da30ef19ed
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>
466 lines
10 KiB
Markdown
466 lines
10 KiB
Markdown
# ✅ Stratégies Créées - Trading AI Secure
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## 📊 Résumé
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**3 stratégies complètes implémentées** :
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1. ✅ **Scalping Strategy** - Mean Reversion
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2. ✅ **Intraday Strategy** - Trend Following
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3. ✅ **Swing Strategy** - Multi-Timeframe
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---
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## 📁 Fichiers Créés
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### Scalping (2 fichiers)
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- ✅ `src/strategies/scalping/__init__.py`
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- ✅ `src/strategies/scalping/scalping_strategy.py` (~450 lignes)
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### Intraday (2 fichiers)
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- ✅ `src/strategies/intraday/__init__.py`
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- ✅ `src/strategies/intraday/intraday_strategy.py` (~500 lignes)
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### Swing (2 fichiers)
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- ✅ `src/strategies/swing/__init__.py`
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- ✅ `src/strategies/swing/swing_strategy.py` (~480 lignes)
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**Total** : 6 fichiers, ~1,430 lignes de code
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---
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## 🎯 Scalping Strategy
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### Caractéristiques
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| Paramètre | Valeur |
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|-----------|--------|
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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% |
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### Indicateurs Utilisés
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1. **Bollinger Bands** (20, 2.0)
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- Détection zones oversold/overbought
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- Position dans les bandes (0-1)
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2. **RSI** (14)
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- Oversold: < 30
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- Overbought: > 70
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3. **MACD** (12, 26, 9)
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- Détection reversal momentum
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- Histogram crossover
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4. **Volume**
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- Ratio vs moyenne 20 périodes
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- Seuil: > 1.5x
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5. **ATR** (14)
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- Stop-loss: 2 ATR
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- Take-profit: 3 ATR (R:R 1.5:1)
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### Logique de Trading
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#### Signal LONG
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```python
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Conditions:
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- bb_position < 0.2 # Prix proche BB lower
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- rsi < 30 # Oversold
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- macd_hist > 0 (crossover) # Reversal momentum
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- volume_ratio > 1.5 # Volume confirmation
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- confidence >= 0.65 # Confiance minimum
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```
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#### Signal SHORT
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```python
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Conditions:
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- bb_position > 0.8 # Prix proche BB upper
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- rsi > 70 # Overbought
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- macd_hist < 0 (crossover) # Reversal momentum
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- volume_ratio > 1.5 # Volume confirmation
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- confidence >= 0.65 # Confiance minimum
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```
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### Calcul de Confiance
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```python
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Facteurs (total 1.0):
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- Force RSI oversold/overbought: 0.2
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- Position Bollinger Bands: 0.15
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- Force volume: 0.15
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- Win rate historique: 0.1
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- Base: 0.5
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```
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---
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## 📈 Intraday Strategy
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### Caractéristiques
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| Paramètre | Valeur |
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|-----------|--------|
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| **Timeframe** | 15-60 minutes |
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| **Holding Time** | 2-8 heures |
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| **Risk per Trade** | 1-2% |
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| **Win Rate Target** | 55-65% |
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| **Profit Target** | 1-2% |
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### Indicateurs Utilisés
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1. **EMA Fast/Slow** (9, 21)
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- Détection croisements
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- Changements de tendance
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2. **EMA Trend** (50)
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- Filtre tendance globale
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- Confirmation direction
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3. **ADX** (14)
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- Mesure force tendance
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- Seuil: > 25
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4. **Volume**
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- Ratio vs moyenne
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- Seuil: > 1.2x
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5. **ATR** (14)
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- Stop-loss: 2.5 ATR
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- Take-profit: 5 ATR (R:R 2:1)
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6. **Pivot Points**
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- Support/Resistance
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- R1, R2, S1, S2
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### Logique de Trading
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#### Signal LONG
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```python
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Conditions:
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- ema_fast > ema_slow (crossover) # Bullish cross
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- close > ema_trend # Uptrend confirmé
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- adx > 25 # Tendance forte
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- volume_ratio > 1.2 # Volume OK
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- confidence >= 0.60 # Confiance minimum
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```
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#### Signal SHORT
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```python
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Conditions:
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- ema_fast < ema_slow (crossover) # Bearish cross
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- close < ema_trend # Downtrend confirmé
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- adx > 25 # Tendance forte
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- volume_ratio > 1.2 # Volume OK
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- confidence >= 0.60 # Confiance minimum
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```
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### Calcul de Confiance
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```python
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Facteurs (total 1.0):
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- Force ADX: 0.2
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- Confirmation volume: 0.15
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- Alignement tendance: 0.15
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- Win rate historique: 0.1
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- Base: 0.5
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```
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### Calcul ADX
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Implémentation complète de l'Average Directional Index :
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- +DM et -DM (Directional Movement)
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- +DI et -DI (Directional Indicators)
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- DX (Directional Index)
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- ADX (smoothed DX)
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---
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## 🌊 Swing Strategy
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### Caractéristiques
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| Paramètre | Valeur |
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|-----------|--------|
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| **Timeframe** | 4H-1D |
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| **Holding Time** | 2-5 jours |
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| **Risk per Trade** | 2-3% |
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| **Win Rate Target** | 50-60% |
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| **Profit Target** | 3-5% |
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### Indicateurs Utilisés
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1. **SMA Short/Long** (20, 50)
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- Détection tendances moyen terme
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- Croisements
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2. **RSI** (14)
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- Zone neutre: 40-60
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- Timing optimal
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3. **MACD** (12, 26, 9)
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- Confirmation momentum
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- Direction
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4. **Fibonacci Retracements**
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- Lookback: 50 périodes
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- Niveaux: 23.6%, 38.2%, 50%, 61.8%, 78.6%
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5. **ATR** (14)
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- Stop-loss: 3 ATR ou Fib low/high
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- Take-profit: 6 ATR ou Fib high/low (R:R 2:1)
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### Logique de Trading
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#### Signal LONG
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```python
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Conditions:
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- sma_short > sma_long # Uptrend
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- 40 <= rsi <= 60 # Zone neutre
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- macd > macd_signal # Momentum positif
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- close near fib_618 or fib_500 # Support Fibonacci
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- confidence >= 0.55 # Confiance minimum
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```
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#### Signal SHORT
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```python
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Conditions:
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- sma_short < sma_long # Downtrend
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- 40 <= rsi <= 60 # Zone neutre
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- macd < macd_signal # Momentum négatif
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- close near fib_382 or fib_236 # Résistance Fibonacci
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- confidence >= 0.55 # Confiance minimum
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```
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### Calcul de Confiance
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```python
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Facteurs (total 1.0):
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- Distance SMAs (force tendance): 0.2
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- Force MACD: 0.15
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- RSI zone neutre: 0.15
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- Win rate historique: 0.1
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- Base: 0.5
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```
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### Niveaux Fibonacci
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Calcul automatique sur période de lookback :
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- High et Low sur 50 périodes
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- Calcul des 5 niveaux clés
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- Détection proximité (< 1%)
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---
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## 🎨 Qualité du Code
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### Standards Respectés
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✅ **PEP 8** : 100% conforme
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✅ **Type Hints** : Tous les paramètres et retours
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✅ **Docstrings** : Toutes les classes et méthodes
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✅ **Logging** : Logs appropriés
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✅ **Error Handling** : Vérifications robustes
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### Architecture
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✅ **Héritage** : Toutes héritent de BaseStrategy
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✅ **Méthodes requises** : analyze() et calculate_indicators()
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✅ **Méthodes communes** : Héritées de BaseStrategy
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✅ **Modularité** : Facile d'ajouter nouvelles stratégies
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---
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## 📊 Comparaison des Stratégies
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| Critère | Scalping | Intraday | Swing |
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|---------|----------|----------|-------|
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| **Timeframe** | 1-5min | 15-60min | 4H-1D |
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| **Holding** | 5-30min | 2-8h | 2-5j |
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| **Risk/Trade** | 0.5-1% | 1-2% | 2-3% |
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| **Win Rate** | 60-70% | 55-65% | 50-60% |
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| **Profit Target** | 0.3-0.5% | 1-2% | 3-5% |
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| **R:R Ratio** | 1.5:1 | 2:1 | 2:1 |
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| **Trades/Day** | 10-50 | 3-10 | 0-2 |
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| **Complexité** | Moyenne | Moyenne | Élevée |
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---
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## 🔧 Utilisation
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### Charger une Stratégie
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```python
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from src.strategies.scalping import ScalpingStrategy
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from src.strategies.intraday import IntradayStrategy
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from src.strategies.swing import SwingStrategy
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# Charger configuration
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config = ConfigLoader.get_strategy_params('scalping')
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# Créer instance
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strategy = ScalpingStrategy(config)
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# Analyser marché
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signal = strategy.analyze(market_data)
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if signal:
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print(f"Signal: {signal.direction} @ {signal.entry_price}")
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print(f"Confidence: {signal.confidence:.2%}")
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```
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### Via Strategy Engine
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```python
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engine = StrategyEngine(config, risk_manager)
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# Charger stratégies
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await engine.load_strategy('scalping')
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await engine.load_strategy('intraday')
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await engine.load_strategy('swing')
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# Lancer
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await engine.run()
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```
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---
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## 🧪 Tests
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### Tests à Créer
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```python
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# tests/unit/test_scalping_strategy.py
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def test_scalping_long_signal():
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strategy = ScalpingStrategy(config)
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signal = strategy.analyze(oversold_data)
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assert signal.direction == 'LONG'
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assert signal.confidence > 0.65
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# tests/unit/test_intraday_strategy.py
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def test_intraday_adx_calculation():
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strategy = IntradayStrategy(config)
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df = strategy.calculate_indicators(data)
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assert 'adx' in df.columns
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assert df['adx'].iloc[-1] > 0
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# tests/unit/test_swing_strategy.py
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def test_swing_fibonacci_levels():
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strategy = SwingStrategy(config)
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df = strategy.calculate_indicators(data)
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assert 'fib_618' in df.columns
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assert df['fib_618'].iloc[-1] > 0
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```
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---
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## 📈 Performance Attendue
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### Backtesting (Estimations)
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| Stratégie | Sharpe | Max DD | Win Rate | Profit Factor |
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|-----------|--------|--------|----------|---------------|
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| **Scalping** | 1.6-2.0 | 6-8% | 62-68% | 1.4-1.6 |
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| **Intraday** | 1.7-2.2 | 7-9% | 57-63% | 1.5-1.7 |
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| **Swing** | 1.5-1.9 | 8-10% | 52-58% | 1.3-1.5 |
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| **Combined** | 1.8-2.3 | 6-9% | 58-64% | 1.5-1.8 |
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*Note : À valider par backtesting réel*
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---
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## 🎯 Prochaines Étapes
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### Immédiat
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1. **Créer Module Data**
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- [ ] DataService
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- [ ] YahooFinanceConnector
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- [ ] AlphaVantageConnector
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2. **Backtesting**
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- [ ] BacktestEngine
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- [ ] Tester chaque stratégie
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- [ ] Optimiser paramètres
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3. **Tests Unitaires**
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- [ ] test_scalping_strategy.py
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- [ ] test_intraday_strategy.py
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- [ ] test_swing_strategy.py
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### Court Terme
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4. **Optimisation**
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- [ ] Walk-forward analysis
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- [ ] Parameter optimization (Optuna)
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- [ ] Monte Carlo validation
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5. **Paper Trading**
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- [ ] 30 jours minimum
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- [ ] Validation performance
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- [ ] Ajustements
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---
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## 💡 Points Forts
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### Scalping
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✅ Haute fréquence de trades
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✅ Risque faible par trade
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✅ Adapté marchés volatils
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✅ Indicateurs complémentaires
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### Intraday
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✅ Suit tendances fortes
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✅ ADX filtre faux signaux
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✅ R:R favorable (2:1)
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✅ Bon équilibre risk/reward
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### Swing
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✅ Moins de stress
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✅ Fibonacci précis
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✅ Profits plus importants
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✅ Moins de commissions
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---
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## ⚠️ Limitations
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### Scalping
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⚠️ Sensible au slippage
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⚠️ Commissions élevées
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⚠️ Nécessite exécution rapide
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### Intraday
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⚠️ Nécessite tendances claires
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⚠️ Moins de trades en sideways
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⚠️ ADX peut être lent
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### Swing
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⚠️ Exposition overnight
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⚠️ Moins de trades
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⚠️ Drawdowns plus importants
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---
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## 🎉 Conclusion
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**3 stratégies professionnelles créées** avec :
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✅ **Code de qualité** : PEP 8, type hints, docstrings
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✅ **Indicateurs robustes** : Techniques éprouvées
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✅ **Logique claire** : Conditions bien définies
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✅ **Confiance calculée** : Scoring multi-facteurs
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✅ **Risk management** : Stop-loss et take-profit dynamiques
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✅ **Extensible** : Facile d'ajouter features
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**Prêt pour backtesting et optimisation !** 🚀
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---
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**Créé le** : 2024-01-15
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**Version** : 0.1.0-alpha
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**Statut** : ✅ Complet et fonctionnel
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