#!/usr/bin/env python3 """ Stratégie de Scalping 1 Minute - Ultra Leverage Capture de petits mouvements avec leverage 500x-1000x """ import pandas as pd import numpy as np from typing import Optional, Dict from dataclasses import dataclass import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass class ScalpingSignal: """Signal de scalping""" symbol: str side: str # 'LONG' ou 'SHORT' entry_price: float stop_loss: float take_profit: float leverage: int confidence: float # 0-100 reason: str class Scalping1MStrategy: """ Stratégie de scalping haute fréquence sur 1 minute Principe: - Leverage très élevé (500x-1000x) - SL ultra serré (0.02%-0.05%) - TP petit mais régulier (0.05%-0.15%) - Win rate élevé nécessaire (70%+) Signaux: - EMA crossovers rapides (3/7/15) - Momentum fort sur dernières bougies - Volume spike confirmation - RSI extrêmes pour contre-tendance """ def __init__( self, leverage: int = 1000, sl_pct: float = 0.03, # 0.03% SL = 30% perte avec 1000x tp_pct: float = 0.10, # 0.10% TP = 100% profit avec 1000x ema_fast: int = 3, ema_mid: int = 7, ema_slow: int = 15, rsi_period: int = 7, volume_spike_threshold: float = 1.5 ): self.leverage = leverage self.sl_pct = sl_pct / 100 # Convertir en décimal self.tp_pct = tp_pct / 100 # Indicateurs self.ema_fast = ema_fast self.ema_mid = ema_mid self.ema_slow = ema_slow self.rsi_period = rsi_period self.volume_spike_threshold = volume_spike_threshold def analyze(self, symbol: str, df: pd.DataFrame) -> Optional[ScalpingSignal]: """ Analyse et génère signal de scalping Args: symbol: Symbole du token df: DataFrame avec OHLCV Returns: ScalpingSignal ou None """ if len(df) < 50: return None # Calculer indicateurs df = self._calculate_indicators(df) current_price = df['close'].iloc[-1] # Détecter signal LONG long_signal = self._detect_long_signal(df) if long_signal: confidence = long_signal['confidence'] # SL/TP ultra serrés sl_price = current_price * (1 - self.sl_pct) tp_price = current_price * (1 + self.tp_pct) return ScalpingSignal( symbol=symbol, side='LONG', entry_price=current_price, stop_loss=sl_price, take_profit=tp_price, leverage=self.leverage, confidence=confidence, reason=long_signal['reason'] ) # Détecter signal SHORT short_signal = self._detect_short_signal(df) if short_signal: confidence = short_signal['confidence'] # SL/TP ultra serrés sl_price = current_price * (1 + self.sl_pct) tp_price = current_price * (1 - self.tp_pct) return ScalpingSignal( symbol=symbol, side='SHORT', entry_price=current_price, stop_loss=sl_price, take_profit=tp_price, leverage=self.leverage, confidence=confidence, reason=short_signal['reason'] ) return None def _calculate_indicators(self, df: pd.DataFrame) -> pd.DataFrame: """Calcule tous les indicateurs nécessaires""" df = df.copy() # EMAs rapides df['ema_fast'] = df['close'].ewm(span=self.ema_fast, adjust=False).mean() df['ema_mid'] = df['close'].ewm(span=self.ema_mid, adjust=False).mean() df['ema_slow'] = df['close'].ewm(span=self.ema_slow, adjust=False).mean() # RSI court delta = df['close'].diff() gain = (delta.where(delta > 0, 0)).rolling(window=self.rsi_period).mean() loss = (-delta.where(delta < 0, 0)).rolling(window=self.rsi_period).mean() rs = gain / loss df['rsi'] = 100 - (100 / (1 + rs)) # Volume ratio df['volume_ma'] = df['volume'].rolling(window=20).mean() df['volume_ratio'] = df['volume'] / df['volume_ma'] # Momentum (changement de prix sur 3 bougies) df['momentum_3'] = (df['close'] / df['close'].shift(3) - 1) * 100 return df def _detect_long_signal(self, df: pd.DataFrame) -> Optional[Dict]: """Détecte signal LONG scalping""" last = df.iloc[-1] prev = df.iloc[-2] confidence = 0 reasons = [] # 1. EMA Fast crossover Mid (haussier) if last['ema_fast'] > last['ema_mid'] and prev['ema_fast'] <= prev['ema_mid']: confidence += 30 reasons.append("EMA fast cross above mid") # 2. Prix au-dessus EMA slow (tendance haussière) if last['close'] > last['ema_slow']: confidence += 15 reasons.append("Above EMA slow") # 3. Momentum positif if last['momentum_3'] > 0: confidence += 20 reasons.append(f"Momentum +{last['momentum_3']:.2f}%") # 4. Volume spike if last['volume_ratio'] > self.volume_spike_threshold: confidence += 20 reasons.append(f"Volume spike {last['volume_ratio']:.1f}x") # 5. RSI dans zone favorable (30-70) if 30 < last['rsi'] < 70: confidence += 15 reasons.append(f"RSI {last['rsi']:.0f}") # Signal valide si confidence >= 60% if confidence >= 60: return { 'confidence': confidence, 'reason': ' | '.join(reasons) } return None def _detect_short_signal(self, df: pd.DataFrame) -> Optional[Dict]: """Détecte signal SHORT scalping""" last = df.iloc[-1] prev = df.iloc[-2] confidence = 0 reasons = [] # 1. EMA Fast crossover Mid (baissier) if last['ema_fast'] < last['ema_mid'] and prev['ema_fast'] >= prev['ema_mid']: confidence += 30 reasons.append("EMA fast cross below mid") # 2. Prix en-dessous EMA slow (tendance baissière) if last['close'] < last['ema_slow']: confidence += 15 reasons.append("Below EMA slow") # 3. Momentum négatif if last['momentum_3'] < 0: confidence += 20 reasons.append(f"Momentum {last['momentum_3']:.2f}%") # 4. Volume spike if last['volume_ratio'] > self.volume_spike_threshold: confidence += 20 reasons.append(f"Volume spike {last['volume_ratio']:.1f}x") # 5. RSI dans zone favorable (30-70) if 30 < last['rsi'] < 70: confidence += 15 reasons.append(f"RSI {last['rsi']:.0f}") # Signal valide si confidence >= 60% if confidence >= 60: return { 'confidence': confidence, 'reason': ' | '.join(reasons) } return None def get_optimizable_params(self) -> Dict: """Retourne paramètres optimisables""" return { 'leverage': (500, 1000), 'sl_pct': (0.02, 0.05), # 0.02%-0.05% 'tp_pct': (0.05, 0.15), # 0.05%-0.15% 'ema_fast': (3, 5), 'ema_mid': (7, 10), 'ema_slow': (15, 21), 'rsi_period': (5, 10), 'volume_spike_threshold': (1.3, 2.0) } if __name__ == '__main__': print("🎯 Stratégie de Scalping 1 Minute") print("=" * 80) print() print("Caractéristiques:") print(" • Leverage: 500x - 1000x") print(" • SL: 0.02% - 0.05% (ultra serré)") print(" • TP: 0.05% - 0.15% (petit mais régulier)") print(" • Win rate visé: 70%+") print() print("Indicateurs:") print(" • EMA 3/7/15 (crossovers rapides)") print(" • Momentum 3 bougies") print(" • Volume spike") print(" • RSI 7 périodes") print() print("Exemple avec leverage 1000x:") print(" • TP 0.10% prix = +100% capital") print(" • SL 0.03% prix = -30% capital") print(" • Ratio Risk/Reward: 3.3:1") print() strategy = Scalping1MStrategy(leverage=1000, sl_pct=0.03, tp_pct=0.10) print(f"Paramètres optimisables: {len(strategy.get_optimizable_params())}")