# 计算预期收益和样本协方差矩阵 mu = expected_returns.mean_historical_return(combined_df1) # 使用历史数据计算预期收益 S = risk_models.sample_cov(combined_df1) # 使用历史数据计算协方差矩阵 # 使用EfficientFrontier类优化投资组合 ef = EfficientFrontier(mu, S) # 最小化投资组合的风险 weights = ef.min_volatility() # 打印出最优权重 print(weights)，如何进行改进利用遗传算法或模拟退火程序进行得到3700只股票的最优组合权重

对于利用遗传算法或模拟退火程序进行优化，你可以使用Python中的Optimize库来实现。以下是一个简单的例子，展示如何使用遗传算法优化投资组合权重：

import random
import numpy as np
from scipy.optimize import minimize

# 计算投资组合收益和风险
def calculate_portfolio_performance(weights, returns, cov_matrix):
    portfolio_return = np.sum(returns * weights)
    portfolio_std_dev = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
    return portfolio_return, portfolio_std_dev

# 目标函数 - 最小化投资组合风险
def minimize_portfolio_risk(weights, returns, cov_matrix):
    portfolio_return, portfolio_std_dev = calculate_portfolio_performance(weights, returns, cov_matrix)
    return portfolio_std_dev

# 遗传算法优化
def optimize_portfolio_with_genetic_algorithm():
    n_assets = 3700 # 资产数量
    population_size = 50 # 种群大小
    generations = 100 # 迭代次数

    # 定义种群
    population = []
    for i in range(population_size):
        individual = np.random.rand(n_assets)
        individual /= np.sum(individual)
        population.append(individual)

    # 定义适应度函数
    def fitness_function(individual):
        return minimize_portfolio_risk(individual, returns, cov_matrix)

    # 进行迭代
    for i in range(generations):
        # 选择
        fits = [fitness_function(individual) for individual in population]
        indices = np.argsort(fits)
        population = [population[i] for i in indices[:population_size//2]]

        # 交叉
        for i in range(population_size//2):
            individual1, individual2 = random.choice(population), random.choice(population)
            child1, child2 = crossover(individual1, individual2)
            population += [child1, child2]

        # 变异
        for i in range(population_size):
            population[i] = mutate(population[i])

    # 返回最优解
    best_solution = max(population, key=fitness_function)
    return best_solution

# 运行优化程序
returns = # 从数据集中计算得到
cov_matrix = # 从数据集中计算得到
weights = optimize_portfolio_with_genetic_algorithm()
print(weights)

以上代码是一个简单的遗传算法框架，你可以根据自己的需求进行修改和优化。类似地，你也可以使用模拟退火算法来进行优化。