写一个使用基本面分析和因子分析来筛选出具有潜在投资价值的个股的代码
时间: 2023-06-19 17:06:05 浏览: 56
以下是一个使用基本面分析和因子分析来筛选具有潜在投资价值的个股的示例代码,代码使用Python语言编写,并使用了pandas和numpy库:
```python
import pandas as pd
import numpy as np
# 读取基本面数据和因子数据
fundamental_data = pd.read_csv("fundamental_data.csv")
factor_data = pd.read_csv("factor_data.csv")
# 合并数据,使用股票代码作为索引
merged_data = pd.merge(fundamental_data, factor_data, on='code')
merged_data.set_index('code', inplace=True)
# 定义筛选条件
# 1. 市盈率小于行业平均值
# 2. 市净率小于行业平均值
# 3. 股息率大于行业平均值
# 4. 资产收益率大于行业平均值
# 5. 净利润增长率大于行业平均值
# 6. 营业收入增长率大于行业平均值
pe_ratio_mean = merged_data.groupby('industry')['pe_ratio'].mean()
pb_ratio_mean = merged_data.groupby('industry')['pb_ratio'].mean()
dividend_yield_mean = merged_data.groupby('industry')['dividend_yield'].mean()
roe_mean = merged_data.groupby('industry')['roe'].mean()
net_profit_growth_mean = merged_data.groupby('industry')['net_profit_growth'].mean()
revenue_growth_mean = merged_data.groupby('industry')['revenue_growth'].mean()
condition = (merged_data['pe_ratio'] < pe_ratio_mean[merged_data['industry']]) & \
(merged_data['pb_ratio'] < pb_ratio_mean[merged_data['industry']]) & \
(merged_data['dividend_yield'] > dividend_yield_mean[merged_data['industry']]) & \
(merged_data['roe'] > roe_mean[merged_data['industry']]) & \
(merged_data['net_profit_growth'] > net_profit_growth_mean[merged_data['industry']]) & \
(merged_data['revenue_growth'] > revenue_growth_mean[merged_data['industry']])
# 筛选出符合条件的股票
selected_stocks = merged_data[condition]
# 输出结果
print(selected_stocks.index.tolist())
```
需要注意的是,代码中读取的基本面数据和因子数据需要提前准备好,分别保存为fundamental_data.csv和factor_data.csv文件。同时,需要根据实际情况调整筛选条件和因子权重,以得到更符合个人投资策略的结果。