量化打板公式源码

由于量化打板公式的种类众多，无法给出统一的源码，但可以提供一些参考：

1. 均值回归： ```

# 均值回归

# 计算股票收益率的均值 mean = np.mean(stock_return) # 计算股票收益率的方差 variance = np.var(stock_return) # 计算股票收益率的协方差

covariance = np.cov(stock_return) # 计算权重

weight = np.dot(np.linalg.inv(covariance),mean) # 计算期望收益率

expected_return = np.dot(weight,mean) # 计算期望波动率 expected_volatility =

np.sqrt(np.dot(weight,np.dot(covariance,weight))) ```

2. 马尔科夫链： ```

# 马尔可夫链 # 定义转移矩阵

transition_matrix = np.array([[0.5, 0.2, 0.3], [0.3, 0.5, 0.2], [0.2, 0.3, 0.5]]) # 定义初始状态概率

initial_state = np.array([0.2, 0.4, 0.4]) # 计算期望收益率

expected_return = np.dot(initial_state, np.dot(transition_matrix, initial_state))

# 计算期望波动率

expected_volatility = np.sqrt(np.dot(initial_state,

np.dot(transition_matrix, np.dot(transition_matrix, initial_state)))) ```

3. 蒙特卡洛模拟： ```

# 蒙特卡洛模拟

# 定义股票收益率的期望值和方差 mean = np.array([0.05, 0.06, 0.07])

variance = np.array([[0.01, 0.005, 0.004], [0.005, 0.02, 0.006], [0.004, 0.006, 0.03]]) # 运行模拟

simulated_returns = np.random.multivariate_normal(mean, variance, size=1000) # 计算期望收益率

expected_return = np.mean(simulated_returns) # 计算期望波动率

expected_volatility = np.std(simulated_returns) ```