def test2():
ts_pro = ls.get_ts_pro()
print(ts_pro)
df = ts_pro.index_basic(market='SZSE')
print(df)
def update_db_stock_basic(list_status):
pro = ls.get_ts_pro()
print(pro)
data = pro.stock_basic(
exchange_id='',
list_status=list_status,
fields='ts_code,symbol,name,area,industry,fullname,enname,'
'market,exchange_id,curr_type,list_status,list_date,delist_date,is_hs')
if data.shape[0] > 0:
for row in data.iterrows():
ts_code, symbol, name, area, industry, fullname, enname, market, exchange_id, curr_type, list_status, list_date, delist_date, is_hs = \
row[-1]
stock_basic = StockBasic(ts_code=ts_code,
symbol=symbol,
name=name,
area=area,
industry=industry,
fullname=fullname,
enname=enname,
market=market,
exchange_id=exchange_id,
curr_type=curr_type,
list_status=list_status,
list_date=list_date,
delist_date=delist_date,
is_hs=is_hs)
stock_basic.save()
def stock_dividend_save(ts_code):
print("stock_dividend_save :%s" % ts_code)
ts_pro = ls.get_ts_pro()
print(ts_pro)
if ts_code is None:
raise Exception
df = ts_pro.dividend(ts_code=ts_code)
df.to_csv(__path_dividend__ + ts_code + __csv__)
return df
def stock_daily_save(ts_code):
ts_pro = ls.get_ts_pro()
print(ts_pro)
df1 = ts_pro.daily(ts_code=ts_code,
start_date='19900101',
end_date='19941231')
df1['date'] = pd.to_datetime(df1['trade_date'])
df1.set_index("date", inplace=True)
df2 = ts_pro.daily(ts_code=ts_code,
start_date='19950101',
end_date='19991231')
df2['date'] = pd.to_datetime(df2['trade_date'])
df2.set_index("date", inplace=True)
df3 = ts_pro.daily(ts_code=ts_code,
start_date='20000101',
end_date='20041231')
df3['date'] = pd.to_datetime(df3['trade_date'])
df3.set_index("date", inplace=True)
df4 = ts_pro.daily(ts_code=ts_code,
start_date='20050101',
end_date='20091231')
df4['date'] = pd.to_datetime(df4['trade_date'])
df4.set_index("date", inplace=True)
df5 = ts_pro.daily(ts_code=ts_code,
start_date='20100101',
end_date='20141231')
df5['date'] = pd.to_datetime(df5['trade_date'])
df5.set_index("date", inplace=True)
df6 = ts_pro.daily(ts_code=ts_code,
start_date='20150101',
end_date='20191231')
df6['date'] = pd.to_datetime(df6['trade_date'])
df6.set_index("date", inplace=True)
# df11 = pd.DataFrame(df1)
# df22 = pd.DataFrame(df2)
# df = pd.concat(df11, df22)
"""
df1['date'] = pd.to_datetime(df1['trade_date'])
df1.set_index("date", inplace=True)
df2['date'] = pd.to_datetime(df2['trade_date'])
df2.set_index("date", inplace=True)
"""
df = pd.concat([df1, df2, df3, df4, df5, df6])
df = df.sort_index()
df.to_csv(__path_daily__ + ts_code + __csv__)
return df
def stock_basic_save(path):
file_path = __path__ if path is None else path
ts_pro = ls.get_ts_pro()
print(ts_pro)
df = ts_pro.stock_basic(
list_status='L',
fields=
'ts_code,symbol,name,fullname,enname,exchange_id,curr_type,list_date,is_hs'
)
df.to_csv(__path__ + "stock_basic.csv")
return df
def test1():
ts_pro = ls.get_ts_pro()
print(ts_pro)
df1 = ts_pro.daily(ts_code='000001.SZ',
start_date='19900101',
end_date='19941231')
df2 = ts_pro.daily(ts_code='000001.SZ',
start_date='19950101',
end_date='19991231')
df3 = ts_pro.daily(ts_code='000001.SZ',
start_date='20000101',
end_date='20041231')
df4 = ts_pro.daily(ts_code='000001.SZ',
start_date='20050101',
end_date='20091231')
df5 = ts_pro.daily(ts_code='000001.SZ',
start_date='20100101',
end_date='20141231')
df6 = ts_pro.daily(ts_code='000001.SZ',
start_date='20150101',
end_date='20191231')
# df11 = pd.DataFrame(df1)
# df22 = pd.DataFrame(df2)
# df = pd.concat(df11, df22)
"""
df1['date'] = pd.to_datetime(df1['trade_date'])
df1.set_index("date", inplace=True)
df2['date'] = pd.to_datetime(df2['trade_date'])
df2.set_index("date", inplace=True)
"""
df = pd.concat([df1, df2, df3, df4, df5, df6])
print(df.dtypes)
print(df.head())
print(df.tail())
def test3():
ts_pro = ls.get_ts_pro()
df = ts.get_realtime_quotes('601318')
print(df)
"""
挖地兔微信号
https://mp.weixin.qq.com/s/P4Suh-JnBsI9cA43GuftuQ
"""
import os
import sys
sys.path.insert(0, '/Users/momantang/PycharmProjects/cobrass/')
sys.path
import tushare as ts
import tushare.futures
import pandas as pd
from local import local_setting as ls
pro = ls.get_ts_pro()
df = pro.stock_basic(exchange_id='',
list_status='L',
fields='ts_code,name,area,industry,list_date,market')
data = pd.crosstab(df.area, df.market)
print(data)