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GetData.py
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GetData.py
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# -*- coding: utf-8 -*-
"""
Created on Tue May 03 12:49:48 2016
@author: ming
"""
import numpy as np
import pandas as pd
import time,os,requests
import datetime
import shutil
from StringIO import StringIO
def day_data(daima,start_date,end_date,fuquan):
def time_transfer_timeStamp(time_str):
timeArray = time.strptime(time_str, "%Y-%m-%d %H:%M:%S")
timeStamp = int(time.mktime(timeArray))
return str(timeStamp)
def time_transfer_string(time_str):
data = time.mktime(time.strptime(time_str,"%a %b %d %H:%M:%S +0800 %Y"))
return str(datetime.fromtimestamp(data))[0:10]
def get_xueqiu(daima,start_date,end_date,fuquan):
header = {'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Encoding':'gzip, deflate, sdch',
'Accept-Language':'en-US,en;q=0.8,zh-CN;q=0.6,zh;q=0.4,ja;q=0.2',
'Cache-Control':'max-age=0',
'Connection':'keep-alive',
'DNT':'1',
'Host':'xueqiu.com',
'Referer':'https://www.baidu.com/link?url=CQu5rGbzI_vt0fSj3b12LTyZgWvzjrK9f3L_GLIBqum&wd=&eqid=88e8a3ca0001535b00000005572edf29',
'User-Agent':'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.94 Safari/537.36'}
start_time_tmp =start_date[0:4]+'-'+start_date[4:6]+'-'+start_date[6:]+' 00:00:00'
end_time_tmp =end_date[0:4]+'-'+end_date[4:6]+'-'+end_date[6:]+' 15:30:00'
if daima[0:2]=='60':
daima_new = 'SH'+daima
else:
daima_new = 'SZ'+daima
s = requests.session()
t = s.get('https://xueqiu.com/',headers = header)
start_time = time_transfer_timeStamp(start_time_tmp)
end_time = time_transfer_timeStamp(end_time_tmp)+'000'
r = s.get('https://xueqiu.com/stock/forchartk/stocklist.json?symbol='+daima_new+'&period=1day&type='+fuquan+'&begin='+start_time+'&end='+end_time+'&_='+end_time,headers = header)
data = r.content.split('[{')[1][0:-3].split('},{')
openp = []
closep = []
highp = []
lowp = []
timep = []
volume = []
turnrate = []
dif = []
dea = []
macd = []
for a in data:
openp.append(float(a.split(',')[1].split(':')[1]))
closep.append(float(a.split(',')[3].split(':')[1]))
highp.append(float(a.split(',')[2].split(':')[1]))
lowp.append(float(a.split(',')[4].split(':')[1]))
volume.append(float(a.split(',')[0].split(':')[1]))
turnrate.append(float(a.split(',')[7].split(':')[1]))
dif.append(float(a.split(',')[12].split(':')[1]))
dea.append(float(a.split(',')[13].split(':')[1]))
macd.append(float(a.split(',')[14].split(':')[1]))
shijian_xueqiu = a.split(',')[-1].split('":"')[1][0:-1]
c = time.mktime(time.strptime(shijian_xueqiu,"%a %b %d %H:%M:%S +0800 %Y"))
time_tmp = datetime.datetime.fromtimestamp(c)
timep.append(time_tmp)
df = pd.DataFrame({'time':timep,
'open':openp,
'close':closep,
'high':highp,
'low':lowp,
'volume':volume,
'turnrate':turnrate,
'dif':dif,
'dea':dea,
'macd':macd})
return df
def get_wangyi(daima,start_date,end_date):
if daima[0:2]=='60':
a = '0'+daima
else:
a = '1'+daima
url='http://quotes.money.163.com/service/chddata.html?code='+a+'&start='+start_date+'&end='+end_date+'&fields=TCLOSE;HIGH;LOW;TOPEN;LCLOSE;CHG;PCHG;TURNOVER;VOTURNOVER;VATURNOVER;TCAP;MCAP'
condition = True
while condition:
try:
r = requests.get(url,timeout = 10)
condition = False
except:
pass
a1=pd.read_csv(StringIO(r.content),skiprows=[0],names=['shijian','daima','name','closep','haighp','lowp','openp','preclosep','CHG','PCHG','TURNOVER','volume','amount','zongshizhi','liutongshizhi'])
if a1.empty:
return None
else:
a2 = a1[a1['volume']<>0].sort_index(axis = 0,ascending=False)
a3 = a2[['shijian','openp','haighp','lowp','closep','volume','amount','liutongshizhi','zongshizhi']]
return a3
def get_tenxun(daima):
if daima[0:2]=='60':
daima_new = 'sh'+daima
else:
daima_new = 'sz'+daima
url = 'http://qt.gtimg.cn/q='+daima_new
r = requests.get(url).content
data = r.split('~')
liutongzhishi = float(data[44])
zongshizhi = float(data[45])
return [liutongzhishi,zongshizhi]
def zuhe():
df_xueqiu = get_xueqiu(daima,start_date,end_date,fuquan)
df_wangyi = get_wangyi(daima,start_date,end_date)
tx_liutongshizhi = get_tenxun(daima)[0]
tx_zongshizhi = get_tenxun(daima)[1]
df_new = pd.DataFrame({'time':np.array(df_xueqiu['time']),
'open':np.array(df_xueqiu['open']),
'close':np.array(df_xueqiu['close']),
'high':np.array(df_xueqiu['high']),
'low':np.array(df_xueqiu['low']),
'volume':np.array(df_xueqiu['volume']),
'turnrate':np.array(df_xueqiu['turnrate']),
'dif':np.array(df_xueqiu['dif']),
'dea':np.array(df_xueqiu['dea']),
'macd':np.array(df_xueqiu['macd']),
'liutongshizhi':np.append(np.array(df_wangyi['liutongshizhi']),tx_liutongshizhi),
'zongshizhi':np.append(np.array(df_wangyi['zongshizhi']),tx_zongshizhi)})
return df_new
return zuhe()
def min_data(daima,start_date,end_date,n):
def count_split_days(start_date,end_date):
if datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))<datetime.datetime(2015,1,5):
num = (datetime.datetime(int(end_date[0:4]),int(end_date[4:6]),int(end_date[6:]))-datetime.datetime(2015,1,5)).days
else:
num = (datetime.datetime(int(end_date[0:4]),int(end_date[4:6]),int(end_date[6:]))-datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))).days
return num
from gmsdk import md
ret = md.init("yourname", "password")
if daima[0:2]=='60':
daima_new = str('SHSE.')+str(daima)
else:
daima_new = str('SZSE.')+str(daima)
mins = n*60
strtime = []
openp = []
high = []
low = []
close = []
volume = []
amount = []
days_internal = 120
LoopNum = divmod(count_split_days(start_date,end_date),days_internal)[0]+1
left = divmod(count_split_days(start_date,end_date),days_internal)[1]
bars_set = []
n = 0
for ln in range(0,LoopNum):
if n == 0:
bars = md.get_bars(daima_new,mins,str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln)))[0:10]+' 09:00:00',str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln+1)))[0:10]+' 15:30:00')
n = n+1
else:
if ln == LoopNum-1:
bars = md.get_bars(daima_new,mins,str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln)+1))[0:10]+' 09:00:00',end_date[0:4]+'-'+end_date[4:6]+'-'+end_date[6:]+' 15:30:00')
n = n+1
else:
bars = md.get_bars(daima_new,mins,str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln)+1))[0:10]+' 09:00:00',str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln+1)))[0:10]+' 15:30:00')
n = n+1
bars_set.append(bars)
for SignalBars in bars_set:
for b in SignalBars:
strtime.append(b.strtime)
openp.append(b.open)
high.append(b.high)
low.append(b.low)
close.append(b.close)
volume.append(b.volume)
amount.append(b.amount)
mins = pd.DataFrame({'strtime':strtime,
'open':openp,
'close':close,
'high':high,
'low':low,
'volume':volume,
'amount':amount})
return mins