-
Notifications
You must be signed in to change notification settings - Fork 78
/
datas.py
130 lines (107 loc) · 4.08 KB
/
datas.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
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")