Initial commit — Trading AI Secure project complet
Architecture Docker (8 services), FastAPI, TimescaleDB, Redis, Streamlit. Stratégies : scalping, intraday, swing. MLEngine + RegimeDetector (HMM). BacktestEngine + WalkForwardAnalyzer + Optuna optimizer. Routes API complètes dont /optimize async. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Tika
385
src/strategies/scalping/scalping_strategy.py
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385
src/strategies/scalping/scalping_strategy.py
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"""
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Scalping Strategy - Stratégie de Scalping Mean Reversion.
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Cette stratégie exploite les micro-mouvements du marché en utilisant
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le retour à la moyenne sur des timeframes très courts (1-5 minutes).
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Indicateurs:
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- Bollinger Bands: Détection zones oversold/overbought
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- RSI: Confirmation conditions extrêmes
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- MACD: Validation momentum reversal
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- Volume: Confirmation force du mouvement
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- ATR: Calcul stop-loss/take-profit dynamiques
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Conditions LONG:
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- Prix proche Bollinger Band inférieure (< 20%)
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- RSI < 30 (oversold)
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- MACD histogram positif (reversal)
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- Volume > 1.5x moyenne
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- Confiance >= seuil minimum
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Conditions SHORT:
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- Prix proche Bollinger Band supérieure (> 80%)
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- RSI > 70 (overbought)
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- MACD histogram négatif (reversal)
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- Volume > 1.5x moyenne
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- Confiance >= seuil minimum
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"""
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from typing import Optional
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import pandas as pd
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import numpy as np
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from datetime import datetime
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import logging
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from src.strategies.base_strategy import BaseStrategy, Signal
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logger = logging.getLogger(__name__)
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class ScalpingStrategy(BaseStrategy):
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"""
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Stratégie de scalping basée sur mean reversion.
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Timeframe: 1-5 minutes
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Holding time: 5-30 minutes
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Risk per trade: 0.5-1%
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Win rate target: 60-70%
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Profit target: 0.3-0.5% par trade
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Usage:
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strategy = ScalpingStrategy(config)
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signal = strategy.analyze(market_data)
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"""
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def __init__(self, config: dict):
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"""
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Initialise la stratégie de scalping.
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Args:
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config: Configuration de la stratégie
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"""
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# Aligner risk_per_trade avec la limite RiskManager pour scalping (0.5%)
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config.setdefault('risk_per_trade', 0.005)
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super().__init__(config)
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# Paramètres par défaut si non fournis
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self.parameters.setdefault('bb_period', 20)
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self.parameters.setdefault('bb_std', 2.0)
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self.parameters.setdefault('rsi_period', 14)
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self.parameters.setdefault('rsi_oversold', 30)
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self.parameters.setdefault('rsi_overbought', 70)
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self.parameters.setdefault('volume_threshold', 1.5)
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self.parameters.setdefault('min_confidence', 0.65)
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logger.info(f"Scalping Strategy initialized with params: {self.parameters}")
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def calculate_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
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"""
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Calcule tous les indicateurs nécessaires pour le scalping.
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Args:
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data: DataFrame avec colonnes OHLCV
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Returns:
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DataFrame avec indicateurs ajoutés
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"""
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df = data.copy()
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# Bollinger Bands
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bb_period = int(self.parameters['bb_period'])
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bb_std = float(self.parameters['bb_std'])
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df['bb_middle'] = df['close'].rolling(bb_period).mean()
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df['bb_std'] = df['close'].rolling(bb_period).std()
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df['bb_upper'] = df['bb_middle'] + (bb_std * df['bb_std'])
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df['bb_lower'] = df['bb_middle'] - (bb_std * df['bb_std'])
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# Position dans les Bollinger Bands (0 = lower, 1 = upper)
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df['bb_position'] = (df['close'] - df['bb_lower']) / (df['bb_upper'] - df['bb_lower'])
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# RSI (Relative Strength Index)
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rsi_period = int(self.parameters['rsi_period'])
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delta = df['close'].diff()
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gain = delta.where(delta > 0, 0).rolling(rsi_period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(rsi_period).mean()
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rs = gain / loss
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df['rsi'] = 100 - (100 / (1 + rs))
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# MACD (Moving Average Convergence Divergence)
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df['ema_12'] = df['close'].ewm(span=12, adjust=False).mean()
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df['ema_26'] = df['close'].ewm(span=26, adjust=False).mean()
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df['macd'] = df['ema_12'] - df['ema_26']
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df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean()
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df['macd_hist'] = df['macd'] - df['macd_signal']
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# Volume (désactivé si données volume non fiables, ex: forex Yahoo Finance)
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df['volume_ma'] = df['volume'].rolling(20).mean()
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if df['volume'].sum() == 0:
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df['volume_ratio'] = 2.0 # Volume fictif >= seuil pour ne pas bloquer
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else:
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df['volume_ratio'] = df['volume'] / df['volume_ma']
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# ATR (Average True Range) pour stop-loss/take-profit
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df['high_low'] = df['high'] - df['low']
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df['high_close'] = abs(df['high'] - df['close'].shift(1))
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df['low_close'] = abs(df['low'] - df['close'].shift(1))
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df['tr'] = df[['high_low', 'high_close', 'low_close']].max(axis=1)
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df['atr'] = df['tr'].rolling(14).mean()
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return df
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def analyze(self, market_data: pd.DataFrame) -> Optional[Signal]:
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"""
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Analyse le marché et génère un signal de scalping.
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Args:
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market_data: DataFrame avec données OHLCV
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Returns:
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Signal si opportunité détectée, None sinon
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"""
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# Calculer indicateurs
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df = self.calculate_indicators(market_data)
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# Besoin d'au moins 50 barres pour indicateurs fiables
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if len(df) < 50:
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logger.debug("Not enough data for analysis")
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return None
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# Données actuelles et précédentes
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current = df.iloc[-1]
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prev = df.iloc[-2]
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# Vérifier que tous les indicateurs sont calculés
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if pd.isna(current['bb_position']) or pd.isna(current['rsi']) or pd.isna(current['macd_hist']):
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logger.debug("Indicators not fully calculated")
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return None
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# Vérifier volume suffisant
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if current['volume_ratio'] < self.parameters['volume_threshold']:
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logger.debug(f"Volume too low: {current['volume_ratio']:.2f}")
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return None
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# Détecter signal LONG (oversold reversal)
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if self._check_long_conditions(current, prev):
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confidence = self._calculate_confidence(df, 'LONG')
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if confidence >= self.parameters['min_confidence']:
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return self._create_long_signal(current, confidence)
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# Détecter signal SHORT (overbought reversal)
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elif self._check_short_conditions(current, prev):
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confidence = self._calculate_confidence(df, 'SHORT')
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if confidence >= self.parameters['min_confidence']:
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return self._create_short_signal(current, confidence)
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return None
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def _check_long_conditions(self, current: pd.Series, prev: pd.Series) -> bool:
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"""
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Vérifie les conditions pour un signal LONG.
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Args:
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current: Barre actuelle
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prev: Barre précédente
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Returns:
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True si conditions remplies
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"""
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return (
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# Prix proche Bollinger Band inférieure
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current['bb_position'] < 0.2 and
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# RSI oversold
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current['rsi'] < self.parameters['rsi_oversold'] and
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# MACD momentum haussier (histogram en hausse — pas besoin de croiser zéro)
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current['macd_hist'] > prev['macd_hist'] and
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# Volume confirmation
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current['volume_ratio'] > self.parameters['volume_threshold']
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)
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def _check_short_conditions(self, current: pd.Series, prev: pd.Series) -> bool:
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"""
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Vérifie les conditions pour un signal SHORT.
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Args:
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current: Barre actuelle
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prev: Barre précédente
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Returns:
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True si conditions remplies
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"""
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return (
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# Prix proche Bollinger Band supérieure
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current['bb_position'] > 0.8 and
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# RSI overbought
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current['rsi'] > self.parameters['rsi_overbought'] and
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# MACD momentum baissier (histogram en baisse — pas besoin de croiser zéro)
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current['macd_hist'] < prev['macd_hist'] and
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# Volume confirmation
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current['volume_ratio'] > self.parameters['volume_threshold']
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)
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def _create_long_signal(self, current: pd.Series, confidence: float) -> Signal:
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"""
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Crée un signal LONG.
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Args:
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current: Barre actuelle
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confidence: Confiance du signal
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Returns:
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Signal LONG
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"""
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entry_price = current['close']
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atr = current['atr']
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# Stop-loss à 2 ATR en dessous
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stop_loss = entry_price - (2.0 * atr)
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# Take-profit à 3 ATR au-dessus (R:R 1.5:1)
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take_profit = entry_price + (3.0 * atr)
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signal = Signal(
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symbol=current.name if hasattr(current, 'name') else 'UNKNOWN',
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direction='LONG',
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entry_price=entry_price,
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stop_loss=stop_loss,
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take_profit=take_profit,
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confidence=confidence,
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timestamp=datetime.now(),
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strategy='scalping',
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metadata={
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'rsi': float(current['rsi']),
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'bb_position': float(current['bb_position']),
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'macd_hist': float(current['macd_hist']),
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'volume_ratio': float(current['volume_ratio']),
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'atr': float(atr)
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}
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)
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logger.info(f"LONG signal generated - Confidence: {confidence:.2%}")
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return signal
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def _create_short_signal(self, current: pd.Series, confidence: float) -> Signal:
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"""
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Crée un signal SHORT.
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Args:
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current: Barre actuelle
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confidence: Confiance du signal
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Returns:
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Signal SHORT
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"""
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entry_price = current['close']
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atr = current['atr']
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# Stop-loss à 2 ATR au-dessus
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stop_loss = entry_price + (2.0 * atr)
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# Take-profit à 3 ATR en dessous (R:R 1.5:1)
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take_profit = entry_price - (3.0 * atr)
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signal = Signal(
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symbol=current.name if hasattr(current, 'name') else 'UNKNOWN',
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direction='SHORT',
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entry_price=entry_price,
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stop_loss=stop_loss,
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take_profit=take_profit,
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confidence=confidence,
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timestamp=datetime.now(),
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strategy='scalping',
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metadata={
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'rsi': float(current['rsi']),
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'bb_position': float(current['bb_position']),
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'macd_hist': float(current['macd_hist']),
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'volume_ratio': float(current['volume_ratio']),
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'atr': float(atr)
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}
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)
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logger.info(f"SHORT signal generated - Confidence: {confidence:.2%}")
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return signal
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def _calculate_confidence(self, df: pd.DataFrame, direction: str) -> float:
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"""
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Calcule la confiance du signal (0.0 à 1.0).
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Facteurs:
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- Force de l'oversold/overbought (RSI)
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- Position dans Bollinger Bands
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- Force du volume
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- Historique win rate
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Args:
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df: DataFrame avec indicateurs
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direction: 'LONG' ou 'SHORT'
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Returns:
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Confiance entre 0.0 et 1.0
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"""
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current = df.iloc[-1]
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# Confiance de base
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confidence = 0.5
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if direction == 'LONG':
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# Force RSI oversold (plus c'est bas, plus c'est fort)
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rsi_strength = (30 - current['rsi']) / 30
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confidence += 0.2 * max(0, rsi_strength)
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# Position Bollinger Bands (plus c'est bas, plus c'est fort)
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bb_strength = (0.2 - current['bb_position']) / 0.2
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confidence += 0.15 * max(0, bb_strength)
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else: # SHORT
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# Force RSI overbought (plus c'est haut, plus c'est fort)
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rsi_strength = (current['rsi'] - 70) / 30
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confidence += 0.2 * max(0, rsi_strength)
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# Position Bollinger Bands (plus c'est haut, plus c'est fort)
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bb_strength = (current['bb_position'] - 0.8) / 0.2
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confidence += 0.15 * max(0, bb_strength)
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# Force du volume
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volume_strength = min((current['volume_ratio'] - 1.5) / 1.5, 1.0)
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confidence += 0.15 * max(0, volume_strength)
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# Historique win rate (bonus si bonne performance)
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if self.win_rate > 0.5:
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confidence += 0.1 * (self.win_rate - 0.5)
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# Limiter entre 0 et 1
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return np.clip(confidence, 0.0, 1.0)
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def get_strategy_info(self) -> dict:
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"""
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Retourne les informations de la stratégie.
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Returns:
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Dictionnaire avec informations
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"""
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return {
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'name': 'Scalping Mean Reversion',
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'type': 'scalping',
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'timeframe': '1-5min',
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'indicators': ['Bollinger Bands', 'RSI', 'MACD', 'Volume', 'ATR'],
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'risk_per_trade': '0.5-1%',
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'target_win_rate': '60-70%',
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'target_profit': '0.3-0.5%',
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'parameters': self.parameters,
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'statistics': self.get_statistics()
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}
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