转自（https://www.joinquant.com/view/community/detail/16916/）

jqdata在提供基础数据的时候，并没有提供换手率这一数据，需要自己进行计算，本文将从财务数据里面计算出来换手率这一数据，合并到日数据和30分钟数据。

话不多说，直接上代码：

import pandas as pdimport jqdatasdk as JQstock_data_day_file = './data/day/'stock_data_m30_file = './data/m30/'# 获取日数据基本数据和财务数据def get_day_data(stock,start_date,end_date):    # 获取基本数据 =======================================================    stock_pd = JQ.get_price(security=stock, start_date=start_date, end_date=end_date, frequency='1d',                         fields=['open', 'high', 'low', 'close', 'avg', 'volume', 'money', 'high_limit', 'low_limit',                                 'pre_close', 'factor', 'paused'], fq='post').dropna()    # 股票数据小于100条的丢弃    if stock_pd.shape[0] < 100:        return None,pd.DataFrame({})    stock_pd = stock_pd.reset_index()  # 去掉索引，把日期索引转化为列    # 处理日期格式    stock_pd['date'] = pd.to_datetime(stock_pd['index'].values).strftime(date_format='%Y%m%d')    stock_pd['date'] = stock_pd['date'].astype(int)    # 处理代码格式    stock_pd['code'] = stock.split('.')[0]    stock_pd['code'] = stock_pd['code'].astype(int)    # 处理成交量为前复权成交量    stock_pd['volume_fq'] = stock_pd['volume']    stock_pd['volume'] = stock_pd['volume'] * stock_pd['factor'] / 100   #  /100 股转为手    # 成交额单位转换 元转换为千元 money    stock_pd['money'] = stock_pd['money'] / 1000    # 计算涨跌幅    stock_pd['pct_change'] = (stock_pd['close'] / stock_pd['pre_close'] - 1) * 100    # 排序字段    stock_pd = stock_pd[['code', 'date', 'open', 'high', 'low', 'close', 'avg', 'pre_close', 'pct_change','volume',                         'money', 'high_limit','low_limit', 'volume_fq', 'factor', 'paused']]    # print(stock_pd)    # print(stock_pd.shape[0])    #  获取财务数据   ==========================================================================    #  circulating_cap    流通股本(万股)    #  circulating_market_cap    流通市值(亿元)    #  turnover_ratio    换手率(%)    Query = JQ.query(JQ.valuation.circulating_cap,                 JQ.valuation.market_cap,                 JQ.valuation.turnover_ratio                 ).filter(JQ.valuation.code.in_([stock]))    panel = JQ.get_fundamentals_continuously(Query, end_date=end_date, count=stock_pd.shape[0])    # 判断当前的股票代码是否在panel里面，是代表有数据，否代表无数据  债没有财务数据，不判断这里会报错    if stock not in panel.minor_axis.values:        return None,pd.DataFrame({})    stock_finance_pd = panel.minor_xs(stock)    stock_finance_pd = stock_finance_pd.reset_index() # 去掉索引，把日期索引转化为列    # 处理日期    stock_finance_pd['date'] = pd.to_datetime(stock_finance_pd['day'].values).strftime(date_format='%Y%m%d')    stock_finance_pd['date'] = stock_finance_pd['date'].astype(int)    # 处理代码格式    stock_finance_pd['code'] = stock.split('.')[0]    stock_finance_pd['code'] = stock_finance_pd['code'].astype(int)    stock_finance_pd = stock_finance_pd[['code', 'date', 'circulating_cap', 'market_cap', 'turnover_ratio']]    #  合并股票基础数据和财务数据==========================================================================    stock_data = pd.merge(stock_pd, stock_finance_pd, on=['code', 'date'])    stock_data = stock_data[['code', 'date', 'open', 'high', 'low', 'close', 'avg', 'pre_close',                             'pct_change','volume','money', 'turnover_ratio','high_limit','low_limit',                             'volume_fq', 'circulating_cap','market_cap','factor', 'paused']]    save_path = stock_data_day_file + stock + '.csv'    stock_data.to_csv(save_path, index=False)    # 返回股票的复权因子，用来处理30分钟的成交量复权问题    stock_factor = stock_data[['code','date','factor']]    return save_path,stock_factor# 获取30分钟基本数据def get_m30_data(stock,stock_factor,start_date,end_date):    stock_m30_pd = JQ.get_price(security=stock, start_date=start_date, end_date=end_date+' 23:59:59', frequency='30m',                         fields=['open', 'high', 'low', 'close', 'volume', 'money'], fq='post')    stock_m30_pd = stock_m30_pd.reset_index() # 去掉索引，把日期索引转化为列    # 处理日期格式    stock_m30_pd['date'] = pd.to_datetime(stock_m30_pd['index'].values).strftime(date_format='%Y%m%d')    stock_m30_pd['date'] = stock_m30_pd['date'].astype(int)    # 处理时间格式  原时间为10:00-15:00  处理为9:30-14:30    stock_m30_pd['time'] = (pd.to_datetime(stock_m30_pd['index'].values) - pd.Timedelta(minutes=30)).strftime(date_format='%H%M')    stock_m30_pd['time'] = stock_m30_pd['time'].astype(int)    # 处理代码格式    stock_m30_pd['code'] = stock.split('.')[0]    stock_m30_pd['code'] = stock_m30_pd['code'].astype(int)    stock_m30_pd = stock_m30_pd[['code', 'date', 'time', 'open', 'high', 'low', 'close', 'volume', 'money']]    # 处理成交量复权问题    stock_m30_data = pd.merge(stock_m30_pd,stock_factor, on=['code','date'])    stock_m30_data['volume'] = stock_m30_data['volume'] * stock_m30_data['factor'] / 100 # /100 成交量股转为手    # 成交额单位转换 元转换为千元 money    stock_m30_data['money'] = stock_m30_data['money'] / 1000    save_path = stock_data_m30_file + stock + '_m30.csv'    stock_m30_data.to_csv(save_path,index=False)    return save_pathdef query_spare():    # 判断当日查询条数余额    spare = JQ.get_query_count()['spare']    if spare < 50000:        print('spare',spare)        sys.exit()    return sparedef main(start_date,end_date):    JQ.auth(username='1300000000', password=‘000000')    # 获取数据已经下载完成的股票代码    stocks_download_list = []    for name in os.listdir(stock_data_day_file):        if name[-4:] == '.csv':            stocks_download_list.append(str(name[:-4]))    # 获取所有股票代码    stocks_all_list = list(JQ.get_all_securities(['stock']).index)    # stocks_all_list = ['600631.XSHG']    # 去掉已经下载完成的股票代码    stocks_list = list(set(stocks_all_list).difference(set(stocks_download_list)))    nums = 1    for stock in stocks_list:        spare = query_spare()        day_save_path, stock_factor = get_day_data(stock,start_date,end_date)        if stock_factor.shape[0] == 0:            print(stock,' data error...')            continue        m30_save_path = get_m30_data(stock,stock_factor,start_date,end_date)        print(nums,len(stocks_list),day_save_path,m30_save_path,spare)        stocks_download_list.append(stock)        nums += 1if __name__ == '__main__':    import os,sys,json    end_date = sys.argv[1] # format : %Y-%m-%d    # end_date = '2018-12-28'    start_date = '2010-01-01'    main(start_date,end_date)