""" Backtest RAPIDE - Focus sur les meilleures configs """ import pandas as pd import numpy as np from apollo_client import ApolloDegenClient print('\n' + '='*100) print('⚡ FAST BACKTEST - Configurations optimales') print('='*100) client = ApolloDegenClient() df = client.get_klines('BTCUSDT', '1m', limit=1500) print(f'✅ {len(df)} bougies chargées') print(f'📅 Période: {df.iloc[0]["timestamp"]} → {df.iloc[-1]["timestamp"]}') print() # Calculer MA for period in [10, 20, 30, 50]: df[f'ma_{period}'] = df['close'].rolling(period).mean() # Calculer EMA df['ema_10'] = df['close'].ewm(span=10).mean() df['ema_30'] = df['close'].ewm(span=30).mean() df['ema_12'] = df['close'].ewm(span=12).mean() df['ema_26'] = df['close'].ewm(span=26).mean() def backtest_ma(df, ma_period, sl_pct, tp_ratio, leverage): """Backtest stratégie MA simple""" capital = 100 trades = [] ma_col = f'ma_{ma_period}' position = None for i in range(ma_period + 10, len(df)): if pd.isna(df.iloc[i][ma_col]): continue close = df.iloc[i]['close'] ma = df.iloc[i][ma_col] # Gérer position ouverte if position: # Check SL/TP if position['side'] == 'LONG': if close <= position['sl']: pnl_pct = (close - position['entry']) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue elif close >= position['tp']: pnl_pct = (close - position['entry']) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue else: # SHORT if close >= position['sl']: pnl_pct = (position['entry'] - close) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue elif close <= position['tp']: pnl_pct = (position['entry'] - close) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue # Nouveau signal si pas de position if not position and capital > 0: side = 'LONG' if close > ma else 'SHORT' if side == 'LONG': sl = close * (1 - sl_pct) tp = close * (1 + sl_pct * tp_ratio) else: sl = close * (1 + sl_pct) tp = close * (1 - sl_pct * tp_ratio) margin = capital * 0.05 # 5% risk position = { 'side': side, 'entry': close, 'sl': sl, 'tp': tp, 'margin': margin } # Fermer position finale if position: close = df.iloc[-1]['close'] if position['side'] == 'LONG': pnl_pct = (close - position['entry']) / position['entry'] else: pnl_pct = (position['entry'] - close) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) if not trades: return None ret = ((capital - 100) / 100) * 100 wins = [t for t in trades if t > 0] wr = (len(wins) / len(trades)) * 100 if trades else 0 return { 'return': round(ret, 2), 'trades': len(trades), 'win_rate': round(wr, 1), 'wins': len(wins), 'losses': len(trades) - len(wins), 'capital': round(capital, 2) } def backtest_ema_cross(df, fast, slow, sl_pct, tp_ratio, leverage): """Backtest EMA cross""" capital = 100 trades = [] ema_fast = f'ema_{fast}' ema_slow = f'ema_{slow}' position = None for i in range(slow + 10, len(df)): if pd.isna(df.iloc[i][ema_fast]) or pd.isna(df.iloc[i][ema_slow]): continue close = df.iloc[i]['close'] fast_val = df.iloc[i][ema_fast] slow_val = df.iloc[i][ema_slow] # Gérer position ouverte if position: # Check SL/TP if position['side'] == 'LONG': if close <= position['sl']: pnl_pct = (close - position['entry']) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue elif close >= position['tp']: pnl_pct = (close - position['entry']) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue else: # SHORT if close >= position['sl']: pnl_pct = (position['entry'] - close) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue elif close <= position['tp']: pnl_pct = (position['entry'] - close) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) position = None continue # Nouveau signal (crossover) if not position and capital > 0 and i > 0: prev_fast = df.iloc[i-1][ema_fast] prev_slow = df.iloc[i-1][ema_slow] side = None if fast_val > slow_val and prev_fast <= prev_slow: side = 'LONG' # Bullish cross elif fast_val < slow_val and prev_fast >= prev_slow: side = 'SHORT' # Bearish cross if side: if side == 'LONG': sl = close * (1 - sl_pct) tp = close * (1 + sl_pct * tp_ratio) else: sl = close * (1 + sl_pct) tp = close * (1 - sl_pct * tp_ratio) margin = capital * 0.05 position = { 'side': side, 'entry': close, 'sl': sl, 'tp': tp, 'margin': margin } # Fermer position finale if position: close = df.iloc[-1]['close'] if position['side'] == 'LONG': pnl_pct = (close - position['entry']) / position['entry'] else: pnl_pct = (position['entry'] - close) / position['entry'] pnl = pnl_pct * leverage * position['margin'] capital += pnl trades.append(pnl) if not trades: return None ret = ((capital - 100) / 100) * 100 wins = [t for t in trades if t > 0] wr = (len(wins) / len(trades)) * 100 if trades else 0 return { 'return': round(ret, 2), 'trades': len(trades), 'win_rate': round(wr, 1), 'wins': len(wins), 'losses': len(trades) - len(wins), 'capital': round(capital, 2) } # TESTS results = [] print('='*100) print('🧪 TEST 1: STRATÉGIES MA SIMPLES') print('='*100) print() configs_tested = 0 for ma in [10, 20, 30, 50]: for sl in [0.0001, 0.00025, 0.0005, 0.001, 0.002]: for tp_ratio in [3, 4, 5, 6, 8, 10]: for lev in [100, 150, 200, 250]: configs_tested += 1 if configs_tested % 50 == 0: print(f'⏳ Testé: {configs_tested} configs...') result = backtest_ma(df, ma, sl, tp_ratio, lev) if result: results.append({ 'strategy': f'MA{ma}', 'ma_period': ma, 'sl_pct': sl, 'tp_ratio': tp_ratio, 'leverage': lev, **result }) print(f'✅ {configs_tested} configs MA testées') print() print('='*100) print('🧪 TEST 2: STRATÉGIES EMA CROSS') print('='*100) print() configs_tested = 0 for fast, slow in [(10, 30), (12, 26)]: for sl in [0.0001, 0.00025, 0.0005, 0.001, 0.002]: for tp_ratio in [3, 4, 5, 6, 8, 10]: for lev in [100, 150, 200, 250]: configs_tested += 1 if configs_tested % 50 == 0: print(f'⏳ Testé: {configs_tested} configs...') result = backtest_ema_cross(df, fast, slow, sl, tp_ratio, lev) if result: results.append({ 'strategy': f'EMA{fast}x{slow}', 'fast': fast, 'slow': slow, 'sl_pct': sl, 'tp_ratio': tp_ratio, 'leverage': lev, **result }) print(f'✅ {configs_tested} configs EMA testées') print() # Trier et afficher results.sort(key=lambda x: x['return'], reverse=True) print('='*100) print('🏆 TOP 30 CONFIGURATIONS PAR PROFIT TOTAL') print('='*100) print() for i, r in enumerate(results[:30], 1): print(f"{i}. {r['strategy']}") print(f" 💵 Return: {r['return']:+.2f}% (Capital: ${r['capital']:.2f})") print(f" ✅ Win Rate: {r['win_rate']:.1f}% ({r['wins']}W/{r['losses']}L)") print(f" 📈 Trades: {r['trades']}") print(f" ⚙️ SL: {r['sl_pct']*100:.3f}% | TP Ratio: {r['tp_ratio']}:1 | Leverage: {r['leverage']}x") print() # Champion if results: champion = results[0] print('='*100) print('👑 CHAMPION ABSOLU') print('='*100) print(f"Stratégie: {champion['strategy']}") print(f"Return: {champion['return']:+.2f}%") print(f"Win Rate: {champion['win_rate']:.1f}%") print(f"Trades: {champion['trades']}") print(f"SL: {champion['sl_pct']*100:.3f}%") print(f"TP Ratio: {champion['tp_ratio']}:1") print(f"Leverage: {champion['leverage']}x") print(f"Capital final: ${champion['capital']:.2f}") print('='*100)