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)