def get_multiday_ave_compare(code, dayl='10'):
dtick = ts.get_today_ticks(code)
d_hist = ema.getdata_ema_trend(code, dayl, 'd')
# print d_hist
day_t = ema.get_today()
if d_hist is not None:
if day_t in d_hist.index:
dl = d_hist.drop(day_t).index
else:
dl = d_hist.index
else:
return 0
# print dl
# print dl
ep_list = []
for da in dl.values:
# print da
td = ts.get_tick_data(code, da)
# print td
if not type(td) == types.NoneType:
ep = td['amount'].sum() / td['volume'].sum()
ep_list.append(ep)
print("D: %s P: %s" % (da[-5:], ep))
ave = ema.less_average(ep_list)
if len(dtick.index) > 0:
ep = dtick['amount'].sum() / dtick['volume'].sum()
p_now = dtick['price'].values[0] * 100
if p_now > ave and ep > ave:
print("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" %
(code, ep, p_now, ave, cct.get_now_time()))
elif p_now > ave and ep < ave:
print("gold:%s ep:%s UP:%s! A:%s %s !" %
(code, ep, p_now, ave, cct.get_now_time()))
elif p_now < ave and ep > ave:
print("down:%s ep:%s Dow:%s? A:%s %s ?" %
(code, ep, p_now, ave, cct.get_now_time()))
else:
print("DOWN:%s ep:%s now:%s??? A:%s %s ???" %
(code, ep, p_now, ave, cct.get_now_time()))
return ave
top_end = top_dif[:5].copy()
top_temp = top_dif[-ct.PowerCount:].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)
cct.set_console(width, height,
title=[du_date, 'dT:%s' % cct.get_time_to_date(time_s), 'G:%s' % goldstock,
'zxg: %s' % (blkname+'-'+market_blk)])
top_all = tdd.get_powerdf_to_all(top_all, top_temp)
top_all = tdd.get_powerdf_to_all(top_all, top_end)
top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl,resample=resample)
print("N:%s K:%s %s G:%s" % (
now_count, len(top_all[top_all['buy'] > 0]),
len(top_now[top_now['volume'] <= 0]), goldstock)),
print "Rt:%0.1f dT:%s N:%s T:%s %s%%" % (float(time.time() - time_Rt), cct.get_time_to_date(time_s), cct.get_now_time(), len(top_temp), round(len(top_temp) / float(ct.PowerCount) * 100, 1))
# top_end = stf.getBollFilter(df=top_end, boll=ct.bollFilter,duration=ct.PowerCountdl)
if 'op' in top_temp.columns:
if cct.get_now_time_int() > ct.checkfilter_end_timeDu and (int(duration_date) > int(ct.duration_date_sort) or int(duration_date) < ct.duration_diff):
top_temp = top_temp.sort_values(by=eval(market_sort_value),
ascending=market_sort_value_key)
else:
top_temp = top_temp.sort_values(by=eval(market_sort_value),
ascending=market_sort_value_key)
if cct.get_now_time_int() > 915 and cct.get_now_time_int() < 935:
# top_temp = top_temp[ (top_temp['ma5d'] > top_temp['ma10d']) & (top_temp['buy'] > top_temp['ma10d']) ][:10]
top_dd = cct.combine_dataFrame(top_temp[:10], top_end,append=True, clean=True)
# top_dd = top_dd.drop_duplicates()
ct_Duration_format_Values = ct.get_Duration_format_Values(ct.Duration_format_buy, market_sort_value[:])
'zxg: %s' % (blkname+'-'+market_blk)])
# print len(top_all),top_all.shape
top_all = tdd.get_powerdf_to_all(top_all, top_temp)
top_all = tdd.get_powerdf_to_all(top_all, top_end)
# top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter,duration=ct.PowerCountdl, filter=True, percent=True, resample=resample)
# top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter,duration=ct.PowerCountdl, filter=True, percent=True, resample=resample)
top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, filter=True, ma5d=False, dl=14, percent=False, resample=resample, ene=False,cuminTrend=False)
# top_end = stf.getBollFilter(df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, filter=False, ma5d=False, dl=14, percent=False, resample=resample, ene=False)
print(("N:%s K:%s %s G:%s" % (
now_count, len(top_all[top_all['buy'] > 0]),
len(top_now[top_now['volume'] <= 0]), goldstock)), end=' ')
nhigh = top_temp[top_temp.close > top_temp.nhigh] if 'nhigh' in top_temp.columns.values else []
nlow = top_temp[top_temp.close > top_temp.nlow] if 'nlow' in top_temp.columns.values else []
print("Rt:%0.1f dT:%s N:%s T:%s %s%% nh:%s nlow:%s" % (float(time.time() - time_Rt), cct.get_time_to_date(time_s), cct.get_now_time(), len(top_temp), round(len(top_temp) / float(ct.PowerCount) * 100, 1),len(nhigh),len(nlow)))
# top_end = stf.getBollFilter(df=top_end, boll=ct.bollFilter,duration=ct.PowerCountdl,filter=False)
if 'op' in top_temp.columns:
# if ptype == 'low':
# top_temp = top_temp.sort_values(by=ct.Duration_sort_op,
# ascending=ct.Duration_sort_op_key)
# else:
# top_temp = top_temp.sort_values(by=ct.Duration_sort_high_op,
# ascending=ct.Duration_sort_high_op_key)
# top_temp=top_temp[top_temp.op >12]
# top_temp = top_temp.sort_values(by=['ra', 'op'],ascending=[0, 0])[:10]
# top_temp = top_temp.sort_values(by=['dff', 'op', 'ra', 'percent', 'ratio'],
# ascending=[0, 0, 0, 0, 1])[:10]
# top_temp = top_temp.sort_values(by=['op', 'ra', 'dff', 'percent', 'ratio'],
& (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)
top_temp = stf.getBollFilter(df=top_temp, boll=1)
print "G:%s Rt:%0.1f dT:%s N:%s T:%s" % (
goldstock, float(time.time() - time_Rt),
cct.get_time_to_date(time_s), cct.get_now_time(),
len(top_temp))
if 'op' in top_temp.columns:
# top_temp = top_temp.sort_values(by=['ra','op','couts'],ascending=[0, 0,0])
# top_temp = top_temp.sort_values(by=['dff', 'op', 'ra', 'percent', 'ratio'],
# top_temp = top_temp.sort_values(by=ct.Monitor_sort_op,
# ascending=ct.Monitor_sort_op_key)
# top_temp = top_temp.sort_values(by=ct.Duration_percentdn_ra,
# ascending=ct.Duration_percentdn_ra_key)
# top_temp = top_temp.sort_values(by=ct.Duration_percent_op,
# ascending=ct.Duration_percent_op_key)
top_temp = top_temp.sort_values(
by=(market_sort_value),
ascending=market_sort_value_key)
# top_temp = top_temp.sort_values(by=['op','ra','dff', 'percent', 'ratio'], ascending=[0,0,0, 0, 1])
# if cct.get_now_time_int() > 915 and cct.get_now_time_int() < 935:
top_all.buy >= top_all.lhigh * 0.99) & (top_all.buy >= top_all.llastp * 0.99)])
top_all=tdd.get_powerdf_to_all(top_all, top_temp)
# top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, filter=False)
# top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, filter=False, ma5d=False, dl=14, percent=False, resample='d')
# top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, filter=True, ma5d=True, dl=14, percent=False, resample=resample)
top_temp=stf.getBollFilter(
df=top_temp, resample=resample, down=True)
top_end=stf.getBollFilter(
df=top_end, resample=resample, down=True)
nhigh = top_temp[top_temp.close > top_temp.nhigh] if 'nhigh' in top_temp.columns else []
nlow = top_temp[top_temp.close > top_temp.nlow] if 'nlow' in top_temp.columns else []
print("G:%s Rt:%0.1f dT:%s N:%s T:%s nh:%s nlow:%s" % (goldstock, float(time.time() - time_Rt), cct.get_time_to_date(time_s), cct.get_now_time(), len(top_temp),len(nhigh),len(nlow)))
top_temp=top_temp.sort_values(by=(market_sort_value),
ascending=market_sort_value_key)
ct_MonitorMarket_Values=ct.get_Duration_format_Values(
ct.Monitor_format_trade, market_sort_value[:2])
if len(st_key_sort.split()) < 2:
f_sort=(st_key_sort.split()[0] + ' f ')
else:
if st_key_sort.find('f') > 0:
f_sort=st_key_sort
else:
f_sort=' '.join(x for x in st_key_sort.split()[
:2]) + ' f ' + ' '.join(x for x in st_key_sort.split()[2:])
title=[
'dT:%s' % cct.get_time_to_date(time_s),
'G:%s' % len(top_dif),
'zxg: %s' % (blkname + '-' + market_blk)
])
top_all = tdd.get_powerdf_to_all(top_all, top_temp)
top_temp = stf.getBollFilter(df=top_temp,
boll=ct.bollFilter,
duration=ct.PowerCountdl)
print("A:%s N:%s K:%s %s G:%s" %
(df_count, now_count, len(top_all[top_all['buy'] > 0]),
len(top_now[top_now['volume'] <= 0]), goldstock)),
print "Rt:%0.1f dT:%s N:%s T:%s %s%%" % (
float(time.time() - time_Rt), cct.get_time_to_date(time_s),
cct.get_now_time(), len(top_temp),
round(len(top_temp) / float(ct.PowerCount) * 100, 1))
if 'op' in top_temp.columns:
top_temp = top_temp.sort_values(
by=(market_sort_value),
ascending=market_sort_value_key)
if st_key_sort.split()[0] == 'x':
top_temp = top_temp[top_temp.topR != 0]
# if cct.get_now_time_int() > 915 and cct.get_now_time_int() < 935:
# top_temp = top_temp.loc[:,ct.MonitorMarket_format_buy]
# else:
# top_temp = top_temp.loc[:,ct.MonitorMarket_format_buy]
ct_MonitorMarket_Values = ct.get_Duration_format_Values(
cct.set_console(width, height,
title=[du_date, 'dT:%s' % cct.get_time_to_date(time_s), 'G:%s' % goldstock,
'zxg: %s' % (blkname + '-' + market_blk)])
# top_all = tdd.get_powerdf_to_all(top_all, top_temp)
top_all = tdd.get_powerdf_to_all(top_all, top_end)
top_temp = stf.getBollFilter(
df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, upper=True, resample=resample)
# df=top_temp, boll=11, duration=ct.PowerCountdl, upper=False, resample=resample)
# df=top_temp, boll=ct.bollFilter, duration=ct.PowerCountdl, upper=False, resample=resample)
print("N:%s K:%s %s G:%s" % (
now_count, len(top_all[top_all['buy'] > 0]),
len(top_now[top_now['volume'] <= 0]), goldstock)),
print ("Rt:%0.1f dT:%s N:%s T:%s %s%%" % (float(time.time() - time_Rt), cct.get_time_to_date(
time_s), cct.get_now_time(), len(top_temp), round(len(top_temp) / float(ct.PowerCount) * 100, 1)))
# print round(len(top_temp)/now_count*100,1)
# print len(top_temp)/now_count*100
# top_end = stf.getBollFilter(df=top_end, boll=ct.bollFilter,duration=ct.PowerCountdl)
if 'op' in top_temp.columns:
# if ptype == 'low':
# top_temp = top_temp.sort_values(by=ct.Duration_sort_op,
# ascending=ct.Duration_sort_op_key)
# else:
# top_temp = top_temp.sort_values(by=ct.Duration_sort_high_op,
# ascending=ct.Duration_sort_high_op_key)
# top_temp=top_temp[top_temp.op >12]
# top_temp = top_temp.sort_values(by=['ra', 'op'],ascending=[0, 0])[:10]
# top_temp = top_temp.sort_values(by=['dff', 'op', 'ra', 'percent', 'ratio'],
