第12章：风险管理与资金管理

控制风险是量化交易生存的关键。

学习目标

• ✅ 理解风险管理的重要性
• ✅ 掌握仓位管理方法
• ✅ 实现止损止盈策略
• ✅ 计算VaR和CVaR

12.1 风险类型

主要风险

1. 市场风险：价格波动
2. 流动性风险：买卖困难
3. 操作风险：系统故障
4. 模型风险：策略失效

12.2 仓位管理

凯利公式

def kelly_criterion(win_rate, win_loss_ratio):
    """
    凯利公式：计算最优仓位
    
    f* = (p * b - q) / b
    p: 胜率
    q: 败率  
    b: 盈亏比
    """
    p = win_rate
    q = 1 - win_rate
    b = win_loss_ratio
    
    kelly = (p * b - q) / b
    
    # 实际使用半凯利（更保守）
    return max(0, kelly * 0.5)

# 使用示例
position = kelly_criterion(win_rate=0.6, win_loss_ratio=2.0)
print(f"建议仓位：{position:.2%}")  # 约20%

波动率目标

class VolatilityTargetPosition:
    """波动率目标仓位管理"""
    
    def __init__(self, target_vol=0.15):
        self.target_vol = target_vol
    
    def calculate_position(self, returns):
        """计算仓位"""
        current_vol = returns.rolling(20).std() * np.sqrt(252)
        position = self.target_vol / current_vol
        return min(position, 1.0)  # 最大100%仓位

等风险贡献

def risk_parity_weights(returns):
    """风险平价权重"""
    cov_matrix = returns.cov() * 252
    n = len(returns.columns)
    
    def risk_contribution(weights):
        portfolio_vol = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
        marginal_contrib = np.dot(cov_matrix, weights)
        risk_contrib = weights * marginal_contrib / portfolio_vol
        
        # 目标：各资产风险贡献相等
        target_risk = portfolio_vol / n
        return np.sum((risk_contrib - target_risk) ** 2)
    
    # 优化求解...

12.3 止损止盈

固定比例止损

class StopLossTakeProfit:
    """止损止盈"""
    
    def __init__(self, stop_loss=-0.05, take_profit=0.10):
        self.stop_loss = stop_loss
        self.take_profit = take_profit
    
    def check(self, entry_price, current_price, position):
        """检查是否触发"""
        pnl = (current_price - entry_price) / entry_price
        
        if position > 0:  # 多头
            if pnl <= self.stop_loss:
                return 'STOP_LOSS'
            elif pnl >= self.take_profit:
                return 'TAKE_PROFIT'
        
        return None

移动止损

class TrailingStop:
    """移动止损"""
    
    def __init__(self, trailing_percent=0.05):
        self.trailing_percent = trailing_percent
        self.highest_price = None
    
    def update(self, current_price):
        """更新最高价"""
        if self.highest_price is None or current_price > self.highest_price:
            self.highest_price = current_price
    
    def should_stop(self, current_price):
        """是否触发"""
        if self.highest_price is None:
            return False
        
        drawdown = (current_price - self.highest_price) / self.highest_price
        return drawdown <= -self.trailing_percent

12.4 VaR计算

历史模拟法

def calculate_var(returns, confidence=0.95):
    """计算VaR（历史模拟法）"""
    return np.percentile(returns, (1 - confidence) * 100)

def calculate_cvar(returns, confidence=0.95):
    """计算CVaR（条件风险价值）"""
    var = calculate_var(returns, confidence)
    return returns[returns <= var].mean()

参数法

from scipy.stats import norm

def calculate_var_parametric(returns, confidence=0.95):
    """计算VaR（参数法）"""
    mean = returns.mean()
    std = returns.std()
    
    var = norm.ppf(1 - confidence, mean, std)
    return var

12.5 风险监控

class RiskMonitor:
    """风险监控"""
    
    def __init__(self, max_drawdown=-0.10, max_var=0.05):
        self.max_drawdown = max_drawdown
        self.max_var = max_var
    
    def check(self, portfolio):
        """检查风险"""
        alerts = []
        
        # 回撤检查
        if portfolio.drawdown < self.max_drawdown:
            alerts.append(f"回撤过大：{portfolio.drawdown:.2%}")
        
        # VaR检查
        if portfolio.var > self.max_var:
            alerts.append(f"VaR过大：{portfolio.var:.2%}")
        
        return alerts

本文节选自《AI量化交易从入门到精通》第12章
完整内容请访问代码仓：bookwriting/part3practice/part12_risk/README.md