def generate_factor(self):
trade_cal = tools.get_trade_cal(self.start_date, self.end_date)
skew = DataFrame()
for date in trade_cal:
r_daily = DataFrame()
files = os.listdir('%s/StockSnapshootData/%s' %
(gc.DATABASE_PATH, date))
try:
data_dic = {
file.split('.')[1]:
pd.read_csv('%s/StockSnapshootData/%s/%s' %
(gc.DATABASE_PATH, date, file),
index_col=[0],
parse_dates=[0])
for file in files
}
except:
print(date)
keys = list(data_dic.keys())
for key in keys:
if len(data_dic[key]) == 0:
del data_dic[key]
price = DataFrame({
'%s.SZ' % stock: data_dic[stock].loc[:, 'price']
for stock in data_dic.keys()
})
price.fillna(method='ffill', inplace=True)
price = price.loc[(price.index > '%s093000' %
(date.replace('-', ''))) &
(price.index < '%s150100' %
(date.replace('-', ''))), :]
r_daily = np.log(price).diff()
r_daily.fillna(0, inplace=True)
skew_daily = r_daily.resample(rule='15T').sum().skew()
skew = pd.concat([skew, DataFrame({date: skew_daily}).T], axis=0)
print(skew)
a = skew
self.factor = a
import os
import sys
import time
import datetime
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import Config
sys.path.append(Config.GLOBALCONFIG_PATH)
import Global_Config as gc
import tools
date = datetime.datetime.today().strftime('%Y%m%d')
trade_cal = tools.get_trade_cal(start_date=date, end_date=date)
if len(trade_cal) == 0:
sys.exit()
if len(sys.argv) == 2:
today = sys.argv[1]
else:
today = datetime.datetime.today().strftime('%Y%m%d')
start = today
end = today
pro = ts.pro_api()
#获取行业分类
stocks = pro.stock_basic(fields='ts_code').ts_code