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[:])
top_dd = top_dd.loc[:, ct_Duration_format_Values]
else:
# top_temp = top_temp[ (top_temp['ma5d'] > top_temp['ma10d']) & (top_temp['trade'] > top_temp['ma10d']) ][:10]
# top_temp = top_temp[top_temp['trade'] > top_temp['ma10d']]
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_trade, market_sort_value[:])
top_dd = top_dd.loc[:, ct_Duration_format_Values]
print cct.format_for_print(top_dd)
# dfgui.show(top_dif)
# if cct.get_now_time_int() < 930 or cct.get_now_time_int() > 1505 or (cct.get_now_time_int() > 1125 and cct.get_now_time_int() < 1505):
# print cct.format_for_print(top_dif[-10:])
# print top_all.loc['000025',:]
# print "staus",status
else:
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[top_temp['buy'] > top_temp['ma10d']]
# top_temp = top_temp[top_temp['ma5d'] > top_temp['ma10d']][:10]
# 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[:5],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[:])
else:
# top_temp = top_temp[top_temp['trade'] > top_temp['ma10d']]
# top_temp = top_temp[top_temp['ma5d'] > top_temp['ma10d']][:10]
# top_temp = top_temp[ (top_temp['ma5d'] > top_temp['ma10d']) & (top_temp['trade'] > top_temp['ma10d']) ][:10]
top_dd = cct.combine_dataFrame(top_temp[:10], top_end[:5],append=True, clean=True)
ct_Duration_format_Values = ct.get_Duration_format_Values(ct.Duration_format_trade, market_sort_value[:])
# ct_Duration_format_Values = ct.get_Duration_format_Values(ct_Duration_format_Values,replace='couts',dest='stdv')
# ct_Duration_format_Values = ct.get_Duration_format_Values(ct_Duration_format_Values,replace='boll',dest='upper')
if 'b1_v' in ct_Duration_format_Values:
ct_Duration_format_Values = ct.get_Duration_format_Values(ct_Duration_format_Values,replace='b1_v',dest='perc3d')
ct_Duration_format_Values = ct.get_Duration_format_Values(ct_Duration_format_Values,replace='couts',dest='b1_v')
# 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:])
market_sort_value2, market_sort_value_key2=ct.get_market_sort_value_key(
f_sort, top_all=top_all)
# ct_MonitorMarket_Values2=ct.get_Duration_format_Values(
# ct.Monitor_format_trade, market_sort_value2[:2])
# 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_temp = top_temp.loc[:,ct.Monitor_format_trade]
# else:
# top_temp = top_temp.loc[:,ct.Monitor_format_trade]
if st_key_sort.split()[0] == 'x':
top_temp = top_temp[top_temp.topR != 0]
ct_MonitorMarket_Values = ct.get_Duration_format_Values(
ct.Monitor_format_trade, market_sort_value[:2])
print cct.format_for_print(
top_temp.loc[:, ct_MonitorMarket_Values][:10])
# print cct.format_for_print(top_all[:10])
if status:
for code in top_all[:10].index:
code = re.findall('(\d+)', code)
if len(code) > 0:
code = code[0]
kind = sl.get_multiday_ave_compare_silent(code)
else:
# print top_now[:10]
print "\tNo data"
int_time = cct.get_now_time_int()
if cct.get_work_time():
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[top_temp['ma5d'] > top_temp['ma10d']][:10]
# 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[:])
else:
# top_temp = top_temp[top_temp['trade'] > top_temp['ma10d']]
# top_temp = top_temp[top_temp['ma5d'] > top_temp['ma10d']][:10]
# top_temp = top_temp[ (top_temp['ma5d'] > top_temp['ma10d']) & (top_temp['trade'] > 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_trade, market_sort_value[:])
# ct_Duration_format_Values = ct.get_Duration_format_Values(ct_Duration_format_Values,replace='couts',dest='stdv')
ct_Duration_format_Values = ct.get_Duration_format_Values(
ct_Duration_format_Values,
