(df_all.tradeAbbrName.str.contains('中证500ETF') == True) |
(df_all.tradeAbbrName.str.contains('沪深300ETF') == True)][[
'secID', 'tradeAbbrName'
]]
#
dbsa = SASQL.ScenarioAnalysis()
#
#
col_query = "select name from syscolumns where id = object_id('{}')".format(
'HistData_Stock')
cols = dbsa.ExecQuery(col_query)
for i in range(0, df.shape[0]):
# query = "delete from HistData_Stock where InstrumentID=''".format(df.iat[i,0])
data = DataAPI.MktFunddGet(
secID=df.iat[i, 0],
beginDate="20150101",
endDate="20181109",
field=
"tradeDate,ticker,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice,CHGPct",
pandas="1")
# data['navChgPct'] = [x[0] for x in np.array(data[['navChgPct']]/100).tolist()]
data.columns = [x[0] for x in cols]
data.to_sql('HistData_Stock',
dbsa.conn,
if_exists='append',
index=False,
chunksize=1000)
# print(data.head(5))
print(df.iat[i, 0])
query = "delete from HistData_Stock where (InstrumentID like '1%' or InstrumentID like '5%') and Date='{}'".format(
date)
dbsa.ExecNonQuery(query)
col_query = "select name from syscolumns where id = object_id('{}')".format(
'HistData_Stock')
cols = dbsa.ExecQuery(col_query)
df_all = DataAPI.FundGet(etfLof="ETF", listStatusCd="L", field="", pandas="1")
df = df_all[(df_all.tradeAbbrName.str.contains('上证50ETF') == True) |
(df_all.tradeAbbrName.str.contains('中证500ETF') == True) |
(df_all.tradeAbbrName.str.contains('沪深300ETF') == True)][[
'secID', 'tradeAbbrName'
]]
ETFli = [x[0] for x in np.array(df[['secID']]).tolist()]
data = DataAPI.MktFunddGet(
secID=ETFli,
tradeDate=date,
field=
"tradeDate,ticker,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice,CHGPct",
pandas="1")
data.columns = [x[0] for x in cols]
data.to_sql('HistData_Stock',
dbsa.conn,
if_exists='append',
index=False,
chunksize=1000)
