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 rl.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() > 935
or cct.get_now_time_int() <
900) and ptype == 'low':
top_dif = top_dif[top_dif.percent >= 0]
top_temp = top_dif[:ct.PowerCount].copy()
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 = top_dif[:ct.PowerCount].copy()
top_end = top_dif[-5:].copy()
top_temp = pct.powerCompute_df(top_temp,
dl=ct.PowerCountdl,
talib=True,
newdays=newdays)
& (top_all.low >= top_all.ma51d) &
(top_all.lasth1d > top_all.lasth2d)]
# top_temp = top_all[((top_all.open > top_all.lastp1d)) & (
# top_all.low >= top_all.lastl1d) & (top_all.lasth1d > top_all.lasth2d)]
# top_temp = top_all[ (top_all.low >= top_all.lastl1d) & (top_all.lasth1d > top_all.lasth2d) & (top_all.low >= top_all.nlow) & ((top_all.open >= top_all.nlow *0.998) & (top_all.open <= top_all.nlow*1.002)) ]
# top_temp = top_all[ (top_all.volume >= 1.2 ) & (top_all.low >= top_all.lastl1d) & (top_all.lasth1d > top_all.lasth2d) & (top_all.close > top_all.lastp1d)]
else:
top_temp = top_all.copy()
# if st_key_sort != '4':
# top_temp = stf.filterPowerCount(top_temp, ct.PowerCount)
top_end = top_all[-int((ct.PowerCount) / 10):].copy()
top_temp = pct.powerCompute_df(top_temp,
dl=ct.PowerCountdl,
talib=True)
top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl)
goldstock = len(
top_dif[(top_dif.buy >= top_dif.lhigh * 0.99)
& (top_dif.buy >= top_dif.llastp * 0.99)])
cct.set_console(width,
height,
title=[
'dT:%s' % cct.get_time_to_date(time_s),
'G:%s' % len(top_dif),
'zxg: %s' %
(blkname + '-' + market_blk)
])
else:
if 'couts' in top_dif.columns.values:
top_dif = top_dif.sort_values(
by=ct.Monitor_sort_count,
ascending=[0, 0, 0, 1, 1])
else:
# print "Good Morning!!!"
top_dif = top_dif.sort_values(
by=['dff', 'percent', 'ratio'],
ascending=[0, 0, 1])
# top_all=top_all.sort_values(by=['percent','dff','couts','ratio'],ascending=[0,0,1,1])
# print rl.format_for_print(top_dif[:10])
top_temp = top_dif[:ct.PowerCount].copy()
top_temp = pct.powerCompute_df(top_temp,
dl=ct.PowerCountdl)
goldstock = len(
top_dif[(top_dif.buy >= top_dif.lhigh * 0.99)
& (top_dif.buy >= top_dif.llastp * 0.99)])
cct.set_console(width,
height,
title=[
'dT:%s' % cct.get_time_to_date(time_s),
'G:%s' % len(top_dif),
'zxg: %s' % (blkname)
])
top_all = tdd.get_powerdf_to_all(top_all, top_temp)
top_temp = stf.getBollFilter(df=top_temp,
boll=ct.bollFilter,