def getPAIRdata_memory(code1, code2, dict): #数据整理,基于内存
#global startdate
#print(dict.get(code1))
#print(dict)
startdate = dict.get('startdate')
enddate = dict.get('enddate')
if dict.get(code1) is None:
dict[code1] = dbQueryTools.queryMySQL(code1, startdate, enddate)
if dict.get(code2) is None:
dict[code2] = dbQueryTools.queryMySQL(code2, startdate, enddate)
df_X = dict.get(code1)
df_Y = dict.get(code2)
if df_X.shape[0] < 1:
return df_X
df = pd.DataFrame(index=df_Y.index)
if df_Y.shape[0] < 1:
return df_Y
df["X"] = df_X["close"]
df["Y"] = df_Y["close"]
df3 = df.sort_index(axis=0, ascending=True)
#pd.set_option('display.max_rows', None) # 打印所有行
df3 = df3.dropna(axis=0, how='any') # 删除表中任何含有NaN的行
#print(df3)
return df3
def getPAIRdata_database(code1, code2): #数据整理,基于数据库
####df_X = ts.get_hist_data(code1, start='2017-12-09', end='2018-12-14')#时间区间设置
####df_Y = ts.get_hist_data(code2, start='2017-12-09', end='2018-12-09')
#try:
#df_X = ts.get_hist_data(code1, startdate, enddate)
#df_Y = ts.get_hist_data(code2, startdate, enddate)
# df = ts.get_hist_data('600848') #一次性获取全部日k线数据
#from findOLS import dbQueryTools
df_X = dbQueryTools.queryMySQL(code1, startdate, enddate)
df_Y = dbQueryTools.queryMySQL(code2, startdate, enddate)
#print(df_Y)
df_X['date1'] = df_X.index
df_X['date2'] = df_X[
'date1'] #df_X['date1'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d')) # 必须两个datetime
df_X = df_X.set_index(['date2'])
# print(df_X)
df_Y['date1'] = df_Y.index
df_Y['date2'] = df_Y[
'date1'] #df_Y['date1'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d')) # 必须两个datetime
df_Y = df_Y.set_index(['date2'])
# print(df_Y)
df = pd.DataFrame(index=df_Y.index)
df["X"] = df_X["close"]
df["Y"] = df_Y["close"]
df3 = df.sort_index(axis=0, ascending=True)
pd.set_option('display.max_rows', None) # 打印所有行
df3 = df3.dropna(axis=0, how='any') # 删除表中任何含有NaN的行
#print(df3)
return df3
def getPAIRdata_database(code1, code2): #数据整理,基于数据库
df_X = dbQueryTools.queryMySQL(code1, startdate, enddate)
df_Y = dbQueryTools.queryMySQL(code2, startdate, enddate)
#print(df_Y)
df_X['date1'] = df_X.index
df_X['date2'] = df_X[
'date1'] #df_X['date1'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d')) # 必须两个datetime
df_X = df_X.set_index(['date2'])
# print(df_X)
df_Y['date1'] = df_Y.index
df_Y['date2'] = df_Y[
'date1'] #df_Y['date1'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d')) # 必须两个datetime
df_Y = df_Y.set_index(['date2'])
# print(df_Y)
df = pd.DataFrame(index=df_Y.index)
df["X"] = df_X["close"]
df["Y"] = df_Y["close"]
df3 = df.sort_index(axis=0, ascending=True)
pd.set_option('display.max_rows', None) # 打印所有行
df3 = df3.dropna(axis=0, how='any') # 删除表中任何含有NaN的行
#print(df3)
return df3
