''' 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