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 day_t in d_hist.index:
dl = d_hist.drop(day_t).index
else:
dl = d_hist.index
# 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
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
'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(),
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)
else:
top_temp = top_temp.sort_values(
by=eval(market_sort_value),
ascending=market_sort_value_key)
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)
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']][:10]
# top_temp = top_temp[ (top_temp['ma5d'] > top_temp['ma10d']) & (top_temp['buy'] > top_temp['ma10d']) ][:10]
top_dd = pd.concat([top_temp[:10], top_end], axis=0)
# top_dd = top_dd.drop_duplicates()
top_dd = top_dd.loc[:, ct.Duration_format_buy]
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))
if 'op' in top_temp.columns:
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.loc[:,ct.MonitorMarket_format_buy]
# else:
# top_temp = top_temp.loc[:,ct.MonitorMarket_format_buy]
print rl.format_for_print(
top_temp.loc[:, ct.MonitorMarket_format_buy][:10])
# print rl.format_for_print(top_dif[:10])