# # ((float(top_now.loc[symbol, 'buy']) - float(
# # top_all.loc[symbol, 'lastp'])) / float(top_all.loc[symbol, 'lastp']) * 100),
# # 1)
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
top_all = cct.combine_dataFrame(top_all, top_now, col=None)
top_dif = top_all.copy()
log.debug('top_dif:%s' % (len(top_dif)))
if 'trade' in top_dif.columns:
top_dif['buy'] = (map(lambda x, y: y if int(x) == 0 else x,
top_dif['buy'].values,
top_dif['trade'].values))
# 判断主升
# log.debug('top_dif:%s'%(len(top_dif)))
if ct.checkfilter and cct.get_now_time_int(
) > 915 and cct.get_now_time_int() < ct.checkfilter_end_timeDu:
top_dif = top_dif[top_dif.low > top_dif.llow *
ct.changeRatio]
# top_dif = top_dif[top_dif.buy >= top_dif.lhigh * ct.changeRatio]
log.debug('top_dif:%s' % (len(top_dif)))
if cct.get_now_time_int() > 915:
top_dif = top_dif[top_dif.buy > 0]
log.debug('top_dif:%s' % (len(top_dif)))
top_dif['dff'] = (map(lambda x, y: round(
(x - y) / y * 100, 1), top_dif['buy'].values,
top_dif['lastp'].values))
# print top_dif.loc['600610',:]
# top_dif = top_dif[top_dif.trade > 0]
# if cct.get_now_time_int() >< 932:
# print "code:",symbol
count_n = top_now.loc[symbol, 'buy']
count_a = top_all.loc[symbol, 'buy']
# print count_a,count_n
# print count_n,count_a
if count_a == 0 and count_n == 0:
continue
elif count_a == 0 and count_n !=0:
top_all.loc[symbol, 'buy'] = top_now.loc[symbol, 'buy']
count_a = count_n
else:
pass
if cct.get_now_time_int() < 930 and count_a != count_n:
# if not count_n == count_a:
top_all.loc[symbol, 'buy'] = top_now.loc[symbol, 'buy']
else:
if not count_n == count_a:
# log.info("n_buy-a_buy:%s" % (count_n - count_a))
top_now.loc[symbol, 'diff'] = round(
((float(count_n) - float(count_a)) / float(count_a) * 100), 1)
if status_change and 'counts' in top_now.columns.values:
print "change:", time.time() - time_s
# top_now.loc[symbol,'lastp']=top_all.loc[symbol,'lastp']
# top_all.loc[symbol, 'buy':'counts'] = top_now.loc[symbol, 'buy':'counts']
top_all.loc[symbol, 'buy':'prev_p'] = top_now.loc[symbol, 'buy':'prev_p']
else:
# top_now.loc[symbol,'lastp']=top_all.loc[symbol,'lastp']
# if 'couts' in top_now.columns.values:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
# else:
# # 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)
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
top_all = cct.combine_dataFrame(top_all, top_now, col=None)
top_dif = top_all.copy()
if 'trade' in top_dif.columns:
top_dif['buy'] = (map(lambda x, y: y if int(x) == 0 else x,
top_dif['buy'].values,
top_dif['trade'].values))
if ct.checkfilter and cct.get_now_time_int(
) > 915 and cct.get_now_time_int() < ct.checkfilter_end_timeDu:
top_dif = top_dif[top_dif.low > top_dif.llow *
ct.changeRatio]
# if cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 926:
# top_dif['percent']= (map(lambda x, y: round((x-y)/y*100,1) if int(y) > 0 else 0, top_dif.buy, top_dif.llastp))
if cct.get_now_time_int() > 915:
top_dif = top_dif[top_dif.buy > 0]
top_dif['dff'] = (map(lambda x, y: round(
(x - y) / y * 100, 1), top_dif['buy'].values,
top_dif['lastp'].values))
# print top_dif.loc['600610',:]
# top_dif = top_dif[top_dif.trade > 0]
# if cct.get_now_time_int() > 932:
# 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:
code = code[0]
kind = sl.get_multiday_ave_compare_silent(code)
top_all = top_bak
del top_bak
gc.collect()
else:
print "no data"
int_time = cct.get_now_time_int()
if cct.get_work_time():
if int_time < 930:
while 1:
time.sleep(60)
if cct.get_now_time_int() < 931:
time.sleep(60)
print ".",
else:
top_all = pd.DataFrame()
time_s = time.time()
print "."
break
else:
time.sleep(60)
elif cct.get_work_duration():
# print "staus",status
if status:
for code in top_dif[:10].index:
code = re.findall('(\d+)', code)
if len(code) > 0:
code = code[0]
kind = sl.get_multiday_ave_compare_silent(code)
# print top_all[top_all.low.values==0]
# else:
# print "\t No RealTime Data"
else:
print "\tNo Data"
int_time = cct.get_now_time_int()
if cct.get_work_time():
if int_time < 925:
time.sleep(30)
elif int_time < 930:
time.sleep((930 - int_time) * 60)
top_all = pd.DataFrame()
time_s = time.time()
else:
time.sleep(60)
elif cct.get_work_duration():
while 1:
time.sleep(60)
if cct.get_work_duration():
print ".",
time.sleep(60)
sys.exit()
# str.isdigit()是用来判断字符串是否纯粹由数字组成
elif not num_input or not len(num_input) == 6:
print("Please input 6 code:or exit")
num_input = ''
if num_input:
if ave == None:
ave = get_code_search_loop(num_input, code, dayl=days)
else:
ave = get_code_search_loop(num_input,
code,
dayl=days,
ave=ave)
code = num_input
int_time = cct.get_now_time_int()
if cct.get_work_time():
if int_time < 1000:
cct.sleep(60)
else:
cct.sleep(ct.duration_sleep_time)
int_time = cct.get_now_time_int()
if cct.get_work_time():
if int_time < 930:
while 1:
cct.sleep(60)
if cct.get_now_time_int() < 931:
cct.sleep(60)
print ".",
else:
# cct.sleep(random.randint(0, 30))
def all(self):
self.stockcode = StockCode()
self.stock_code_path = self.stockcode.stock_code_path
self.stock_codes = self.stockcode.get_stock_codes()
self.load_stock_codes()
# print "stocks:",len(self.stock_codes)
self.stock_codes = [elem for elem in self.stock_codes if elem.startswith(('6','30','00'))]
h5 = h5a.load_hdf_db(self.hdf_name,self.table, code_l=self.stock_codes)
# print "stocks:",len(self.stock_codes)
if h5 is not None and len(h5) > 0:
o_time = h5[h5.timel <> 0].timel
if len(o_time) > 0:
o_time = o_time[0]
l_time = time.time() - o_time
sina_limit_time = ct.sina_limit_time
sina_time_status = (cct.get_work_day_status() and 915 < cct.get_now_time_int() < 926)
# return_hdf_status = not cct.get_work_day_status() or (cct.get_work_day_status() and (cct.get_work_time() and l_time < sina_limit_time))
return_hdf_status = not cct.get_work_time() or (cct.get_work_time() and l_time < sina_limit_time)
log.info("915:%s sina_time:%0.2f limit:%s"%(sina_time_status,l_time,sina_limit_time))
if sina_time_status and l_time < 8:
log.info("open 915 hdf ok:%s"%(len(h5)))
return h5
elif not sina_time_status and return_hdf_status:
log.info("return hdf5 data:%s"%(len(h5)))
return h5
else:
log.info("no return hdf5:%s"%(len(h5)))
# self.stock_with_exchange_list = list(
# map(lambda stock_code: ('sh%s' if stock_code.startswith(('5', '6', '9')) else 'sz%s') % stock_code,
# self.stock_codes))
self.stock_with_exchange_list = list(
map(lambda stock_code: ('sh%s' if stock_code.startswith(('6')) else 'sz%s') % stock_code,
self.stock_codes))
self.stock_list = []
self.request_num = len(self.stock_with_exchange_list) // self.max_num
for range_start in range(self.request_num):
num_start = self.max_num * range_start
num_end = self.max_num * (range_start + 1)
request_list = ','.join(
self.stock_with_exchange_list[num_start:num_end])
self.stock_list.append(request_list)
# print len(self.stock_with_exchange_list), num_end
if len(self.stock_with_exchange_list) > num_end:
request_list = ','.join(
self.stock_with_exchange_list[num_end:])
self.stock_list.append(request_list)
self.request_num += 1
# a = 0
# for x in range(self.request_num):
# print x
# i = len(self.stock_list[x].split(','))
# print i
# a += i
# print a
log.debug('all:%s' % len(self.stock_list))
# log.error('all:%s req:%s' %
# (len(self.stock_list), len(self.stock_list)))
return self.get_stock_data()
def format_response_data(self,index=False):
stocks_detail = ''.join(self.stock_data)
# print stocks_detail
result = self.grep_stock_detail.finditer(stocks_detail)
# stock_dict = dict()
list_s = []
for stock_match_object in result:
stock = stock_match_object.groups()
# print stock
# print stock
# fn=(lambda x:x)
# list.append(map(fn,stock))
# df = pd.DataFrame(list,columns=ct.SINA_Total_Columns)
# list_s.append({'code'})
list_s.append(
{'code': stock[0],
'name': stock[1],
'open': float(stock[2]),
'close': float(stock[3]),
'now': float(stock[4]),
'high': float(stock[5]),
'low': float(stock[6]),
'buy': float(stock[7]),
'sell': float(stock[8]),
'volume': int(stock[9]),
'turnover': float(stock[10]),
# 'amount': float(stock[10]),
'b1_v': int(stock[11]),
'b1': float(stock[12]),
'b2_v': int(stock[13]),
'b2': float(stock[14]),
'b3_v': int(stock[15]),
'b3': float(stock[16]),
'b4_v': int(stock[17]),
'b4': float(stock[18]),
'b5_v': int(stock[19]),
'b5': float(stock[20]),
'a1_v': int(stock[21]),
'a1': float(stock[22]),
'a2_v': int(stock[23]),
'a2': float(stock[24]),
'a3_v': int(stock[25]),
'a3': float(stock[26]),
'a4_v': int(stock[27]),
'a4': float(stock[28]),
'a5_v': int(stock[29]),
'a5': float(stock[30]),
'dt': (stock[31]),
'ticktime': (stock[32])})
# print list_s
# df = pd.DataFrame.from_dict(stock_dict,columns=ct.SINA_Total_Columns)
df = pd.DataFrame(list_s, columns=ct.SINA_Total_Columns)
# if self.index_status and cct.get_work_time():
# if self.index_status:
# if cct.get_work_time() or (cct.get_now_time_int() > 915) :
# df = df.drop('close', axis=1)
df.rename(columns={'close': 'llastp'}, inplace=True)
if (cct.get_now_time_int() > 915 and cct.get_now_time_int() < 931):
# df.rename(columns={'buy': 'close'}, inplace=True)
df['close']=df['buy']
df['low']=df['buy']
elif (cct.get_now_time_int() > 830 and cct.get_now_time_int() <= 915):
# df.rename(columns={'buy': 'close'}, inplace=True)
df['buy']=df['llastp']
df['close']=df['buy']
df['low']=df['buy']
else:
# df.rename(columns={'now': 'close'}, inplace=True)
df['close'] = df['now']
# df =
df = df.drop_duplicates('code')
# df = df.loc[:, ct.SINA_Total_Columns_Clean]
# df = df.loc[:, ct.SINA_Total_Columns]
# df.rename(columns={'turnover': 'amount'}, inplace=True)
df = df.fillna(0)
# df = df.sort_values(by='code', ascending=0)
df = df.set_index('code')
# print ("Market-df:%s %s time: %s" % (
# cct.get_now_time()))
log.info("hdf:all%s %s"%(len(df),len(self.stock_codes)))
h5a.write_hdf_db(self.hdf_name,df,self.table,index=index)
# self.index_status = False
log.info("wr end:%0.2f"%(time.time() - self.start_t))
return df
# if symbol in top_all.index and top_now.loc[symbol, 'buy'] <> 0:
# if 'couts' in top_now.columns.values:
# top_all.loc[symbol, 'buy':'prev_p'] = top_now.loc[
# symbol, 'buy':'prev_p']
# else:
# top_all.loc[symbol, 'buy':'low'] = top_now.loc[
# symbol, 'buy':'low']
top_all = cct.combine_dataFrame(top_all, top_now, col=None)
top_dif = top_all.copy()
top_dif = top_dif[top_dif.lvol > ct.LvolumeSize]
# if top_dif[:1].llow.values <> 0:
# if not (cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 925):
if len(top_dif[:5][top_dif[:5]['buy'] > 0]) > 3:
log.debug('diff2-0-buy>0')
if cct.get_now_time_int() > 915 and cct.get_now_time_int(
) < ct.checkfilter_end_time:
top_dif = top_dif[top_dif.low > top_dif.llow *
ct.changeRatio]
log.debug('dif4 open>low0.99:%s' % len(top_dif))
# top_dif['buy'] = (map(lambda x, y: y if int(x) == 0 else x, top_dif['buy'].values, top_dif['trade'].values))
# if 'volumn' in top_dif.columns and 'lvol' in top_dif.columns:
top_dif['volume'] = (map(
lambda x, y: round(x / y / radio_t, 1), top_dif['volume'],
top_dif['lvol']))
# if 'lastp' in top_dif.columns and 'buy' in top_dif.columns:
top_dif['dff'] = map(lambda x, y: round(
(x - y) / y * 100, 1), top_dif['buy'].values,
top_dif['lastp'].values)
# # 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)
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
top_all = cct.combine_dataFrame(top_all, top_now, col=None)
top_dif = top_all.copy()
log.debug('top_dif:%s' % (len(top_dif)))
if 'trade' in top_dif.columns:
top_dif['buy'] = (
map(lambda x, y: y if int(x) == 0 else x, top_dif['buy'].values, top_dif['trade'].values))
# 判断主升
# log.debug('top_dif:%s'%(len(top_dif)))
if ct.checkfilter and cct.get_now_time_int() > 915 and cct.get_now_time_int() < ct.checkfilter_end_timeDu:
top_dif = top_dif[top_dif.low > top_dif.llow * ct.changeRatio]
# top_dif = top_dif[top_dif.buy >= top_dif.lhigh * ct.changeRatio]
log.debug('top_dif:%s' % (len(top_dif)))
if cct.get_now_time_int() > 915:
top_dif = top_dif[top_dif.buy > 0]
log.debug('top_dif:%s' % (len(top_dif)))
top_dif['dff'] = (
map(lambda x, y: round((x - y) / y * 100, 1), top_dif['buy'].values, top_dif['lastp'].values))
# print top_dif.loc['600610',:]
# top_dif = top_dif[top_dif.trade > 0]
# if cct.get_now_time_int() > 932:
# top_dif = top_dif[top_dif.low > 0]
top_all["counts"] = 0
top_all["prev_p"] = 0
for symbol in top_now.index:
if "counts" in top_now.columns.values:
top_all.loc[symbol, "buy":"prev_p"] = top_now.loc[symbol, "buy":"prev_p"]
else:
# top_now.loc[symbol, 'diff'] = round(
# ((float(top_now.loc[symbol, 'buy']) - float(
# top_all.loc[symbol, 'lastp'])) / float(top_all.loc[symbol, 'lastp']) * 100),
# 1)
top_all.loc[symbol, "buy":"low"] = top_now.loc[symbol, "buy":"low"]
# top_all.loc[symbol, 'buy'] = top_now.loc[symbol, 'buy']
# top_all = top_all[top_all.buy > 0]
top_dif = top_all
if cct.get_now_time_int() < 935:
top_dif["diff"] = map(
lambda x, y: round((x - y) / y * 100, 1), top_dif["buy"].values, top_dif["lastp"].values
)
top_dif = top_dif[top_dif.buy > 0]
else:
top_dif["diff"] = map(
lambda x, y: round((x - y) / y * 100, 1), top_dif["trade"].values, top_dif["lastp"].values
)
# print top_dif.loc['600610',:]
top_dif = top_dif[top_dif.low > 0]
top_dif = top_dif[top_dif.trade > 0]
# top_dif.loc['600610','volume':'lvol']
top_dif = top_dif[top_dif.lvol > 100000]
top_dif["volume"] = map(
# top_all = top_all[top_all.open>=top_all.low*0.99]
# top_all = top_all[top_all.buy >= top_all.open*0.99]
# top_all = top_all[top_all.trade >= top_all.low*0.99]
# top_all = top_all[top_all.trade >= top_all.high*0.99]
# top_all = top_all[top_all.buy >= top_all.llastp]
# top_all = top_all[top_all.percent >= 0]
# if cct.get_now_time_int() <1500:
top_dif = top_all.copy()
top_dif['dff'] = (map(lambda x, y: round(
(x - y) / y * 100, 1), top_dif['buy'].values,
top_dif['lastp'].values))
log.info('dif1:%s' % len(top_dif))
if not (cct.get_now_time_int() > 915
and cct.get_now_time_int() <= 925):
top_dif = top_dif[top_dif.lvol > ct.LvolumeSize]
if cct.get_now_time_int() > 915 and cct.get_now_time_int(
) < ct.checkfilter_end_time:
top_dif = top_dif[top_dif.low > top_dif.llow *
ct.changeRatio]
# top_dif = top_dif[top_dif.buy >= top_dif.lhigh * ct.changeRatio]
log.debug('dif2:%s' % len(top_dif))
# if cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 926:
# top_dif['percent']= (map(lambda x, y: round((x-y)/y*100,1) if int(y) > 0 else 0, top_dif.buy, top_dif.llastp))
# if top_dif[:1].llow.values <> 0:
if len(top_dif[:5][top_dif[:5]['buy'] > 0]) > 3:
log.debug('diff2-0-low>0')
# log.debug("TDX Col:%s" % tdxdata.columns.values)
# # df_now=top_all.merge(data,on='code',how='left')
# # df_now=pd.merge(top_all,data,left_index=True,right_index=True,how='left')
# top_all = top_all.merge(tdxdata, left_index=True, right_index=True, how='left')
# log.info('Top-merge_now:%s' % (top_all[:1]))
# 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))
if cct.get_now_time_int() > 915:
top_all = top_all[top_all.low > top_all.llow *
ct.changeRatio]
# top_all = top_all[top_all.trade > top_all.lhigh * ct.changeRatio]
# if cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 926:
# top_all['percent'] = (map(lambda x, y: round(
# (x - y) / y * 100, 1) if int(y) > 0 else 0, top_all.trade, top_all.llastp))
# top_all = top_all[top_all.prev_p >= top_all.lhigh]
# top_all = top_all.loc[:,
# ['name', 'percent', 'ma5d','dff', 'couts', 'volume', 'trade', 'prev_p', 'ratio']]
if cct.get_now_time_int() > 1030 and cct.get_now_time_int(
) < 1400:
top_all = top_all[(top_all.volume > ct.VolumeMinR)
& (top_all.volume < ct.VolumeMaxR)]
# for symbol in top_now.index:
# if 'couts' in top_now.columns.values:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
# else:
# # 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)
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
top_all = cct.combine_dataFrame(top_all, top_now, col=None)
top_dif = top_all.copy()
if 'trade' in top_dif.columns:
top_dif['buy'] = (
map(lambda x, y: y if int(x) == 0 else x, top_dif['buy'].values, top_dif['trade'].values))
if ct.checkfilter and cct.get_now_time_int() > 915 and cct.get_now_time_int() < ct.checkfilter_end_timeDu:
top_dif = top_dif[top_dif.low > top_dif.llow * ct.changeRatio]
# top_dif = top_dif[top_dif.buy >= top_dif.lhigh * ct.changeRatio]
# if cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 926:
# top_dif['percent']= (map(lambda x, y: round((x-y)/y*100,1) if int(y) > 0 else 0, top_dif.buy, top_dif.llastp))
if cct.get_now_time_int() > 915:
top_dif = top_dif[top_dif.buy > 0]
top_dif['dff'] = (
map(lambda x, y: round((x - y) / y * 100, 1), top_dif['buy'].values, top_dif['lastp'].values))
# print top_dif.loc['600610',:]
# top_dif = top_dif[top_dif.trade > 0]
# if cct.get_now_time_int() > 932:
from JSONData import tdx_data_Day as tdd
from JSONData import sina_data
from JohhnsonUtil import LoggerFactory as LoggerFactory
from JohhnsonUtil import commonTips as cct
if __name__ == "__main__":
# parsehtml(downloadpage(url_s))
# StreamHandler(sys.stdout).push_application()
log = LoggerFactory.getLogger('Autodata')
# log.setLevel(LoggerFactory.DEBUG)
# handler=StderrHandler(format_string='{record.channel}: {record.message) [{record.extra[cwd]}]')
# log.level = log.debug
# error_handler = SyslogHandler('Sina-M-Log', level='ERROR')
try:
if cct.get_work_day_status() and cct.get_now_time_int() < 1505:
# tdx_data=tdd.get_tdx_all_day_DayL_DF(market='cyb', dayl=1)
print sina_data.Sina().get_stock_code_data('300006')
tdx_data = rl.get_sina_Market_json('cyb')
if len(tdx_data) < 1:
raise KeyboardInterrupt("StopTime")
else:
print tdx_data[-2:]
code = '300366'
except (KeyboardInterrupt) as e:
pass
except (IOError, EOFError, Exception) as e:
print "Error", e
#traceback.print_exc()
sleeptime = random.randint(5, 15)
