trader-ml/docs/RISK_FRAMEWORK.md

⚠️ Framework de Risk Management - Trading AI Secure

📋 Table des Matières

1. Philosophie du Risk Management
2. Architecture Multi-Niveaux
3. Limites et Contraintes
4. Risk Manager Core
5. Validation Pré-Trade
6. Circuit Breakers
7. Métriques de Risque
8. Implémentation Technique

---

🎯 Philosophie du Risk Management

Principe Fondamental : "Survivre d'abord, Profiter ensuite"

Le risk management est intégré à tous les niveaux du système, pas une couche ajoutée après coup.

┌─────────────────────────────────────────────────────────┐
│         HIÉRARCHIE DES PRIORITÉS                        │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  1. 🛡️  PROTECTION DU CAPITAL (Priorité absolue)       │
│  2. 📉  LIMITATION DES PERTES (Drawdown < 10%)         │
│  3. 🎯  CONSISTANCE (Sharpe > volatilité)              │
│  4. 💰  PROFITABILITÉ (Objectif final)                 │
│                                                         │
└─────────────────────────────────────────────────────────┘

Règles d'Or

1. Jamais de trade sans stop-loss ❌
2. Jamais plus de 2% du capital en risque total ❌
3. Jamais plus de 10% de drawdown ❌
4. Toujours vérifier la corrélation ✅
5. Toujours valider la liquidité ✅

---

🏗️ Architecture Multi-Niveaux

5 Niveaux de Protection

┌──────────────────────────────────────────────────────────┐
│  NIVEAU 5: GLOBAL PORTFOLIO RISK                         │
│  ├─ Max total risk: 2% du capital                        │
│  ├─ Max drawdown: 10%                                    │
│  └─ Daily loss limit: 3%                                 │
├──────────────────────────────────────────────────────────┤
│  NIVEAU 4: STRATEGY ALLOCATION                           │
│  ├─ Max per strategy: 33% du capital                     │
│  ├─ Correlation limit: 0.7 entre stratégies              │
│  └─ Min diversification: 3 stratégies actives            │
├──────────────────────────────────────────────────────────┤
│  NIVEAU 3: POSITION SIZING                               │
│  ├─ Kelly Criterion adaptatif                            │
│  ├─ Max position: 5% du capital                          │
│  └─ Volatility-adjusted sizing                           │
├──────────────────────────────────────────────────────────┤
│  NIVEAU 2: TRADE VALIDATION                              │
│  ├─ Stop-loss obligatoire                                │
│  ├─ Risk/Reward > 1.5                                    │
│  ├─ Liquidity check                                      │
│  └─ Margin verification                                  │
├──────────────────────────────────────────────────────────┤
│  NIVEAU 1: CIRCUIT BREAKERS                              │
│  ├─ Emergency stop                                       │
│  ├─ Volatility spike detection                           │
│  ├─ Flash crash protection                               │
│  └─ API failure handling                                 │
└──────────────────────────────────────────────────────────┘

---

📊 Limites et Contraintes

Limites Globales (Portfolio)

global_limits:
  # Capital
  max_portfolio_risk: 0.02              # 2% du capital total en risque
  max_position_size: 0.05               # 5% max par position
  max_total_exposure: 1.0               # 100% du capital (pas de levier)
  
  # Drawdown
  max_drawdown: 0.10                    # 10% drawdown maximum
  max_daily_loss: 0.03                  # 3% perte journalière max
  max_weekly_loss: 0.07                 # 7% perte hebdomadaire max
  
  # Corrélation
  max_correlation: 0.7                  # Corrélation max entre positions
  min_diversification: 3                # Minimum 3 positions non-corrélées
  
  # Liquidité
  min_daily_volume: 1000000             # 1M$ volume quotidien minimum
  max_position_vs_volume: 0.01          # Max 1% du volume quotidien
  
  # Concentration
  max_sector_exposure: 0.30             # 30% max par secteur
  max_asset_class_exposure: 0.50        # 50% max par classe d'actif

Limites par Stratégie

strategy_limits:
  scalping:
    max_trades_per_day: 50
    risk_per_trade: 0.005               # 0.5% par trade
    max_holding_time: 1800              # 30 minutes
    max_slippage: 0.001                 # 0.1% slippage max
    min_profit_target: 0.003            # 0.3% profit minimum
    
  intraday:
    max_trades_per_day: 10
    risk_per_trade: 0.015               # 1.5% par trade
    max_holding_time: 86400             # 1 jour
    max_slippage: 0.002                 # 0.2% slippage max
    min_profit_target: 0.01             # 1% profit minimum
    
  swing:
    max_trades_per_week: 5
    risk_per_trade: 0.025               # 2.5% par trade
    max_holding_time: 432000            # 5 jours
    max_slippage: 0.003                 # 0.3% slippage max
    min_profit_target: 0.03             # 3% profit minimum

Limites Dynamiques (Ajustées selon conditions)

class DynamicLimits:
    """
    Limites ajustées selon conditions de marché
    """
    
    def adjust_limits(self, market_conditions: Dict) -> Dict:
        """
        Ajuste limites selon volatilité, drawdown, etc.
        """
        base_limits = self.get_base_limits()
        
        # Réduire limites si haute volatilité
        if market_conditions['volatility'] > 0.03:  # > 3% vol quotidienne
            base_limits['max_position_size'] *= 0.5
            base_limits['max_portfolio_risk'] *= 0.7
            
        # Réduire limites si drawdown élevé
        if market_conditions['current_drawdown'] > 0.05:  # > 5% DD
            reduction_factor = 1 - (market_conditions['current_drawdown'] / 0.10)
            base_limits['max_position_size'] *= reduction_factor
            
        # Réduire limites si losing streak
        if market_conditions['consecutive_losses'] > 3:
            base_limits['max_trades_per_day'] //= 2
            base_limits['risk_per_trade'] *= 0.5
            
        return base_limits

---

🛡️ Risk Manager Core

Singleton Pattern

# src/core/risk_manager.py

from typing import Dict, List, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
import threading
import numpy as np

@dataclass
class Position:
    """Représente une position ouverte"""
    symbol: str
    quantity: float
    entry_price: float
    current_price: float
    stop_loss: float
    take_profit: float
    strategy: str
    entry_time: datetime
    unrealized_pnl: float
    risk_amount: float

@dataclass
class RiskMetrics:
    """Métriques de risque en temps réel"""
    total_risk: float
    current_drawdown: float
    daily_pnl: float
    weekly_pnl: float
    portfolio_var: float
    portfolio_cvar: float
    correlation_matrix: np.ndarray
    largest_position: float
    num_positions: int

class RiskManager:
    """
    Risk Manager Central (Singleton)
    
    Responsabilités:
    - Validation pré-trade
    - Monitoring positions
    - Circuit breakers
    - Calcul métriques risque
    """
    
    _instance = None
    _lock = threading.Lock()
    
    def __new__(cls):
        if cls._instance is None:
            with cls._lock:
                if cls._instance is None:
                    cls._instance = super().__new__(cls)
        return cls._instance
    
    def __init__(self):
        if not hasattr(self, 'initialized'):
            self.initialized = True
            
            # Configuration
            self.config = self._load_config()
            
            # État
            self.positions: Dict[str, Position] = {}
            self.daily_trades: List[Dict] = []
            self.portfolio_value: float = 100000.0  # Initial capital
            self.peak_value: float = 100000.0
            
            # Historique
            self.pnl_history: List[float] = []
            self.drawdown_history: List[float] = []
            
            # Circuit breakers
            self.trading_halted: bool = False
            self.halt_reason: Optional[str] = None
            
    def validate_trade(
        self,
        symbol: str,
        quantity: float,
        price: float,
        stop_loss: float,
        take_profit: float,
        strategy: str
    ) -> tuple[bool, Optional[str]]:
        """
        Valide un trade avant exécution
        
        Returns:
            (is_valid, error_message)
        """
        # 1. Vérifier si trading halted
        if self.trading_halted:
            return False, f"Trading halted: {self.halt_reason}"
        
        # 2. Vérifier stop-loss obligatoire
        if stop_loss is None or stop_loss == 0:
            return False, "Stop-loss is mandatory"
        
        # 3. Calculer risque du trade
        risk_amount = abs(price - stop_loss) * quantity
        risk_pct = risk_amount / self.portfolio_value
        
        # 4. Vérifier limites par trade
        max_risk_per_trade = self.config['strategy_limits'][strategy]['risk_per_trade']
        if risk_pct > max_risk_per_trade:
            return False, f"Risk per trade ({risk_pct:.2%}) exceeds limit ({max_risk_per_trade:.2%})"
        
        # 5. Vérifier risque total portfolio
        total_risk = self._calculate_total_risk() + risk_amount
        max_portfolio_risk = self.config['global_limits']['max_portfolio_risk'] * self.portfolio_value
        
        if total_risk > max_portfolio_risk:
            return False, f"Total portfolio risk ({total_risk:.2f}) exceeds limit ({max_portfolio_risk:.2f})"
        
        # 6. Vérifier taille position
        position_value = price * quantity
        position_pct = position_value / self.portfolio_value
        max_position_size = self.config['global_limits']['max_position_size']
        
        if position_pct > max_position_size:
            return False, f"Position size ({position_pct:.2%}) exceeds limit ({max_position_size:.2%})"
        
        # 7. Vérifier corrélation
        if not self._check_correlation(symbol, strategy):
            return False, "Correlation with existing positions too high"
        
        # 8. Vérifier nombre de trades quotidiens
        strategy_trades_today = len([t for t in self.daily_trades if t['strategy'] == strategy])
        max_trades = self.config['strategy_limits'][strategy]['max_trades_per_day']
        
        if strategy_trades_today >= max_trades:
            return False, f"Max daily trades for {strategy} reached ({max_trades})"
        
        # 9. Vérifier Risk/Reward ratio
        risk = abs(price - stop_loss)
        reward = abs(take_profit - price)
        rr_ratio = reward / risk if risk > 0 else 0
        
        if rr_ratio < 1.5:
            return False, f"Risk/Reward ratio ({rr_ratio:.2f}) below minimum (1.5)"
        
        # 10. Vérifier drawdown actuel
        current_dd = self._calculate_current_drawdown()
        max_dd = self.config['global_limits']['max_drawdown']
        
        if current_dd >= max_dd:
            return False, f"Max drawdown reached ({current_dd:.2%})"
        
        # Toutes validations passées
        return True, None
    
    def add_position(self, position: Position):
        """Ajoute une position au portfolio"""
        self.positions[position.symbol] = position
        
        # Enregistrer trade
        self.daily_trades.append({
            'symbol': position.symbol,
            'strategy': position.strategy,
            'time': position.entry_time,
            'risk': position.risk_amount
        })
        
    def update_position(self, symbol: str, current_price: float):
        """Met à jour prix d'une position"""
        if symbol in self.positions:
            position = self.positions[symbol]
            position.current_price = current_price
            position.unrealized_pnl = (current_price - position.entry_price) * position.quantity
            
            # Vérifier stop-loss / take-profit
            self._check_exit_conditions(position)
    
    def close_position(self, symbol: str, exit_price: float) -> float:
        """Ferme une position et retourne P&L"""
        if symbol not in self.positions:
            return 0.0
        
        position = self.positions[symbol]
        pnl = (exit_price - position.entry_price) * position.quantity
        
        # Mettre à jour portfolio
        self.portfolio_value += pnl
        self.pnl_history.append(pnl)
        
        # Mettre à jour peak
        if self.portfolio_value > self.peak_value:
            self.peak_value = self.portfolio_value
        
        # Supprimer position
        del self.positions[symbol]
        
        return pnl
    
    def get_risk_metrics(self) -> RiskMetrics:
        """Calcule métriques de risque en temps réel"""
        return RiskMetrics(
            total_risk=self._calculate_total_risk(),
            current_drawdown=self._calculate_current_drawdown(),
            daily_pnl=self._calculate_daily_pnl(),
            weekly_pnl=self._calculate_weekly_pnl(),
            portfolio_var=self._calculate_var(),
            portfolio_cvar=self._calculate_cvar(),
            correlation_matrix=self._calculate_correlation_matrix(),
            largest_position=self._get_largest_position(),
            num_positions=len(self.positions)
        )
    
    def _calculate_total_risk(self) -> float:
        """Calcule risque total du portfolio"""
        return sum(pos.risk_amount for pos in self.positions.values())
    
    def _calculate_current_drawdown(self) -> float:
        """Calcule drawdown actuel"""
        if self.peak_value == 0:
            return 0.0
        return (self.peak_value - self.portfolio_value) / self.peak_value
    
    def _calculate_daily_pnl(self) -> float:
        """Calcule P&L du jour"""
        today = datetime.now().date()
        daily_pnl = sum(
            pnl for pnl, time in zip(self.pnl_history, self.daily_trades)
            if time['time'].date() == today
        )
        
        # Ajouter unrealized P&L
        unrealized = sum(pos.unrealized_pnl for pos in self.positions.values())
        
        return daily_pnl + unrealized
    
    def _calculate_var(self, confidence=0.95) -> float:
        """
        Calcule Value at Risk (VaR)
        
        VaR = perte maximale avec X% de confiance
        """
        if len(self.pnl_history) < 30:
            return 0.0
        
        returns = np.array(self.pnl_history[-30:]) / self.portfolio_value
        var = np.percentile(returns, (1 - confidence) * 100)
        
        return abs(var * self.portfolio_value)
    
    def _calculate_cvar(self, confidence=0.95) -> float:
        """
        Calcule Conditional Value at Risk (CVaR)
        
        CVaR = perte moyenne au-delà du VaR
        """
        if len(self.pnl_history) < 30:
            return 0.0
        
        returns = np.array(self.pnl_history[-30:]) / self.portfolio_value
        var_threshold = np.percentile(returns, (1 - confidence) * 100)
        
        # Moyenne des pertes au-delà du VaR
        tail_losses = returns[returns <= var_threshold]
        cvar = np.mean(tail_losses) if len(tail_losses) > 0 else 0
        
        return abs(cvar * self.portfolio_value)
    
    def _check_correlation(self, symbol: str, strategy: str) -> bool:
        """
        Vérifie corrélation avec positions existantes
        """
        if len(self.positions) == 0:
            return True
        
        # Simplification: vérifier si même stratégie
        # En production: calculer corrélation réelle des returns
        same_strategy_positions = [
            pos for pos in self.positions.values()
            if pos.strategy == strategy
        ]
        
        max_correlation = self.config['global_limits']['max_correlation']
        
        # Si trop de positions de même stratégie, corrélation trop haute
        if len(same_strategy_positions) >= 3:
            return False
        
        return True
    
    def _check_exit_conditions(self, position: Position):
        """Vérifie conditions de sortie (stop-loss / take-profit)"""
        # Stop-loss hit
        if position.current_price <= position.stop_loss:
            self.close_position(position.symbol, position.stop_loss)
            logger.warning(f"Stop-loss hit for {position.symbol}")
        
        # Take-profit hit
        elif position.current_price >= position.take_profit:
            self.close_position(position.symbol, position.take_profit)
            logger.info(f"Take-profit hit for {position.symbol}")
    
    def check_circuit_breakers(self):
        """
        Vérifie conditions d'arrêt automatique
        """
        # 1. Drawdown excessif
        current_dd = self._calculate_current_drawdown()
        if current_dd >= self.config['global_limits']['max_drawdown']:
            self.halt_trading(f"Max drawdown reached: {current_dd:.2%}")
            return
        
        # 2. Perte journalière excessive
        daily_pnl_pct = self._calculate_daily_pnl() / self.portfolio_value
        if daily_pnl_pct <= -self.config['global_limits']['max_daily_loss']:
            self.halt_trading(f"Max daily loss reached: {daily_pnl_pct:.2%}")
            return
        
        # 3. Volatilité extrême
        if self._detect_volatility_spike():
            self.halt_trading("Extreme volatility detected")
            return
    
    def halt_trading(self, reason: str):
        """Arrête le trading"""
        self.trading_halted = True
        self.halt_reason = reason
        
        logger.critical(f"TRADING HALTED: {reason}")
        
        # Fermer toutes positions (optionnel)
        # self._close_all_positions()
        
        # Envoyer alertes
        self._send_emergency_alert(reason)
    
    def resume_trading(self):
        """Reprend le trading (manuel uniquement)"""
        self.trading_halted = False
        self.halt_reason = None
        logger.info("Trading resumed")
    
    def _detect_volatility_spike(self) -> bool:
        """Détecte spike de volatilité anormal"""
        if len(self.pnl_history) < 20:
            return False
        
        recent_vol = np.std(self.pnl_history[-5:])
        baseline_vol = np.std(self.pnl_history[-20:-5])
        
        # Spike si volatilité > 3x baseline
        return recent_vol > 3 * baseline_vol
    
    def _send_emergency_alert(self, reason: str):
        """Envoie alerte d'urgence"""
        # TODO: Implémenter notifications (Telegram, SMS, Email)
        pass
    
    def _load_config(self) -> Dict:
        """Charge configuration depuis YAML"""
        import yaml
        with open('config/risk_limits.yaml', 'r') as f:
            return yaml.safe_load(f)

---

✅ Validation Pré-Trade

Checklist Complète

class PreTradeValidator:
    """
    Validation exhaustive avant chaque trade
    """
    
    def __init__(self, risk_manager: RiskManager):
        self.risk_manager = risk_manager
    
    def validate(self, trade_request: Dict) -> tuple[bool, List[str]]:
        """
        Exécute toutes validations
        
        Returns:
            (is_valid, list_of_errors)
        """
        errors = []
        
        # 1. Validation basique
        errors.extend(self._validate_basic(trade_request))
        
        # 2. Validation risque
        errors.extend(self._validate_risk(trade_request))
        
        # 3. Validation liquidité
        errors.extend(self._validate_liquidity(trade_request))
        
        # 4. Validation margin
        errors.extend(self._validate_margin(trade_request))
        
        # 5. Validation technique
        errors.extend(self._validate_technical(trade_request))
        
        return len(errors) == 0, errors
    
    def _validate_basic(self, trade: Dict) -> List[str]:
        """Validations basiques"""
        errors = []
        
        # Stop-loss obligatoire
        if 'stop_loss' not in trade or trade['stop_loss'] is None:
            errors.append("Stop-loss is mandatory")
        
        # Take-profit obligatoire
        if 'take_profit' not in trade or trade['take_profit'] is None:
            errors.append("Take-profit is mandatory")
        
        # Quantité positive
        if trade.get('quantity', 0) <= 0:
            errors.append("Quantity must be positive")
        
        # Prix valide
        if trade.get('price', 0) <= 0:
            errors.append("Price must be positive")
        
        return errors
    
    def _validate_risk(self, trade: Dict) -> List[str]:
        """Validations risque"""
        errors = []
        
        # Risk/Reward ratio
        risk = abs(trade['price'] - trade['stop_loss'])
        reward = abs(trade['take_profit'] - trade['price'])
        
        if risk > 0:
            rr_ratio = reward / risk
            if rr_ratio < 1.5:
                errors.append(f"Risk/Reward ratio {rr_ratio:.2f} below minimum 1.5")
        
        # Taille position
        position_value = trade['price'] * trade['quantity']
        position_pct = position_value / self.risk_manager.portfolio_value
        
        if position_pct > 0.05:  # 5% max
            errors.append(f"Position size {position_pct:.2%} exceeds 5%")
        
        return errors
    
    def _validate_liquidity(self, trade: Dict) -> List[str]:
        """Validations liquidité"""
        errors = []
        
        # TODO: Vérifier volume quotidien
        # TODO: Vérifier spread bid/ask
        # TODO: Vérifier market depth
        
        return errors
    
    def _validate_margin(self, trade: Dict) -> List[str]:
        """Validations margin"""
        errors = []
        
        # TODO: Vérifier margin disponible
        # TODO: Calculer margin requis
        # TODO: Vérifier margin call risk
        
        return errors
    
    def _validate_technical(self, trade: Dict) -> List[str]:
        """Validations techniques"""
        errors = []
        
        # Vérifier que stop-loss est du bon côté
        if trade['quantity'] > 0:  # Long
            if trade['stop_loss'] >= trade['price']:
                errors.append("Stop-loss must be below entry price for long")
            if trade['take_profit'] <= trade['price']:
                errors.append("Take-profit must be above entry price for long")
        else:  # Short
            if trade['stop_loss'] <= trade['price']:
                errors.append("Stop-loss must be above entry price for short")
            if trade['take_profit'] >= trade['price']:
                errors.append("Take-profit must be below entry price for short")
        
        return errors

---

🚨 Circuit Breakers

Types de Circuit Breakers

class CircuitBreaker:
    """
    Système d'arrêt automatique multi-niveaux
    """
    
    def __init__(self):
        self.breakers = {
            'drawdown': DrawdownBreaker(),
            'daily_loss': DailyLossBreaker(),
            'volatility': VolatilityBreaker(),
            'flash_crash': FlashCrashBreaker(),
            'api_failure': APIFailureBreaker(),
            'correlation': CorrelationBreaker(),
        }
    
    def check_all(self, market_data: Dict, portfolio_state: Dict) -> Optional[str]:
        """
        Vérifie tous circuit breakers
        
        Returns:
            Reason for halt, or None if all OK
        """
        for name, breaker in self.breakers.items():
            if breaker.should_halt(market_data, portfolio_state):
                return f"{name}: {breaker.get_reason()}"
        
        return None

class DrawdownBreaker:
    """Arrêt si drawdown excessif"""
    
    def __init__(self, max_drawdown=0.10):
        self.max_drawdown = max_drawdown
        self.reason = ""
    
    def should_halt(self, market_data: Dict, portfolio: Dict) -> bool:
        current_dd = portfolio['current_drawdown']
        
        if current_dd >= self.max_drawdown:
            self.reason = f"Drawdown {current_dd:.2%} >= {self.max_drawdown:.2%}"
            return True
        
        return False
    
    def get_reason(self) -> str:
        return self.reason

class VolatilityBreaker:
    """Arrêt si volatilité extrême"""
    
    def __init__(self, spike_threshold=3.0):
        self.spike_threshold = spike_threshold
        self.reason = ""
    
    def should_halt(self, market_data: Dict, portfolio: Dict) -> bool:
        current_vol = market_data.get('current_volatility', 0)
        baseline_vol = market_data.get('baseline_volatility', 0)
        
        if baseline_vol > 0 and current_vol > self.spike_threshold * baseline_vol:
            self.reason = f"Volatility spike: {current_vol/baseline_vol:.1f}x baseline"
            return True
        
        return False
    
    def get_reason(self) -> str:
        return self.reason

class FlashCrashBreaker:
    """Arrêt si mouvement de prix extrême"""
    
    def __init__(self, max_move_pct=0.05):
        self.max_move_pct = max_move_pct
        self.reason = ""
    
    def should_halt(self, market_data: Dict, portfolio: Dict) -> bool:
        price_change = market_data.get('price_change_1min', 0)
        
        if abs(price_change) > self.max_move_pct:
            self.reason = f"Flash crash detected: {price_change:.2%} move in 1 minute"
            return True
        
        return False
    
    def get_reason(self) -> str:
        return self.reason

---

📊 Métriques de Risque

Calculs Avancés

class RiskMetricsCalculator:
    """
    Calcule métriques de risque avancées
    """
    
    @staticmethod
    def calculate_var(returns: np.ndarray, confidence=0.95) -> float:
        """Value at Risk"""
        return np.percentile(returns, (1 - confidence) * 100)
    
    @staticmethod
    def calculate_cvar(returns: np.ndarray, confidence=0.95) -> float:
        """Conditional Value at Risk (Expected Shortfall)"""
        var = RiskMetricsCalculator.calculate_var(returns, confidence)
        return returns[returns <= var].mean()
    
    @staticmethod
    def calculate_sharpe_ratio(returns: np.ndarray, risk_free_rate=0.02) -> float:
        """Sharpe Ratio"""
        excess_returns = returns - risk_free_rate / 252  # Daily
        return np.mean(excess_returns) / np.std(excess_returns) * np.sqrt(252)
    
    @staticmethod
    def calculate_sortino_ratio(returns: np.ndarray, risk_free_rate=0.02) -> float:
        """Sortino Ratio (downside deviation)"""
        excess_returns = returns - risk_free_rate / 252
        downside_returns = returns[returns < 0]
        downside_std = np.std(downside_returns)
        
        return np.mean(excess_returns) / downside_std * np.sqrt(252)
    
    @staticmethod
    def calculate_max_drawdown(equity_curve: np.ndarray) -> float:
        """Maximum Drawdown"""
        peak = np.maximum.accumulate(equity_curve)
        drawdown = (equity_curve - peak) / peak
        return np.min(drawdown)
    
    @staticmethod
    def calculate_calmar_ratio(returns: np.ndarray, equity_curve: np.ndarray) -> float:
        """Calmar Ratio (Return / Max Drawdown)"""
        annual_return = np.mean(returns) * 252
        max_dd = abs(RiskMetricsCalculator.calculate_max_drawdown(equity_curve))
        
        return annual_return / max_dd if max_dd > 0 else 0

---

🔔 Système d'Alertes

Configuration Alertes

# config/alerts.yaml
alerts:
  risk_threshold_breach:
    channels: ['telegram', 'email']
    priority: high
    conditions:
      - total_risk > max_portfolio_risk
      - current_drawdown > 0.08  # 80% du max
      - daily_loss > 0.025       # 83% du max
  
  position_alerts:
    channels: ['telegram']
    priority: medium
    conditions:
      - position_size > 0.04     # 80% du max
      - correlation > 0.6        # 85% du max
  
  circuit_breaker:
    channels: ['telegram', 'sms', 'email']
    priority: critical
    conditions:
      - trading_halted == true

---

Suite dans le prochain fichier...