# top_dif['dff'] = top_dif['dff'].apply(lambda x: x * 2 if x > 0 else x)
top_dif = top_dif[top_dif.lvol > ct.LvolumeSize]
top_dif['dff'] = top_dif['dff'].apply(lambda x: x * 2 if x < 0 else x)
if 'couts' in top_dif.columns.values:
top_dif = top_dif.sort_values(by=['dff', 'percent', 'volume', 'couts', 'ratio'],
ascending=[1, 0, 0, 1, 1])
else:
top_dif = top_dif.sort_values(by=['dff', 'percent', 'ratio'], ascending=[1, 0, 1])
# top_all=top_all.sort_values(by=['percent','dff','couts','ratio'],ascending=[0,0,1,1])
# print cct.format_for_print(top_dif[:10])
# top_dd = pd.concat([top_dif[:5],top_temp[:3],top_dif[-3:],top_temp[-3:]], axis=0)
if percent_status == 'y' and (
cct.get_now_time_int() > 915 and cct.get_now_time_int() < 1505) and ptype == 'low':
top_dif = top_dif[top_dif.percent >= 0]
top_temp = stf.filterPowerCount(top_dif,ct.PowerCount)
top_end = top_dif[-5:].copy()
top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl, talib=True, newdays=newdays)
top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl, talib=True, newdays=newdays)
# elif percent_status == 'y' and cct.get_now_time_int() > 935 and ptype == 'high' :
elif ptype == 'low':
# top_dif = top_dif[top_dif.percent >= 0]
top_temp = stf.filterPowerCount(top_dif,ct.PowerCount)
top_end = top_dif[-5:].copy()
top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl, talib=True, newdays=newdays)
top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl, talib=True, newdays=newdays)
else:
# top_dif = top_dif[top_dif.percent >= 0]
top_end = top_dif[:5].copy()
top_temp = top_dif[-ct.PowerCount:].copy()
top_all = top_all.sort_values(by=ct.Monitor_sort_count,
ascending=[0, 0, 0, 0, 1])
# top_all = top_all.sort_values(by=[ 'couts'], ascending=[0])
# top_all=top_all.sort_values(by=['dff','percent','couts','ratio'],ascending=[0,0,1,1])
# top_all=top_all.sort_values(by=['dff','couts'],ascending=[0,0])
# top_all=top_all.sort_values(by=['dff','percent','couts','ratio'],ascending=[0,0,1,1])
# print top_all
# print pt.PrettyTable([''] + list(top_all.columns))
# print tbl.tabulate(top_all,headers='keys', tablefmt='psql')
# print tbl.tabulate(top_all,headers='keys', tablefmt='orgtbl')
# print cct.format_for_print(top_all)
# print top_all[:10]
top_temp = stf.filterPowerCount(top_all, ct.PowerCount)
top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl)
goldstock = len(
top_all[(top_all.buy >= top_all.lhigh * 0.99)
& (top_all.buy >= top_all.llastp * 0.99)])
# print "G:%s Rt:%0.1f dT:%s N:%s" % (len(top_all),float(time.time() -
# time_Rt),cct.get_time_to_date(time_s),cct.get_now_time())
cct.set_console(width,
height,
title=[
'dT:%s' % cct.get_time_to_date(time_s),
'G:%s' % len(top_all),
'zxg: %s' % (blkname + '-' + market_blk)
])
top_all = tdd.get_powerdf_to_all(top_all, top_temp)
