net) if (1 + position_size * net) else capital
+        n_trades += 1
+        if gross > 0:
+            wins += 1
+        bars = exit_idx - i
+        bars_in_market += bars
+        bars_sum += bars
+        busy_until = exit_idx
+
+        # propaga equity fino a exit_idx (mark a fine trade, semplice ma onesto a livello trade)
+        equity[i:exit_idx + 1] = capital
+
+    # riempi i buchi finali
+    for k in range(1, n):
+        if equity[k] == initial_capital and equity[k - 1] != initial_capital:
+            equity[k] = equity[k - 1]
+    # forward fill robusto
+    last = initial_capital
+    for k in range(n):
+        if equity[k] != last and equity[k] != initial_capital:
+            last = equity[k]
+        else:
+            equity[k] = last
+
+    # per-anno dal vettore equity
+    eq_s = pd.Series(equity, index=ts)
+    yearly_ret = {}
+    for y, grp in eq_s.groupby(eq_s.index.year):
+        if len(grp) > 1 and grp.iloc[0] > 0:
+            yearly_ret[int(y)] = float(grp.iloc[-1] / grp.iloc[0] - 1)
+
+    span_days = (ts.iloc[-1] - ts.iloc[0]).total_seconds() / 86400
+    years = span_days / 365.25 if span_days > 0 else 1.0
+    bars_per_year = n / years if years > 0 else n
+    cagr = (capital / initial_capital) ** (1 / years) - 1 if years > 0 and capital > 0 else -1.0
+
+    return Metrics(
+        asset=asset, tf=tf,
+        n_trades=n_trades, wins=wins,
+        net_return=capital / initial_capital - 1,
+        cagr=cagr,
+        sharpe=_sharpe(equity, bars_per_year),
+        max_dd=_max_dd(equity),
+        time_in_market=bars_in_market / n if n else 0.0,
+        avg_bars=bars_sum / n_trades if n_trades else 0.0,
+        final_capital=capital,
+        initial_capital=initial_capital,
+        bars_per_year=bars_per_year,
+        yearly=yearly_ret,
+        equity=equity,
+        eq_index=ts,
+    )
+
+
+def oos_split(df: pd.DataFrame, frac: float = 0.65):
+    """Indice di taglio IS/OOS (default 65% in-sample)."""
+    return int(len(df) * frac)