df.loc[:, 'volX'] = df2.loc[:, 'volX']
#------------
_df_vol = df[['vol']]
_df_vol.loc[:, 'mean'] = _df_vol['vol'].rolling(5).mean()
_df_vol.loc[:, 'std'] = _df_vol['vol'].rolling(5).std()
_df_vol.loc[:, 'scatter_index'] = (_df_vol['vol'] -
_df_vol['mean']) / _df_vol.loc[:, 'std']
df.loc[:, 'si'] = _df_vol.loc[:, 'scatter_index']
# for i in range(1,30):
# _df_vol.loc[:,'vol+%i'%i]=_df_vol['vol'].shift(i)
return df
if __name__ == '__main__':
import kplot as kp
ts.set_token('bf3b4e51fcc67507e8694e9a3f2bd591be93bea276f9d86f564fe28f')
pro = ts.pro_api()
df = ts.pro_bar(api=pro,
ts_code='000004.SZ',
adj='qfq',
start_date='20180201')
# df = ts.pro_bar(ts_code='0A0001.SH', asset='I', start_date='20180101', end_date='20190411')
# df = ts.get_hist_data('sh',start='2017-01-01',end='2018-03-31')
macd_index_cal(df)
kdj_index_cal(df)
rsi_index_cal(df)
vol_index_cal(df)
kp.kplot(df, 'vol')
da.data_cal_index()
# 数据获取及分析，分析今日出现MACD金叉，金叉Pre的股票
import pandas as pd
import symbol_select as ss
select_Df_macd = ss.macd_select()
#select_Df_kdj=ss.kdj_select()
# 绘图
import kplot as kp
import dataAnalysis as da
import matplotlib.pyplot as plt
#ResultDf=pd.read_pickle('result_data/Select_macd.pkl')
#select_Df2=select_Df[select_Df['kdX']==1]#
select_Df = select_Df_macd
if len(select_Df) != 0:
Symbollist = select_Df.loc[:, 'ts_code']
else:
Symbollist = []
Symbollist.to_pickle('Symbollist.pkl')
for iSymbol in Symbollist:
df = da.data_read(iSymbol)
startDate = '20180501'
df1 = df[df['trade_date'] > startDate]
#fig=kp.kplot(df1,'MACD')#绘图
#fig=kp.kplot(df1,'KDJ')#绘图
fig = kp.kplot(df1, 'MACD_KDJ')
fig.savefig('MACD_%s.jpg' % df1['ts_code'][0], dpi=600)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 5 17:04:26 2019
@author: mac
"""
# 把所有数据获取一遍，并更新数据信息，每天运行一次即可
import dataAnalysis as da
import pandas as pd
df = da.data_read('000002.SZ')
# 绘图
import kplot as kp
import dataAnalysis as da
import matplotlib.pyplot as plt
startDate = '20181201'
df1 = df[df['trade_date'] > startDate]
df1['mavol5'] = df1['vol'].rolling(5).mean()
df1['mavol10'] = df1['vol'].rolling(10).mean()
df1.to_csv('a1.csv')
fig = kp.kplot(df1, 'vol')