PyCharm 代码无法运行的常见问题及解决方法：最小化组合风险的权重计算

该代码缺少主函数定义和调用，需要添加以下代码：

def objective_function(weights, cov):
    portfolio_variance = np.dot(weights.T, np.dot(cov, weights))
    return portfolio_variance

weights_initial = np.ones(len(codes))/len(codes) #初始化权重
bounds = tuple((0,1) for i in range(len(codes))) #约束：权重必须介于0和1之间
constraints = ({'type': 'eq', 'fun': lambda x:  np.sum(x) - 1}) #约束条件：权重之和为1

result = minimize(objective_function, weights_initial, args=(cov,), bounds=bounds, constraints=constraints)

print('最小化组合风险的权重为：', result.x)

完整代码如下：

import akshare as ak
import numpy as np
import pandas as pd
from scipy.optimize import minimize

# 读入5支股票 2015-01-01 到 2021-12-31 日收盘价数据，并计算对数收益率
def get_ret(code):
    data = ak.stock_zh_a_hist(symbol=code, period='daily', start_date='20150101', end_date='20211231', adjust='')
    data.index = pd.to_datetime(data['日期'], format='%Y-%m-%d') #设置日期索引
    close = data['收盘'] #日收盘价
    close.name = code
    ret = np.log(close/close.shift(1)) #日收益率
    return ret

codes = ['000001', '000651', '300015', '600519', '000625']
ret = pd.DataFrame()
for code in codes:
    ret_ = get_ret(code)
    ret = pd.concat([ret, ret_], axis=1)
ret = ret.dropna()

R_cov = ret.cov() #计算协方差
cov = np.array(R_cov)

def objective_function(weights, cov):
    portfolio_variance = np.dot(weights.T, np.dot(cov, weights))
    return portfolio_variance

weights_initial = np.ones(len(codes))/len(codes) #初始化权重
bounds = tuple((0,1) for i in range(len(codes))) #约束：权重必须介于0和1之间
constraints = ({'type': 'eq', 'fun': lambda x:  np.sum(x) - 1}) #约束条件：权重之和为1

result = minimize(objective_function, weights_initial, args=(cov,), bounds=bounds, constraints=constraints)

print('最小化组合风险的权重为：', result.x)