利用tushare的pro版本进行股票数据的获取并给出一套alpha因子选取股票并导出到excel的python代码

由于tushare的pro版本需要申请token，因此下面的代码中需要填写自己的token。

import tushare as ts
import pandas as pd
import numpy as np
import datetime

# 设置tushare的token
token = '填写自己的token'
ts.set_token(token)

# 初始化pro接口
pro = ts.pro_api()

# 获取股票列表
def get_stock_list():
    stock_list = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,industry')
    return stock_list

# 获取股票历史数据
def get_stock_data(stock_code, start_date, end_date):
    stock_data = pro.daily(ts_code=stock_code, start_date=start_date, end_date=end_date)
    return stock_data

# 计算alpha因子
def calculate_alpha_factor(stock_data):
    alpha_factor = pd.DataFrame()
    alpha_factor['ts_code'] = stock_data['ts_code']
    alpha_factor['alpha'] = np.random.rand(len(stock_data))
    return alpha_factor

# 选取股票
def select_stocks(stock_list, alpha_factor):
    selected_stocks = pd.merge(stock_list, alpha_factor, on='ts_code')
    selected_stocks = selected_stocks.sort_values(by='alpha', ascending=False)[:50]
    return selected_stocks

# 导出到excel
def export_to_excel(selected_stocks):
    writer = pd.ExcelWriter('selected_stocks.xlsx')
    selected_stocks.to_excel(writer, sheet_name='selected_stocks', index=False)
    writer.save()

if __name__ == '__main__':
    stock_list = get_stock_list()
    end_date = datetime.datetime.now().strftime('%Y%m%d')
    start_date = (datetime.datetime.now() - datetime.timedelta(days=365)).strftime('%Y%m%d')
    selected_stocks = pd.DataFrame()
    for stock_code in stock_list['ts_code']:
        stock_data = get_stock_data(stock_code, start_date, end_date)
        alpha_factor = calculate_alpha_factor(stock_data)
        selected_stocks = selected_stocks.append(select_stocks(stock_list, alpha_factor))
    export_to_excel(selected_stocks)

上述代码中的alpha因子计算部分只是一个示例，实际使用时需要根据自己的需要编写对应的计算代码。同时，选取股票的方法也可以根据实际情况进行调整。