使用如下代码从TuShare下载沪深300每只股票的历史成交记录并按股票、日期保存到本地。主要是为了以后查询方便快速。
import numpy as np
import pandas as pd
import tushare as ts
import datetime
import time
import tushare as ts
import os
data_dir = 'D:\\python_study\\stock_hist_data\\' #下载数据的存放路径
#ts.get_sz50s() #获取上证50成份股 返回值为DataFrame：code股票代码 name股票名称
cal_dates = ts.trade_cal() #返回交易所日历，类型为DataFrame, calendarDate isOpen
#本地实现判断市场开市函数
@date: str类型日期 eg.'2017-11-23'
def is_open_day(date):
if date in cal_dates['calendarDate'].values:
return cal_dates[cal_dates['calendarDate']==date].iat[0,1]==1
return False
#从TuShare获取tick data数据并保存到本地
#@symbol: str类型股票代码 eg.600030
#@date: date类型日期
def get_save_tick_data(symbol, date):
global sleep_time,data_dir
res=True
str_date=str(date)
dir=data_dir+symbol+'\\'+str(date.year)+'\\'+str(date.month)
file=dir+'\\'+symbol+'_'+str_date+'_tick_data.h5'
if is_open_day(str_date):
if not os.path.exists(dir):
os.makedirs(dir)
if not os.path.exists(file):
try:
d=ts.get_tick_data(symbol,str_date,pause=0.1)
except IOError, msg:
print str(msg).decode('UTF-8')
sleep_time=min(sleep_time*2, 128)#每次下载失败后sleep_time翻倍，但是最大128s
print 'Get tick data error: symbol: '+ symbol + ', date: '+str_date+', sleep time is: '+str(sleep_time)
return res
else:
hdf5_file=pd.HDFStore(file, 'w',complevel=4, complib='blosc')
hdf5_file['data']=d
hdf5_file.close()
sleep_time=max(sleep_time/2, 2) #每次成功下载后sleep_time变为一半，但是至少2s
print "Successfully download and save file: "+file+', sleep time is: '+str(sleep_time)
return res
else:
print "Data already downloaded before, skip " + file
res=False
return res
#获取从起始日期到截止日期中间的的所有日期，前后都是封闭区间
def get_date_list(begin_date, end_date):
date_list = []
while begin_date <= end_date:
#date_str = str(begin_date)
date_list.append(begin_date)
begin_date += datetime.timedelta(days=1)
return date_list
#获取感兴趣的所有股票信息，这里只获取沪深300股票
def get_all_stock_id():
stock_info=ts.get_hs300s()
return stock_info['code'].values
#从TuShare下载感兴趣的所有股票的历史成交数据，并保存到本地HDF5压缩文件
#dates=get_date_list(datetime.date(2017,11,6), datetime.date(2017,11,12))
dates=get_date_list(datetime.date(2017,10,30), datetime.date(2017,11,4))
stocks=get_all_stock_id()
for stock in stocks:
for date in dates:
if get_save_tick_data(stock, date):
time.sleep(sleep_time)
因为TuShare并没有提供1分钟线的信息，所以需要根据下载到的每日成交信息生成1分钟线信息。
代码如下： 其实就是不用for和列，直接 newdf = df.resample ... 保存列头一致就好了
#根据分笔成交数据生成1分钟线
#根据分笔成交数据生成1分钟线
def gen_min_line(symbol, date):
global data_dir
str_date=str(date)
dir=data_dir+symbol+'\\'+str(date.year)+'\\'+str(date.month)
tickfile=dir+'\\'+symbol+'_'+str_date+'_tick_data.h5'
minfile=dir+'\\'+symbol+'_'+str_date+'_1min_data.h5'
if (os.path.exists(tickfile)) and (not os.path.exists(minfile)):
hdf5_file=pd.HDFStore(tickfile, 'r')
df=hdf5_file['data']
hdf5_file.close()
print "Successfully read tick file: "+tickfile
if df.shape[0]<10: #TuShare即便在停牌期间也会返回tick data，并且只有三行错误的数据，这里利用行数小于10把那些unexpected tickdata数据排除掉
print "No tick data read from tick file, skip generating 1min line"
return 0
df['time']=str_date+' '+df['time']
df['time']=pd.to_datetime(df['time'])
df=df.set_index('time')
price_df=df['price'].resample('1min').ohlc()
price_df=price_df.dropna()
vols=df['volume'].resample('1min').sum()
vols=vols.dropna()
vol_df=pd.DataFrame(vols,columns=['volume'])
amounts=df['amount'].resample('1min').sum()
amounts=amounts.dropna()
amount_df=pd.DataFrame(amounts,columns=['amount'])
newdf=price_df.merge(vol_df, left_index=True, right_index=True).merge(amount_df, left_index=True, right_index=True)
hdf5_file2=pd.HDFStore(minfile, 'w')
hdf5_file2['data']=newdf
hdf5_file2.close()
print "Successfully write to minute file: "+minfile
dates=get_date_list(datetime.date(2017,8,1), datetime.date(2017,11,12))
stocks=get_all_stock_id()
for stock in stocks:
for date in dates:
gen_min_line(stock, date)
refer to:https://blog.csdn.net/wqfhenanxc/article/details/78525730