feat(analysis): 3 strategie oneste validate OOS multi-crypto (DIP/TR/ROT)
Ricerca onesta post-squeeze su 8 crypto (2018-2026), engine fee-aware con ingresso eseguibile a close[i], uscita TP/SL intrabar, OOS held-out, sweep fee. Lezione madre: shortare cripto perde OOS sistematicamente (campione net-bull) -> tutte le strategie robuste sono long-biased. Tre meccanismi distinti e complementari: - DIP01 dip-buy z-score reversion (long-only, 1h) robusto BTC/ETH/SOL - TR01 EMA 20/100 trend-following (long-only, 4h) robusto su 5/8 asset - ROT01 rotazione cross-sectional momentum sul paniere (1d) OOS +44%, param-insensitive Engine e validazione: scripts/analysis/honest_lab.py + honest_final.py (+ honest_candidates/diag/diag2/trend/rotation). Diario in docs/diary/. Onesto sull'obiettivo: €50/giorno su €1000 in pochi mesi non e' raggiungibile a rischio sano (~1825%/anno); edge reali 30-60% OOS pluriennale. Via realistica: portafoglio delle 3, leva moderata, crescita composta. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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"""DIP01 — Dip-Buy Z-Score Reversion (long-only).
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Variante robusta e ONESTA della famiglia mean-reversion: compra SOLO i dip
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(close a z<=-z_in deviazioni sotto la media mobile) e prende profitto al rientro
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verso la media. Niente short: nel campione 2018-2026 shortare cripto perde OOS
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sistematicamente (vedi scripts/analysis/honest_final.py).
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Logica:
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1. z-score = (close - SMA(n)) / STD(n)
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2. ENTRY long quando z attraversa al ribasso -z_in (capitolazione)
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3. EXIT: take-profit alla media mobile, stop-loss a sl_atr*ATR sotto l'entry,
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o time-limit max_bars
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4. ingresso a close[i] (eseguibile dal vivo, nessun look-ahead)
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Validazione (netto, fee 0.10% RT Deribit, leva 3x, OOS = ultimo 30%):
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BTC 1h: FULL +298% / OOS +59% / DD 23% / 7-9 anni positivi
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ETH 1h: FULL +190% / OOS +224% / DD 54%
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SOL 1h: FULL +50% / OOS +13% / DD 25%
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Regge lo sweep fee fino a 0.20% RT (BTC OOS +45% anche a 0.20%).
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Robusto su BTC/ETH/SOL (asset major); sugli alt molto parabolici (DOGE/BNB)
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non ha edge -> usare solo su BTC/ETH/SOL.
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Compatibile con StrategyWorker: ogni Signal porta tp/sl/max_bars in metadata.
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats, TF_MINUTES
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from src.data.downloader import load_data
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def _atr(df: pd.DataFrame, n: int = 14) -> np.ndarray:
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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pc = np.roll(c, 1); pc[0] = c[0]
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tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
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return pd.Series(tr).rolling(n).mean().values
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class DipReversion(Strategy):
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name = "DIP01_dip_reversion"
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description = "Long-only dip-buy z-score reversion, TP alla media"
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default_assets = ["BTC", "ETH", "SOL"]
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default_timeframes = ["1h"]
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fee_rt = 0.001
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leverage = 3.0
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position_size = 0.15
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initial_capital = 1000.0
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def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
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**params) -> list[Signal]:
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c = df["close"].values
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n = params.get("n", 50)
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z_in = params.get("z_in", 2.5)
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sl_atr = params.get("sl_atr", 2.5)
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max_bars = params.get("max_bars", 24)
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ma = pd.Series(c).rolling(n).mean().values
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sd = pd.Series(c).rolling(n).std().values
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a = _atr(df, 14)
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z = (c - ma) / np.where(sd == 0, np.nan, sd)
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signals: list[Signal] = []
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for i in range(n + 14, len(c)):
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if np.isnan(z[i]) or np.isnan(a[i]):
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continue
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if z[i] <= -z_in and z[i - 1] > -z_in:
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signals.append(Signal(
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idx=i, direction=1, entry_price=c[i],
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metadata={"tp": float(ma[i]), "sl": float(c[i] - sl_atr * a[i]),
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"max_bars": max_bars},
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))
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return signals
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def backtest(self, asset: str, tf: str = "1h", hold: int = 3,
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**params) -> BacktestResult | None:
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df = load_data(asset, tf)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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signals = self.generate_signals(df, ts, **params)
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if not signals:
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return None
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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n = len(c)
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fee = self.fee_rt * self.leverage
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capital = peak = float(self.initial_capital)
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max_dd = 0.0
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total_bars = 0
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last_exit = -1
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yearly: dict[int, dict] = {}
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for sig in signals:
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i, d = sig.idx, sig.direction
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if i <= last_exit or i + 1 >= n:
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continue
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entry = c[i]
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tp, sl, mb = sig.metadata["tp"], sig.metadata["sl"], sig.metadata["max_bars"]
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exit_p = c[min(i + mb, n - 1)]
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j = min(i + mb, n - 1)
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for step in range(1, mb + 1):
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j = i + step
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if j >= n:
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j = n - 1; exit_p = c[j]; break
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hit_sl = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl)
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hit_tp = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
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if hit_sl:
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exit_p = sl; break
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if hit_tp:
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exit_p = tp; break
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if step == mb:
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exit_p = c[j]
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ret = (exit_p - entry) / entry * d * self.leverage - fee
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capital = max(capital + capital * self.position_size * ret, 10.0)
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if capital > peak:
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peak = capital
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max_dd = max(max_dd, (peak - capital) / peak)
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total_bars += (j - i)
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last_exit = j
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year = ts.iloc[i].year
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yr = yearly.setdefault(year, {"w": 0, "t": 0, "pnl": 0.0})
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yr["t"] += 1
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if ret > 0:
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yr["w"] += 1
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yr["pnl"] += ret * self.initial_capital
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all_t = sum(v["t"] for v in yearly.values())
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all_w = sum(v["w"] for v in yearly.values())
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if all_t == 0:
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return None
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yearly_stats = [YearlyStats(y, v["t"], v["w"], v["pnl"]) for y, v in sorted(yearly.items())]
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return BacktestResult(
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strategy_name=self.name, asset=asset, timeframe=tf, params=params,
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trades=all_t, wins=all_w, pnl=sum(v["pnl"] for v in yearly.values()),
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capital=capital, initial_capital=self.initial_capital,
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max_dd=max_dd * 100, time_in_market_pct=total_bars / n * 100,
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avg_trade_duration_h=total_bars / all_t * TF_MINUTES.get(tf, 60) / 60,
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years_active=len(yearly), yearly=yearly_stats,
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)
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if __name__ == "__main__":
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strat = DipReversion()
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print(f"{'=' * 100}")
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print(f" DIP01 DIP-BUY REVERSION — netto fee {strat.fee_rt*100:.2f}% RT, leva {strat.leverage:.0f}x")
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print(f"{'=' * 100}")
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for asset in ["BTC", "ETH", "SOL"]:
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r = strat.backtest(asset, "1h", n=50, z_in=2.5, sl_atr=2.5, max_bars=24)
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if r:
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r.strategy_name = f"DIP01 {asset} 1h"
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r.print_summary()
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