遗传算法求投资组合最优解，请给我一段python代码

遗传算法是一种模拟自然选择过程的优化算法，可以用于解决复杂的问题，如投资组合优化。以下是一个简单的Python代码片段，展示了如何使用遗传算法来寻找投资组合的最优权重（假设我们有股票数据和目标收益率），这个例子使用了DEAP库：

from deap import base, creator, tools
import numpy as np

# 假设stock_prices是个包含历史价格的DataFrame，returns是价格变动率
stock_prices = ...  # 例如：pd.read_csv('prices.csv')
returns = stock_prices.pct_change()

# 定义问题参数
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def evaluate(individual):
    total_return = sum(returns.iloc[:, i] * individual[i] for i in range(len(individual)))
    risk = np.std(np.sum(returns.values * individual.reshape(-1, 1), axis=0))
    return 1 / (total_return + risk),  # 目标是最大化夏普比率

toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=len(returns.columns))

toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", evaluate)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

pop_size = 50
ngen = 100
pop = toolbox.population(n=pop_size)

for gen in range(ngen):
    offspring = toolbox.select(pop, len(pop))
    offspring = [toolbox.clone(ind) for ind in offspring]

    # 变异操作
    for child1, child2 in zip(offspring[::2], offspring[1::2]):
        if random.random() < 0.5:
            toolbox.mate(child1, child2)
            del child1.fitness.values
            del child2.fitness.values

    # 适应度评估
    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = map(toolbox.evaluate, invalid_ind)
    for ind, fit in zip(invalid_ind, fitnesses):
        ind.fitness.values = fit

    # 更新种群
    pop[:] = offspring

# 最终的最优个体
best_solution = tools.selBest(pop, 1)[0]