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ts_mysql_stock_all_qfq.py
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ts_mysql_stock_all_qfq.py
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"""
The ts_mysql_stock_all_qfq.py（从tushare下载所有股票的前复权数据到mysql数据库）is to download data from https://tushare.pro/document/2?doc_id=25
复权类型(只针对股票)：qfq前复权
1。如果第一次使用这个程序(ts_mysql_stock_all_qfq.py)，那么就需要下载所有的数据（这里默认的时间是从'19900101'开始--preprocess_stockQFQ.py中设置），
那么在"run_stockQFQ.py"中，设置first_update_flag=True
2。如果只是更新当天或者过去几天的数据，则设置first_update_flag=False -- 基础积分每分钟内最多调取200次
Written by mumu-2014 on Oct. 25, 2019 in Shanghai, China.
Modified by mumu-2014 on Dec. 3, 2019 in Shanghai, China.
"""
import tushare as ts
import pymysql
import datetime
import time
import numpy as np
import pandas as pd
def preprocess_stockQFQ(cursor, pro ):
# check download data in mysql database
sql_dabase = 'use ts_stock;'
cursor.execute(sql_dabase)
# -------创建表格---------
sql_comm = "create table if not exists stock_all_qfq " \
"( id int not null auto_increment primary key,"
sql_insert = "INSERT INTO stock_all_qfq( "
sql_value = "VALUES ( "
df = ts.pro_bar( ts_code='000001.SZ', asset='E',
api=pro, adj='qfq', start_date='20190102', end_date='20190104' )
#---改变列名
df.rename( columns={ 'change': 'close_chg' }, inplace=True )
cols = df.columns.tolist()
str_index = []
for ctx in range( 0, len( cols ) ):
col = cols[ctx]
if isinstance(df[col].iloc[0], str):
sql_comm += col + " varchar(40), "
sql_insert += col + ', '
sql_value += "'%s', "
str_index.append(ctx)
elif isinstance( df[col].iloc[0], float ):
sql_comm += col + " decimal(20, 2), "
sql_insert += col + ', '
sql_value += "'%.2f', "
#
sql_comm = sql_comm[0: len(sql_comm) - 2]
sql_comm += ") engine=innodb default charset=utf8mb4;"
#
sql_insert = sql_insert[0: len(sql_insert) - 2]
sql_insert += " )"
sql_value = sql_value[0: len(sql_value) - 2]
sql_value += " )"
#
cursor.execute(sql_comm)
# ------------------------
#
sql_table = "select trade_date from stock_all_qfq where ts_code = '000001.SZ' " \
"order by trade_date desc limit 0, 1; "
cursor.execute(sql_table)
res = cursor.fetchall()
if len(res) == 0:
# =====================设定获取日线行情的初始日期和终止日期=======================
start_dt = '19900101' # '19910101' --- 下载时候中间改错日期
time_temp = datetime.datetime.now() - datetime.timedelta(days=1)
end_dt = time_temp.strftime('%Y%m%d')
print('start_date: ', start_dt, ', end_date: ', end_dt)
else:
last_trade_date = res[0][0]
#
start_dt = (datetime.datetime.strptime(last_trade_date, '%Y%m%d')
+ datetime.timedelta(days=1)).strftime("%Y%m%d")
#
time_temp = datetime.datetime.now()
end_dt = time_temp.strftime('%Y%m%d')
print('start_date: ', start_dt, ', end_date: ', end_dt)
return sql_insert, sql_value, start_dt, end_dt
def mysql_stockQFQ( db, cursor, pro, itx, stock_pool,
start_dt, end_dt, sql_insert, sql_value ):
# -------------获取日线行情数据------------
df = ts.pro_bar( ts_code=stock_pool[ itx ], asset='E',
api=pro, adj='qfq', freq='D',
start_date=start_dt, end_date=end_dt )
#-------modified by mumu-2014 on Dec. 14, 2019 in Shanghai, China----
if len( df ) == 4000: #最多下载4000条记录
last_download_date = df[ 'trade_date' ].iloc[ -1 ]
#
last_download_date = (datetime.datetime.strptime( last_download_date, '%Y%m%d')
- datetime.timedelta(days=1)).strftime("%Y%m%d")
df2 = ts.pro_bar( ts_code=stock_pool[ itx ], asset='E',
api=pro, adj='qfq', freq='D',
start_date=start_dt, end_date=last_download_date )
if len(df2 ) > 0:
df = pd.concat( [ df, df2 ], axis=0 )
if df is not None:
#---改变列名
df.rename( columns={ 'change': 'close_chg' }, inplace=True )
df.drop_duplicates( inplace=True )
df = df.sort_values( by=[ 'trade_date' ], ascending=False )
df.reset_index( inplace=True, drop=True )
c_len = df.shape[0]
for jtx in range( 0, c_len ):
resu0 = list( df.iloc[ c_len - 1 - jtx ] )
resu = []
for k in range( len( resu0 ) ):
if isinstance( resu0[ k ], str ):
resu.append( resu0[ k ] )
elif isinstance( resu0[ k ], float ):
if np.isnan( resu0[k] ):
resu.append( -1 )
else:
resu.append( resu0[ k ] )
elif resu0[ k ] == None:
resu.append( -1 )
#save into mysql database
try:
sql_impl = sql_insert + sql_value
sql_impl = sql_impl % tuple( resu )
cursor.execute(sql_impl )
db.commit()
except Exception as err:
print( err )
continue
def run_stockQFQ( db, pro, first_update_flag=False ):
# -----查询当前所有正常上市交易的股票列表---------
data = pro.stock_basic(exchange='', list_status='L' )
# 设定需要获取数据的股票池
stock_pool = data['ts_code'].tolist()
#print( stock_pool.index( '002427.SZ') )
# ----- create an object cursor: 模块主要的作用就是用来和数据库交互的
cursor = db.cursor()
# 获得跟数据库互动的参数
sql_insert, sql_value, start_dt, end_dt = preprocess_stockQFQ( cursor, pro )
if first_update_flag:
itx = 0
while itx < len(stock_pool):
print('itx = ', itx, ', code = ', stock_pool[itx])
mysql_stockQFQ(db, cursor, pro, itx, stock_pool, start_dt, end_dt, sql_insert, sql_value)
# ======update index=========
itx += 1
else:
itx = 0
itx_org = itx
time_start = int(time.time())
while itx < len( stock_pool ):
print('itx = ', itx, ', code = ', stock_pool[itx])
mysql_stockQFQ(db, cursor, pro, itx, stock_pool, start_dt, end_dt, sql_insert, sql_value)
#---
time_curr = int(time.time())
if ( ( itx - itx_org ) ) >= 198 or ( ( int( time.time() ) - time_start ) > 55 ):
print('Enter sleep......, ', (itx - itx_org), ', time: ', (time_curr - time_start))
time.sleep( 60 )
itx_org = itx
time_start = int( time.time() )
#======update index=========
itx += 1
#========================================
print('All Finished!')
#------------
cursor.close()
db.close()
#-----使用ts.pro_bar()获得batch——code（100个）-------
def mysql_stockQFQ_batch( db, cursor, pro, batch_codes, start_dt, end_dt, sql_insert, sql_value ):
# -------------获取获取股票行情数据------------
df = ts.pro_bar( ts_code=batch_codes, asset='E',
api=pro, adj='qfq', freq='D',
start_date=start_dt, end_date=end_dt )
#
if df is not None:
#---改变列名
df.rename( columns={ 'change': 'close_chg' }, inplace=True )
df.drop_duplicates( inplace=True )
df = df.sort_values( by=[ 'trade_date' ], ascending=False )
df.reset_index( inplace=True, drop=True )
c_len = df.shape[0]
for jtx in range( 0, c_len ):
resu0 = list( df.iloc[ c_len - 1 - jtx ] )
resu = []
for k in range( len( resu0 ) ):
if isinstance( resu0[ k ], str ):
resu.append( resu0[ k ] )
elif isinstance( resu0[ k ], float ):
if np.isnan( resu0[k] ):
resu.append( -1 )
else:
resu.append( resu0[ k ] )
elif resu0[ k ] == None:
resu.append( -1 )
#save into mysql database
try:
sql_impl = sql_insert + sql_value
sql_impl = sql_impl % tuple( resu )
cursor.execute(sql_impl )
db.commit()
except Exception as err:
print( err )
continue
def run_stockQFQ_batch( db, pro, first_update_flag=False ):
# -----查询当前所有正常上市交易的股票列表---------
data = pro.stock_basic(exchange='', list_status='L' )
# 设定需要获取数据的股票池
stock_pool = data['ts_code'].tolist()
#print( stock_pool.index( '002427.SZ') )
# ----- create an object cursor: 模块主要的作用就是用来和数据库交互的
cursor = db.cursor()
# 获得跟数据库互动的参数
sql_insert, sql_value, start_dt, end_dt = preprocess_stockQFQ( cursor, pro )
if first_update_flag:
itx = 0
while itx < len(stock_pool):
print('itx = ', itx, ', code = ', stock_pool[itx])
mysql_stockQFQ(db, cursor, pro, itx, stock_pool, start_dt, end_dt, sql_insert, sql_value)
# ======update index=========
itx += 1
else:
btx = 0
batch_size = 100
batch_index = np.arange( len( stock_pool ) / batch_size + 1)
while btx < len( batch_index ):
batch_start = btx * batch_size
batch_end = ( btx + 1 ) * batch_size
batch_codes = ", ".join( stock_pool[ batch_start : batch_end ] )
print('btx = ', btx, ', batch_codes: ', batch_codes )
#
mysql_stockQFQ_batch(db, cursor, pro, batch_codes, start_dt, end_dt, sql_insert, sql_value)
#======update index=========
btx += 1
#========================================
print('All Finished!')
#------------
cursor.close()
db.close()
if __name__ == '__main__':
# ===============建立数据库连接,剔除已入库的部分============================
# connect database
config = {
'host': 'localhost',
'user': 'root',
'password': '123456',
'database': 'ts_stock',
'charset': 'utf8'
}
db = pymysql.connect( **config )
# -----------设置tushare pro的token并获取连接---------------
token = 'xxxx'
pro = ts.pro_api( token )
#如何第一次使用这个程序，那么需要下载所有的数据：
# 这里默认的时间是从'19900101'开始--preprocess_stockQFQ.py中设置
# first_update_flag=True
#如何只是更新当天或者过去几天的数据，则设置first_update_flag=False -- 基础积分每分钟内最多调取200次
#使用通用行情接口
#run_stockQFQ(db, pro, first_update_flag=True )
#modified by mumu-2014 on Dec. 3, 2019:
# 使用batch_code 每次读取100只股票信息而不是每次读取一只股票
# 然后再按照原来的方法入库
run_stockQFQ_batch(db, pro, first_update_flag=True)