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datas.py
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datas.py
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import baostock as bs
import pandas as pd
from multiprocessing import Pool
import akshare as ak
import os
def download_date_data(code, flag):
try:
fg = '' if flag not in ['qfq', 'hfq'] else flag
stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol=code, period="daily", start_date='19901219', adjust=fg)
stock_zh_a_hist_df.to_csv(f'./data_{flag}/{code}.csv')
except Exception as e:
print(f"download {flag} stock {code} error!!!")
def download_all_date_data(flag):
# 获取所有股票代码，akshare接口
stock_zh_a_spot_em_df = ak.stock_zh_a_spot_em()
list_code = stock_zh_a_spot_em_df['代码'].to_list()
fg = 'bfq' if flag not in ['qfq', 'hfq'] else flag # bfq: 不复权; qfq: 前复权; hfq: 后复权
# 创建保存路径
path = f'data_{fg}'
if not os.path.isdir(path):
os.makedirs(path)
# 创建进程池来下载股票日数据
count = os.cpu_count()
pool = Pool(min(count*4, 60))
for code in list_code:
pool.apply_async(download_date_data, (code, flag))
pool.close()
pool.join()
def get_all_date_data(start_time, end_time, list_assets):
data_path = 'data_bfq'
# 从本地保存的数据中读出需要的股票日数据
list_all = []
for c in list_assets:
df = pd.read_csv(f'{data_path}/{c}.csv')
df['asset'] = c
list_all.append(df[(df['日期'] >= start_time) & (df['日期'] <= end_time)])
print(len(list_all))
# 所有股票日数据拼接成一张表
df_all = pd.concat(list_all)
# 修改列名
df_all = df_all.rename(columns={
"日期": "date",
"开盘": "open",
"收盘": "close",
"最高": "high",
"最低": "low",
"成交量": "volume",
"成交额": "amount",
"涨跌幅": "pctChg"})
# 计算平均成交价
df_all['vwap'] = df_all.amount / df_all.volume / 100
# 返回计算因子需要的列
df_all = df_all.reset_index()
df_all = df_all[['asset','date', "open", "close", "high", "low", "volume", 'vwap', "pctChg"]]
return df_all
def get_zz500_stocks(time):
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
# 获取中证500成分股
rs = bs.query_zz500_stocks('2019-01-01')
print('query_zz500 error_code:'+rs.error_code)
print('query_zz500 error_msg:'+rs.error_msg)
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
lists = result['code'].to_list()
lists = [x.split('.')[1] for x in lists]
# 登出系统
bs.logout()
return lists, result
def get_hs300_stocks(time):
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
# 获取沪深300成分股
rs = bs.query_hs300_stocks(time)
print('query_hs300 error_code:'+rs.error_code)
print('query_hs300 error_msg:'+rs.error_msg)
# 打印结果集
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
hs300_stocks.append(rs.get_row_data())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
lists = result['code'].to_list()
lists = [x.split('.')[1] for x in lists]
# 登出系统
bs.logout()
return lists, result
def download_index_data(code):
path = 'index'
stock_zh_index_daily_df = ak.stock_zh_index_daily(symbol=code)
# 创建保存路径
if not os.path.isdir(path):
os.makedirs(path)
stock_zh_index_daily_df.to_csv(f'{path}/{code}.csv')
if __name__ == '__main__':
download_index_data("sh000300")
download_all_date_data("bfq")