log.debug('dif6 vol:%s' % (top_dif[:1].volume))
log.debug('dif6 vol>lvol:%s' % len(top_dif))
# top_dif = top_dif[top_dif.buy >= top_dif.open*0.99]
# log.debug('dif5 buy>open:%s'%len(top_dif))
# top_dif = top_dif[top_dif.trade >= top_dif.buy]
# df['volume']= df['volume'].apply(lambda x:x/100)
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]), len(top_dif))),
# print "Rt:%0.3f" % (float(time.time() - time_Rt))
print "Rt:%0.1f dT:%s" % (float(time.time() - time_Rt),cct.get_time_to_date(time_s))
if 'counts' in top_dif.columns.values:
top_dif = top_dif.sort_values(by=['diff', 'volume', 'percent', 'counts', 'ratio'],
ascending=[0, 0, 0, 1, 1])
else:
# print "Good Morning!!!"
top_dif = top_dif.sort_values(by=['diff', 'percent', 'ratio'], ascending=[0, 0, 1])
# top_all=top_all.sort_values(by=['percent','diff','counts','ratio'],ascending=[0,0,1,1])
# print rl.format_for_print(top_dif[:10])
print rl.format_for_print(top_dif[:10])
# print top_all.loc['000025',:]
# print "staus",status
if status:
for code in top_dif[:10].index:
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)
])
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)
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(),
top_all = top_all[top_all["llow"] > 0]
log.info("df:%s" % top_all[:1])
radio_t = cct.get_work_time_ratio()
log.debug("Second:vol/vol/:%s" % radio_t)
# top_dif['volume'] = top_dif['volume'].apply(lambda x: round(x / radio_t, 1))
log.debug("top_diff:vol")
top_all["volume"] = map(
lambda x, y: round(x / y / radio_t, 1), top_all["volume"].values, top_all["lvol"].values
)
# top_all = top_all[top_all.prev_p >= top_all.lhigh]
top_all = top_all.loc[
:, ["name", "percent", "diff", "counts", "volume", "trade", "prev_p", "ratio"]
]
print "G:%s dt:%s" % (len(top_all), cct.get_time_to_date(time_s))
top_all = top_all.sort_values(by=["diff", "counts", "volume", "ratio"], ascending=[0, 0, 0, 1])
# top_all=top_all.sort_values(by=['percent','diff','counts','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 rl.format_for_print(top_all)
# print top_all[:10]
print rl.format_for_print(top_all[:10])
# print "staus",status
if status:
for code in top_all[:10].index:
code = re.findall("(\d+)", code)
if len(code) > 0:
log.debug('dif6 vol>lvol:%s' % len(top_dif))
# top_dif = top_dif[top_dif.buy >= top_dif.open*0.99]
# log.debug('dif5 buy>open:%s'%len(top_dif))
# top_dif = top_dif[top_dif.trade >= top_dif.buy]
# df['volume']= df['volume'].apply(lambda x:x/100)
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]), len(top_dif))),
# print "Rt:%0.3f" % (float(time.time() - time_Rt))
print "Rt:%0.1f dT:%s" % (
float(time.time() - time_Rt),
cct.get_time_to_date(time_s))
if 'counts' in top_dif.columns.values:
top_dif = top_dif.sort_values(
by=['diff', 'percent', 'volume', 'counts', 'ratio'],
ascending=[0, 0, 0, 1, 0])
else:
# print "Good Morning!!!"
top_dif = top_dif.sort_values(
by=['diff', 'percent', 'ratio'], ascending=[0, 0, 1])
# top_all=top_all.sort_values(by=['percent','diff','counts','ratio'],ascending=[0,0,1,1])
print rl.format_for_print(top_dif[:10])
# print top_all.loc['000025',:]
# print "staus",status
if status:
# 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)
top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl, talib=True)
else:
# top_dif = top_dif[top_dif.percent >= 0]
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)
top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl, talib=True)
cct.set_console(width, height,
title=[du_date, 'dT:%s' % cct.get_time_to_date(time_s), 'G:%s' % goldstock,
'zxg: %s' % (blkname)])
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)
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 %0.1f%%" % (float(time.time() - time_Rt), cct.get_time_to_date(time_s), cct.get_now_time(), len(top_temp), round(len(top_temp) / now_count * 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)
# 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])
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,
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) / now_count * 100, 1))
