df = pd.read_excel(r'D:\bianpojiance\yuce.xlsx')

As an AI language model, I don't have access to your local machine's file directory, so I cannot run this code. However, I can explain what this code is doing.

This code reads an Excel file named "yuce.xlsx" located in the "D:\bianpojiance" directory and stores its data as a pandas DataFrame object named "df". The "r" before the file path indicates that it's a raw string and the backslashes are not interpreted as escape characters.

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yuce = pd.read_excel("E:/应统案例大赛/附件1-股票交易数据/yuceclose.xlsx",index_col=0) # 计算预期收益和样本协方差矩阵 mu3 = expected_returns.mean_historical_return(yuce) # 使用历史数据计算预期收益 S3 = risk_models.sample_cov(yuce) # 使用历史数据计算协方差矩阵 # Optimize for maximal Sharpe ratio ef = EfficientFrontier(mu3, S3) raw_weights = ef.max_sharpe() cleaned_weights = ef.clean_weights() ef.save_weights_to_file("yuceweight.csv") # saves to file print(cleaned_weights) ef.portfolio_performance(verbose=True)如何绘图表示

可以使用matplotlib库绘制有效前沿图。代码示例如下：

import numpy as np
import matplotlib.pyplot as plt

# 计算有效前沿
ef = EfficientFrontier(mu3, S3)
raw_frontier = ef.efficient_frontier()
cleaned_frontier = ef.clean_frontier()

# 绘制有效前沿图
fig, ax = plt.subplots()
ax.plot(cleaned_frontier[0], cleaned_frontier[1], label='Efficient Frontier')
ax.plot(np.sqrt(np.diagonal(S3)), mu3, 'o', markersize=5, label='Stocks')
ax.set_title('Efficient Frontier')
ax.set_xlabel('Volatility')
ax.set_ylabel('Expected Return')
ax.legend()
plt.show()

此代码将有效前沿和各个资产的预期收益率和波动率作为散点图进行可视化，并标注有效前沿的位置。可以通过修改mu3和S3变量中的值来适应不同的投资组合。

from pypfopt.efficient_frontier import EfficientFrontier from pypfopt import risk_models from pypfopt import expected_returns yuce = pd.read_excel("E:/应统案例大赛/附件1-股票交易数据/yuceclose.xlsx",index_col=0) # 计算预期收益和样本协方差矩阵 mu3 = expected_returns.mean_historical_return(yuce) # 使用历史数据计算预期收益 S3 = risk_models.sample_cov(yuce) # 使用历史数据计算协方差矩阵 # Optimize for maximal Sharpe ratio ef = EfficientFrontier(mu3, S3) raw_weights = ef.max_sharpe() cleaned_weights = ef.clean_weights() ef.save_weights_to_file("yuceweight1.csv") # saves to file print(cleaned_weights) ef.portfolio_performance(verbose=True) # 设置无风险回报率为0 risk_free = 0 # 计算每项资产的夏普比率 RandomPortfolios['Sharpe'] = (RandomPortfolios.Returns - risk_free) / RandomPortfolios.Volatility # 绘制收益-标准差的散点图，并用颜色描绘夏普比率 plt.scatter(RandomPortfolios.Volatility, RandomPortfolios.Returns, c=RandomPortfolios.Sharpe) plt.colorbar(label='Sharpe Ratio') plt.show()修改后面的代码，与前面匹配

可以使用以下代码来绘制资产收益-标准差的散点图，并用颜色描绘夏普比率：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 读取预测收盘价数据
yuce = pd.read_excel("E:/应统案例大赛/附件1-股票交易数据/yuceclose.xlsx", index_col=0)

# 计算预期收益和样本协方差矩阵
mu3 = expected_returns.mean_historical_return(yuce)  # 使用历史数据计算预期收益
S3 = risk_models.sample_cov(yuce)  # 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(mu3, S3)
raw_weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
ef.save_weights_to_file("yuceweight1.csv")  # 保存权重到文件
print(cleaned_weights)
ef.portfolio_performance(verbose=True)

# 生成随机投资组合
np.random.seed(1)
n_samples = 10000
weights = np.random.dirichlet(np.ones(len(yuce.columns)), n_samples)
returns = weights.dot(mu3)
volatility = np.sqrt(np.diag(weights @ S3 @ weights.T))
df = pd.DataFrame({'Returns': returns, 'Volatility': volatility})

# 计算夏普比率
risk_free = 0
df['Sharpe'] = (df.Returns - risk_free) / df.Volatility

# 绘制资产收益-标准差的散点图，并用颜色描绘夏普比率
plt.scatter(df.Volatility, df.Returns, c=df.Sharpe, cmap='viridis')
plt.colorbar(label='Sharpe Ratio')
plt.xlabel('Volatility')
plt.ylabel('Expected Return')
plt.show()

此代码将资产收益-标准差作为散点图进行可视化，并用颜色描绘夏普比率。可以通过修改yuce变量中的值来适应不同的投资组合。