status_change = True
time_s = time.time()
top_all = pd.DataFrame()
else:
status_change = False
if len(top_now) > 10 or cct.get_work_time():
# time_Rt = time.time()
if len(top_all) == 0 and len(lastpTDX_DF) == 0:
cct.get_terminal_Position(position=sys.argv[0])
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
top_now,
lastpTDX_DF=None,
dl=duration_date,
end=end_date,
ptype=ptype,
filter=filter,
power=ct.lastPower,
lastp=False,
resample=resample)
log.debug("len:%s" % (len(top_all)))
top_list = tdd.compute_jump_du_count(top_all,
resample=resample)
elif len(top_all) == 0 and len(lastpTDX_DF) > 0:
top_all = top_now
top_all = top_all.merge(lastpTDX_DF,
left_index=True,
right_index=True,
how='left')
log.info('Top-merge_now:%s' % (top_all[:1]))
log.info("top_now.buy[:30]>0:%s" %
len(top_now[:30][top_now[:30]['buy'] > 0]))
if len(top_now) > 10 or cct.get_work_time():
# if len(top_now) > 10 or len(top_now[:10][top_now[:10]['buy'] > 0]) > 3:
# if len(top_now) > 10 and not top_now[:1].buy.values == 0:
# top_now=top_now[top_now['percent']>=0]
if 'trade' in top_now.columns:
top_now['buy'] = (map(lambda x, y: y if int(x) == 0 else x,
top_now['buy'].values,
top_now['trade'].values))
# time_Rt = time.time()
if len(top_all) == 0 and len(lastpTDX_DF) == 0:
cct.get_terminal_Position(position=sys.argv[0])
time_Rt = time.time()
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(top_now)
elif len(top_all) == 0 and len(lastpTDX_DF) > 0:
# time_Rt = time.time()
top_all = tdd.get_append_lastp_to_df(top_now, lastpTDX_DF)
else:
if 'couts' in top_now.columns.values:
if not 'couts' in top_all.columns.values:
top_all['couts'] = 0
top_all['prev_p'] = 0
# for symbol in top_now.index:
# # code = rl._symbol_to_code(symbol)
# if symbol in top_all.index and top_now.loc[symbol, 'buy'] <> 0:
# # top_now.loc[symbol, 'dff'] = round(((float(top_now.loc[symbol, 'buy']) - float(top_all.loc[symbol, 'lastp'])) / float(top_all.loc[symbol, 'lastp']) * 100), 1)
# if 'couts' in top_now.columns.values:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
len(top_now[:30][top_now[:30]['buy'] > 0]))
if len(top_now) > 10 or cct.get_work_time():
# if len(top_now) > 10 or len(top_now[:10][top_now[:10]['buy'] > 0]) > 3:
# if len(top_now) > 10 and not top_now[:1].buy.values == 0:
# top_now=top_now[top_now['percent']>=0]
if 'trade' in top_now.columns:
top_now['buy'] = (map(lambda x, y: y if int(x) == 0 else x,
top_now['buy'].values,
top_now['trade'].values))
if len(top_all) == 0 and len(lastpTDX_DF) == 0:
terminal_count = cct.get_terminal_Position(
position=sys.argv[0])
print "term:%s" % (terminal_count),
if terminal_count > 1:
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
top_now, lastpTDX_DF=None, dl=duration_date)
else:
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
top_now,
lastpTDX_DF=None,
dl=duration_date,
checknew=True)
# time_Rt = time.time()
# top_all,lastpTDX_DF = tdd.get_append_lastp_to_df(top_now,end=end_date,dl=duration_date)
elif len(top_all) == 0 and len(lastpTDX_DF) > 0:
# time_Rt = time.time()
top_all = tdd.get_append_lastp_to_df(top_now, lastpTDX_DF)
else:
if 'couts' in top_now.columns.values:
if not 'couts' in top_all.columns.values:
time_s = time.time()
top_all = pd.DataFrame()
else:
status_change = False
# print ("Buy>0:%s" % len(top_now[top_now['buy'] > 0])),
if len(top_now) > 10 or cct.get_work_time():
time_Rt = time.time()
if len(top_all) == 0 and len(lastpTDX_DF) == 0:
cct.get_terminal_Position(position=sys.argv[0])
time_Rt = time.time()
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
top_now,
lastpTDX_DF=None,
dl=duration_date,
end=end_date,
ptype=ptype,
filter=filter,
power=False,
lastp=False,
newdays=newdays)
elif len(top_all) == 0 and len(lastpTDX_DF) > 0:
time_Rt = time.time()
top_all = top_now
top_all = top_all.merge(lastpTDX_DF,
left_index=True,
right_index=True,
how='left')
top_all = top_all[top_all['llow'] > 0]
else:
else:
status_change = False
# print ("Buy>0:%s" % len(top_now[top_now['buy'] > 0])),
if len(top_now) > 0 or cct.get_work_time():
# time_Rt = time.time()
if len(top_all) == 0 and len(lastpTDX_DF) == 0:
cct.get_terminal_Position(position=sys.argv[0])
# time_Rt = time.time()
print "term:%s" % (cct.get_terminal_Position(cmd='DurationDn.py')),
# if cct.get_terminal_Position(cmd='DurationDn.py') > 1:
# top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
# top_now, lastpTDX_DF=None, dl=duration_date, end=end_date, ptype=ptype, filter=filter, power=ct.lastPower, lastp=lastp, newdays=newdays, resample=resample)
# else:
newdays = 0
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
top_now, lastpTDX_DF=None, dl=duration_date, end=end_date, ptype=ptype, filter=filter, power=ct.lastPower, lastp=lastp, newdays=newdays, checknew=True, resample=resample)
# codelist = top_all.index.tolist()
# log.info('toTDXlist:%s' % len(codelist))
# # tdxdata = tdd.get_tdx_all_day_LastDF(codelist,dt=duration_date,ptype=ptype)
# # print "duration_date:%s ptype=%s filter:%s"%(duration_date, ptype,filter)
# # tdxdata = tdd.get_tdx_exp_all_LastDF(codelist, dt=duration_date, end=end_date, ptype=ptype,filter=filter)
# tdxdata = tdd.get_tdx_exp_all_LastDF_DL(codelist, dt=duration_date, end=end_date, ptype=ptype,filter=filter,power=power)
# log.debug("TdxLastP: %s %s" % (len(tdxdata), tdxdata.columns.values))
# tdxdata.rename(columns={'low': 'llow'}, inplace=True)
# tdxdata.rename(columns={'high': 'lhigh'}, inplace=True)
# tdxdata.rename(columns={'close': 'lastp'}, inplace=True)
# tdxdata.rename(columns={'vol': 'lvol'}, inplace=True)
# if power:
# tdxdata = tdxdata.loc[:, ['llow', 'lhigh', 'lastp', 'lvol', 'date','ra','op','fib','ldate']]
# # print len(tdxdata[tdxdata.op >12]),
if time_d - time_s > delay_time:
status_change = True
time_s = time.time()
top_all = pd.DataFrame()
else:
status_change = False
if len(top_now) > 10 and len(top_now.columns) > 4:
# top_now = top_now[top_now.trade >= top_now.high * 0.98]
# if 'percent' in top_now.columns.values:
# top_now = top_now[top_now['percent'] >= 0]
if len(top_all) == 0 and len(lastpTDX_DF) == 0:
cct.get_terminal_Position(position=sys.argv[0])
time_Rt = time.time()
top_all, lastpTDX_DF = tdd.get_append_lastp_to_df(
top_now, resample=resample)
elif len(top_all) == 0 and len(lastpTDX_DF) > 0:
time_Rt = time.time()
top_all = tdd.get_append_lastp_to_df(top_now, lastpTDX_DF)
# dd=dd.fillna(0)
else:
# for symbol in top_now.index:
# if symbol in top_all.index:
# count_n = top_now.loc[symbol, 'couts']
# count_a = top_all.loc[symbol, 'couts']
# top_now.loc[symbol, 'dff'] = count_n - count_a
# if status_change:
# # top_all.loc[symbol] = top_now.loc[symbol]
# top_all.loc[symbol, ['name', 'percent', 'dff', 'couts', 'trade', 'high', 'open', 'low', 'ratio', 'volume',
# 'prev_p']] = top_now.loc[symbol, ['name', 'percent', 'dff', 'couts', 'trade', 'high', 'open', 'low', 'ratio', 'volume',
# 'prev_p']]
