''' Constants'''
delay = 1
start_date = str(20100101)
end_date = str(20181231)
histdays = 20 # need histdays >= delay
actdays = get_datelist(start_date, end_date, histdays, -1)
days = [
int(d.replace('-', '')) for d in meta_api.get_trading_date_range(
int(start_date), int(end_date), 'SSE')
]
daysdata = helpfunc_loadcache(actdays[0], actdays[-1], 'days')
symbols = helpfunc_loadcache(actdays[0], actdays[-1], 'stocks')
instruments = len(symbols)
startdi = daysdata.tolist().index(days[0])
enddi = startdi + len(days) - 1
'Data Part'
vwap = helpfunc_loadcache(actdays[0], actdays[-1], 'vwap', 'basedata')
high = helpfunc_loadcache(actdays[0], actdays[-1], 'high', 'basedata')
low = helpfunc_loadcache(actdays[0], actdays[-1], 'low', 'basedata')
groupdata = helpfunc_loadcache(actdays[0], actdays[-1], 'WIND01', 'basedata')
'Alpha Part'
alpha = np.full([1, enddi - startdi + 1, instruments], np.nan)
''' Constants'''
delay = 1
start_date = str(20100101)
end_date = str(20181231)
histdays = 20 # need histdays >= delay
actdays = get_datelist(start_date, end_date, histdays, -1) #trading day + hist
days = [
int(d.replace('-', '')) for d in meta_api.get_trading_date_range(
int(start_date), int(end_date), 'SSE')
]
#days is just trading day from start to end
daysdata = helpfunc_loadcache(actdays[0], actdays[-1], 'days')
#trading day + hist
symbols = helpfunc_loadcache(actdays[0], actdays[-1], 'stocks')
instruments = len(symbols)
startdi = daysdata.tolist().index(days[0])
#find first day
enddi = startdi + len(days) - 1
'Data Part'
close = helpfunc_loadcache(actdays[0], actdays[-1], 'close', 'basedata')
groupdata = helpfunc_loadcache(actdays[0], actdays[-1], 'WIND01', 'basedata')
''' Constants'''
delay = 1
start_date = str(20100101)
end_date = str(20181231)
histdays = 20 # need histdays >= delay
actdays = get_datelist(start_date, end_date, histdays, -1)
days = [
int(d.replace('-', '')) for d in meta_api.get_trading_date_range(
int(start_date), int(end_date), 'SSE')
]
daysdata = helpfunc_loadcache(actdays[0], actdays[-1], 'days')
symbols = helpfunc_loadcache(actdays[0], actdays[-1], 'stocks')
instruments = len(symbols)
startdi = daysdata.tolist().index(days[0])
enddi = startdi + len(days) - 1
'Data Part'
high = helpfunc_loadcache(actdays[0], actdays[-1], 'high', 'basedata')
ops = helpfunc_loadcache(actdays[0], actdays[-1], 'open', 'basedata')
groupdata = helpfunc_loadcache(actdays[0], actdays[-1], 'WIND01', 'basedata')
alpha = np.full([1, enddi - startdi + 1, instruments], np.nan)
def Rankop_rank(xmatrix):
return pd.DataFrame(xmatrix).rank(pct=True,axis=1).values
''' Constants'''
delay = 1
start_date = str(20100101)
end_date = str(20181231)
histdays = 20 # need histdays >= delay
actdays = get_datelist(start_date,end_date,histdays,-1)#trading day + hist
days = [int(d.replace('-', '')) for d in meta_api.get_trading_date_range(int(start_date), int(end_date), 'SSE')]
#days is just trading day from start to end
daysdata = helpfunc_loadcache(actdays[0],actdays[-1],'days')
#trading day + hist
symbols = helpfunc_loadcache(actdays[0],actdays[-1],'stocks')
instruments = len(symbols)
startdi = daysdata.tolist().index(days[0])
#find first day
enddi = startdi + len(days) - 1
'Data Part'
vwap = helpfunc_loadcache(actdays[0],actdays[-1],'vwap','basedata')
volume = helpfunc_loadcache(actdays[0],actdays[-1],'vol','basedata')
index = np.argwhere(volume == 0)
for item in index:
''' Constants'''
delay = 1
start_date = str(20100101)
end_date = str(20181231)
histdays = 20 # need histdays >= delay
actdays = get_datelist(start_date, end_date, histdays, -1) #trading day + hist
days = [
int(d.replace('-', '')) for d in meta_api.get_trading_date_range(
int(start_date), int(end_date), 'SSE')
]
#days is just trading day from start to end
daysdata = helpfunc_loadcache(actdays[0], actdays[-1], 'days')
#trading day + hist
symbols = helpfunc_loadcache(actdays[0], actdays[-1], 'stocks')
instruments = len(symbols)
startdi = daysdata.tolist().index(days[0])
#find first day
enddi = startdi + len(days) - 1
'Data Part'
volume = helpfunc_loadcache(actdays[0], actdays[-1], 'vol', 'basedata')
index = np.argwhere(volume == 0)
for item in index:
volume[item[0], item[1]] = np.nan