def filterPowerCount(df, count=200, down=False, duration=2):
nowint = cct.get_now_time_int()
df.sort_values('percent', ascending=False, inplace=True)
top_temp = df[:count].copy()
if 915 < nowint <= 945:
if nowint <= 935:
top_temp = df[(df.buy > df.llastp) | (df.percent > df['per%sd' %
(duration)])]
else:
top_temp = df[((df.open > df.llastp) & (df.low >= df.llastp)) |
(df.percent > df['per%sd' % (duration)])]
if len(top_temp) > 0:
return top_temp
else:
log.error("915 open>llastp is None")
return df[:count].copy()
if 930 < nowint <= 945:
top_high = df[((df.open > df.llastp) & (df.low >= df.llastp))]
elif 945 < nowint:
if 'nlow' in df.columns:
# top_high = df[ (df.nlow >= df.llastp) | (df.close >= df.nlow)]
top_high = df[((df.nlow >= df.llastp) & (df.nlow >= df.low)) |
((df.nlow <= df.low) & (df.open == df.nlow))]
else:
top_high = df[((df.open > df.llastp) & (df.low > df.llastp)) |
(df.percent > df['per%sd' % (duration)])]
else:
top_high = None
if top_high is not None:
top_high['upper'] = map(lambda x: round((1 + 11.0 / 100) * x, 1),
top_high.ma10d)
top_high['lower'] = map(lambda x: round((1 - 9.0 / 100) * x, 1),
top_high.ma10d)
top_high['ene'] = map(lambda x, y: round((x + y) / 2, 1),
top_high.upper, top_high.lower)
if 930 < nowint < 1500:
radio_t = cct.get_work_time_ratio()
else:
radio_t = 1
top_high = top_high[(((top_high.buy > top_high.ene) &
(top_high.volume / radio_t > 2))) |
(((top_high.lastv1d > top_high.lvol * 1.5)) &
(top_high.lastp1d > top_high.ene))]
if len(top_high) > count:
top_high = top_high[(top_high.low > top_high.ene) |
((top_high.percent > 1) &
(top_high.volume > 2))]
top_temp = cct.combine_dataFrame(top_temp,
top_high,
col=None,
compare=None,
append=True,
clean=True)
return top_temp
def getconfigBigCount(count=None,write=False):
conf_ini = cct.get_work_path('stock','JSONData','count.ini')
fname = 'bigcount_logtime'
logtime = cct.get_config_value_ramfile(fname)
# print os.chdir(os.path.dirname(sys.argv[0]))
# print (os.path.dirname(sys.argv[0]))
# log.setLevel(LoggerFactory.INFO)
if os.path.exists(conf_ini):
log.info("file ok:%s"%conf_ini)
config = ConfigObj(conf_ini,encoding='UTF8')
if int(config['BigCount']['type2']) > 0:
big_last= int(config['BigCount']['type2'])
if logtime != 0:
if (time.time() - float(cct.get_config_value_ramfile(fname)) > float(ct.bigcount_logtime)):
duratime = cct.get_config_value_ramfile(fname,currvalue=time.time(),xtype='time',update=True)
if count is None:
big_now = int(sina_json_Big_Count())
else:
big_now = int(count)
else:
write = False
big_now = big_last
else:
duratime = cct.get_config_value_ramfile(fname,currvalue=time.time(),xtype='time',update=True)
write = False
big_now = big_last
ratio_t=cct.get_work_time_ratio()
bigRt=round( big_now / big_last / ratio_t, 1)
big_v=int(bigRt*int(config['BigCount']['type2']))
# print big_now,big_last,bigRt
int_time=cct.get_now_time_int()
# print int_time
if write and (int_time < 915 or int_time > 1500 ) and big_now > 0 and big_last != big_now :
# print write,not cct.get_work_time(),big_now > 0,big_last != big_now
log.info("big_now update:%s last:%s"%(big_now,big_last))
config['BigCount']['type2'] = big_now
rt=float(config['BigCount']['ratio'])
# if rt != bigRt:
log.info("bigRt:%s"%bigRt)
config['BigCount']['ratio'] = bigRt
config.write()
return [big_now,bigRt,big_v]
else:
log.info("not work:%s ra:%s"%(big_now,bigRt))
return [big_now,bigRt,big_v]
else:
config = ConfigObj(conf_ini,encoding='UTF8')
config['BigCount'] = {}
config['BigCount']['type2'] = sina_json_Big_Count()
config['BigCount']['ratio'] = 0
config.write()
big_v= 0
cl=[config['BigCount']['type2'],config['BigCount']['ratio'],0]
return cl
from JSONData import tdx_data_Day as tdd
from JSONData import sina_data
from JohnsonUtil import LoggerFactory as LoggerFactory
from JohnsonUtil 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)
# top_all = top_all[top_all.open > top_all.lasth1d]
# 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)]
if st_key_sort.split()[0] in ['4','9'] and 915 < cct.get_now_time_int() < 930:
# if 915 < cct.get_now_time_int() < 930:
top_all['dff'] = (list(map(lambda x, y: round((x - y) / y * 100, 1),
top_all['buy'].values, top_all['llastp'].values)))
top_all['dff2'] = (list(map(lambda x, y: round((x - y) / y * 100, 1),
top_all['buy'].values, top_all['lastp'].values)))
elif st_key_sort.split()[0] in ['4','9'] and 926 < cct.get_now_time_int() < 1455 and 'lastbuy' in top_all.columns:
# elif 926 < cct.get_now_time_int() < 1455 and 'lastbuy' in top_all.columns:
top_all['dff'] = (list(map(lambda x, y: round((x - y) / y * 100, 1),
top_all['buy'].values, top_all['lastbuy'].values)))
top_all['dff2'] = (list(map(lambda x, y: round((x - y) / y * 100, 1),
top_all['buy'].values, top_all['lastp'].values)))
# if len(top_all[top_all.lastbuy < 0]) > 0 or len(top_all[top_all.dff < -10]) >0 :
# print top_all.loc['600313'].lastbuy,top_all.loc['600313'].buy,top_all.loc['600313'].lastp
top_all['prev_p'] = 0
# for symbol in top_all.index:
# if 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 cct.get_now_time_int() > 830 and cct.get_now_time_int(
) <= 926:
top_dif = top_all[(top_all.topR > 0)
& (top_all.close > top_all.lastp1d)]
else:
top_dif = top_all[(top_all.topR > 0)
& (top_all.low > top_all.lastp1d)]
top_dif = top_dif[(top_dif.low >= top_dif.ene)
& (top_dif.low >= top_dif.ma5d)]
market_sort_value, market_sort_value_key = ct.get_market_sort_value_key(
st_key_sort, top_all=top_all)
log.debug('top_dif:%s' % (len(top_dif)))
if 'trade' in top_dif.columns:
top_dif['buy'] = (list(
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]))
top_all = top_all[top_all['llow'] > 0]
else:
log.info("start symbol code :%0.2f" %
(cct.get_now_time_int()))
log.info("diff co:%s" %
(set(top_all.columns) - set(top_now.columns)))
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:
# if 'couts' in top_now.columns.values:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
# else:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
# no_index = top_all.drop([inx for inx in top_all.index if inx not in top_now.index], axis=0)
# no_index.drop([col for col in no_index.columns if col in top_now.columns], axis=1,inplace=True)
# no_index = no_index.merge(top_now, left_index=True, right_index=True, how='left')
# 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)]
if st_key_sort.split()[0] in [
'4', '9'
] and 915 < cct.get_now_time_int() < 930:
# if 915 < cct.get_now_time_int() < 930:
top_all['dff'] = (map(
lambda x, y: round((x - y) / y * 100, 1),
top_all['buy'].values, top_all['llastp'].values))
top_all['dff2'] = (map(
lambda x, y: round((x - y) / y * 100, 1),
top_all['buy'].values, top_all['lastp'].values))
elif st_key_sort.split()[0] in [
'4', '9'
] and 926 < cct.get_now_time_int(
) < 1455 and 'lastbuy' in top_all.columns:
# elif 926 < cct.get_now_time_int() < 1455 and 'lastbuy' in top_all.columns:
top_all['dff'] = (map(
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)
df['b1_vv'] = df['b1_v']
if (cct.get_now_time_int() > 915 and cct.get_now_time_int() < 926):
# df.rename(columns={'buy': 'close'}, inplace=True)
df['close'] = df['buy']
df['low'] = df['buy']
df['volume'] = ((df['b1_v'] + df['b2_v'])).map(lambda x: x)
# df['b1_v'] = ((df['b1_v'] + df['b2_v']) / 100 / 10000).map(lambda x: round(x, 1) + 0.01)
# df['b1_v'] = ((df['b1_v']) / 100 / 10000).map(lambda x: round(x, 1) + 0.01)
# df['b1_vv'] = map(lambda x: round(x / 100 / 10000, 1) + 0.01, df['b1_v'])
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']
# df['b1_v'] = ((df['b1_v']) / df['volume'] * 100).map(lambda x: round(x, 1))
else:
# df['b1_v'] = ((df['b1_v']) / df['volume'] * 100).map(lambda x: round(x, 1))
# df.rename(columns={'now': 'close'}, inplace=True)
df['close'] = df['now']
df['nvol'] = df['volume']
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')
if index:
df.index = map(
(lambda x: str(1000000 - int(x)) if x.startswith('0') else x),
df.index)
# print ("Market-df:%s %s time: %s" % (
# cct.get_now_time()))
log.info("hdf:all%s %s" % (len(df), len(self.stock_codes)))
dd = df.copy()
h5_fname = 'sina_MultiIndex_data'
h5_table = 'all' + '_' + str(ct.sina_limit_time)
if cct.get_now_time_int() >= 925 and not index and len(
df) > 3000 and cct.get_work_time():
time_s = time.time()
df.index = df.index.astype(str)
df.ticktime = df.ticktime.astype(str)
# df.ticktime = map(lambda x: int(x.replace(':', '')), df.ticktime)
df.ticktime = map(lambda x, y: str(x) + ' ' + str(y), df.dt,
df.ticktime)
df.ticktime = pd.to_datetime(df.ticktime,
format='%Y-%m-%d %H:%M:%S')
# df = df.loc[:, ['open', 'high', 'low', 'close', 'llastp', 'volume', 'ticktime']]
df = df.loc[:, [
'close', 'high', 'low', 'llastp', 'volume', 'ticktime'
]]
if 'code' not in df.columns:
df = df.reset_index()
if 'dt' in df.columns:
df = df.drop(['dt'], axis=1)
# df.dt = df.dt.astype(str)
if 'name' in df.columns:
# df.name = df.name.astype(str)
df = df.drop(['name'], axis=1)
df = df.set_index(['code', 'ticktime'])
h5a.write_hdf_db(h5_fname,
df,
table=h5_table,
index=False,
baseCount=500,
append=False,
MultiIndex=True)
log.info("hdf5 class all :%s time:%0.2f" %
(len(df), time.time() - time_s))
if 'nlow' not in df.columns or 'nhigh' not in df.columns or (
cct.get_work_time() and 925 < cct.get_now_time_int() < 1500):
# if 'nlow' not in df.columns or 'nhigh' not in df.columns or cct.get_work_time():
h5 = h5a.load_hdf_db(h5_fname, h5_table, timelimit=False)
time_s = time.time()
if cct.get_work_time() and cct.get_now_time_int() <= 1000:
run_col = ['low', 'high', 'close']
startime = '09:25:00'
# endtime = '10:00:00'
endtime = '09:45:00'
dd = self.get_col_agg_df(h5, dd, run_col, all_func, startime,
endtime)
startime = '09:25:00'
# endtime = '10:00:00'
endtime = '10:00:00'
run_col = {'close': 'std'}
dd = self.get_col_agg_df(h5, dd, run_col, run_col, startime,
endtime)
dd.rename(columns={'std': 'nstd'}, inplace=True)
if dd is not None and len(
dd
) > 0 and 'nclose' in dd.columns and 'nstd' in dd.columns:
for co in ['nclose', 'nstd']:
dd[co] = dd[co].apply(lambda x: round(x, 2))
else:
run_col = ['low', 'high']
startime = '09:25:00'
# endtime = '10:00:00'
endtime = '09:40:00'
dd = self.get_col_agg_df(h5, dd, run_col, all_func, startime,
endtime)
startime = '09:25:00'
endtime = '15:01:00'
run_col = ['close']
# h5 = cct.get_limit_multiIndex_Group(h5, freq='15T', col=run_col,start=startime, end=endtime)
dd = self.get_col_agg_df(h5, dd, run_col, all_func, startime,
endtime)
run_col = {'close': 'std'}
dd = self.get_col_agg_df(h5, dd, run_col, run_col, startime,
endtime)
dd.rename(columns={'std': 'nstd'}, inplace=True)
if dd is not None and len(
dd
) > 0 and 'nclose' in dd.columns and 'nstd' in dd.columns:
for co in ['nclose', 'nstd']:
dd[co] = dd[co].apply(lambda x: round(x, 2))
log.info("agg_df_all_time:%0.2f" % (time.time() - time_s))
# top_temp[:1][['high','nhigh','low','nlow','close','nclose','llastp']]
h5a.write_hdf_db(self.hdf_name, dd, self.table, index=index)
log.info("wr end:%0.2f" % (time.time() - self.start_t))
return dd
clean=True)
log.info('agg_df_Row:%.2f h5:%s endtime:%s' %
((time.time() - time_n), len(h5), endtime))
return dd
h5_fname = 'sina_MultiIndex_data'
# h5_fname = 'sina_multi_index'
h5_table = 'all_10'
time_s = time.time()
h5 = h5a.load_hdf_db(h5_fname,
table=h5_table,
code_l=None,
timelimit=False,
dratio_limit=0.12)
print 'h5:', len(h5)
if cct.get_work_time() and cct.get_now_time_int() <= 1000:
run_col = ['low', 'high', 'close']
startime = None
# endtime = '10:00:00'
endtime = '09:45:00'
dd = get_col_agg_df_Test(h5, dd, run_col, all_func, startime, endtime)
else:
run_col = ['low', 'high']
startime = None
# endtime = '10:00:00'
endtime = '09:45:00'
dd = get_col_agg_df_Test(h5, dd, run_col, all_func, startime, endtime)
startime = '09:30:00'
endtime = '15:01:00'
# run_col = ['close']
def getBollFilter(df=None,
boll=ct.bollFilter,
duration=ct.PowerCountdl,
filter=True,
ma5d=True,
dl=14,
percent=True,
resample='d',
ene=False,
upper=False,
down=False,
indexdff=True,
cuminTrend=False,
top10=True):
# drop_cxg = cct.GlobalValues().getkey('dropcxg')
# if len(drop_cxg) >0:
# log.info("stf drop_cxg:%s"%(len(drop_cxg)))
# drop_cxg = list(set(drop_cxg))
# drop_t = [ co for co in drop_cxg if co in df.index]
# if len(drop_t) > 0:
# df = df.drop(drop_t,axis=0)top
# log.error("stf drop_cxg:%s"%(len(drop_t)))
# hvdu max量天数 lvdu min天数 hv max量 lv 量
# fib < 2 (max) fibl > 2 lvdu > 3 (hvdu > ? or volume < 5)? ene
time_s = time.time()
# df['upper'] = map(lambda x: round((1 + 11.0 / 100) * x, 1), df.ma10d)
# df['lower'] = map(lambda x: round((1 - 9.0 / 100) * x, 1), df.ma10d)
# df['ene'] = map(lambda x, y: round((x + y) / 2, 1), df.upper, df.lower)
'''
df = df[(df.close > df.ma20d) | (df.close > df.ene)]
if filter:
if 'boll' in df.columns:
df = df[(df.boll >= boll) | (df.buy > df.upper)]
'''
# if 'nlow' in df.columns:
# df = df[ ((df.nlow >= df.llastp) & (df.nlow >= df.low)) | ((df.nlow <= df.low) & (df.open == df.nlow))]
# if cct.get_work_time() and 'df2' in df.columns:
# if 915 < cct.get_now_time_int() < 930:
# df['cumnow'] = map(lambda x, y: 1 if x - y > 0 else 0, df.close, df.cumaxe)
# else:
# df['cumnow'] = map(lambda x, y: 1 if x - y > 0 else 0, df.high, df.cumaxe)
# df['df2'] = (df['df2'] + df['cumnow']).map(lambda x: int(x))
# df = df[df.df2 > 1]
# else:
# df = df[df.df2 > 0]
radio_t = cct.get_work_time_ratio()
df['lvolr%s' % (resample)] = df['volume']
# df['volume'] = (map(lambda x, y: round(x / y / radio_t, 1), df.nvol.values, df.lvolume.values))
if cct.get_now_time_int() > 1530:
df['volume'] = (map(
lambda x, y, z: round((x / y / radio_t)
if z < 9.9 else (x / y), 1), df.nvol.values,
df.ma5vol.values, df.percent.values))
else:
df['volume'] = (map(
lambda x, y, z: round((x / y / radio_t)
if z < 9.9 else (x / y), 1), df.nvol.values,
df.lvol.values, df.percent.values))
# df['volume'] = (map(lambda x, y,z: round((x / y / radio_t) if z < 9.9 else (x / y) , 1), df.nvol.values, df.ma5vol.values,df.percent.values))
# df['volume'] = (map(lambda x, y: round(x / y / radio_t, 1), df.nvol.values, df.lastv1d.values))
indexfibl = cct.GlobalValues().getkey('indexfibl')
sort_value = cct.GlobalValues().getkey('market_sort_value')
market_key = cct.GlobalValues().getkey('market_key')
market_value = cct.GlobalValues().getkey('market_value')
# if int(market_value) > 1 and 930 < cct.get_now_time_int():
df = compute_perd_value(df, market_value, 'perc')
df = compute_perd_value(df, market_value, 'per')
if sort_value <> 'percent' and (market_key in ['2', '3']
and market_value not in ['1']):
# print("sort_value:%s,market_key:%s ,market_value:%s" %
# (sort_value, market_key, market_value))
if market_key is not None and market_value is not None:
# if market_key == '3':
if market_key == '3':
market_value = int(market_value)
log.info("stf market_key:%s" % (market_key))
idx_k = cct.get_col_in_columns(df, 'perc%sd', market_value)
# filter percd > idx
# df= df[(df[("perc%sd" % (idx_k))] >= idx_k) | (df[("perc%sd" % (idx_k))]< -idx_k)]
# if int(market_value) > 1 and 930 < cct.get_now_time_int():
# df= compute_perd_value(df, market_value, 'perc')
# df= compute_perd_value(df, market_value, 'per')
# elif market_key == '2':
# if int(market_value) > 1 and 915 < cct.get_now_time_int():
# # df['per%d'%(market_value)] = compute_perd_value(df,market_value)
# df= compute_perd_value(df, market_value,'per')
# df= compute_perd_value(df, market_value, 'perc')
# if sort_value <> 'percent' and (market_key in ['2','3'] and market_value not in ['1'] ):
# if indexfibl is not None:
# df = df[df.fibl >= (indexfibl - 1)]
# else:
# indexfibl = 1
# if cuminTrend:
# cuminfibl = cct.GlobalValues().getkey('cuminfibl')
# if cuminfibl is None:
# cuminfibl = cct.get_index_fibl()
# if cuminfibl > 1:
# df = df[df.df2 >= cuminfibl]
# print df.loc['300107'][['close','cumine','llastp','cumaxe','cumaxc']]
# df = df[ (df.cumine == df.cumaxc) | (df.close >= df.cumine) | (df.llastp >=df.cumaxc) | (df.close >= df.cumaxe) ]
# else:
# upper = True
if not down:
if upper:
# upper_count = len( df[ ( ( df.buy > df.upper)| ( df.lastp0d > df.upper) | ( df.lastp1d > df.upper) | ( df.lastp2d > df.upper) | ( df.lastp3d > df.upper))])
# # ene_count = len(df[df.buy > df.upper])
# if upper_count > 30:
# df = df[ ( ( df.buy > df.upper)| ( df.lastp0d > df.upper) | ( df.lastp1d > df.upper) | ( df.lastp2d > df.upper) | ( df.lastp3d > df.upper))]
# else:
# df = df[ ( (( df.buy > df.ene ) ) | ( df.buy > df.upper)| ( df.lasth0d > df.upper) | ( df.lasth1d > df.upper) | ( df.lasth2d > df.upper) | ( df.lasth3d > df.upper))]
df = df[(df.low >= df.upper) | (df.close >= df.upper) |
(df.per1d >= 9.5) | (df.lastp1d == df.hmax) |
((df.lastp1d <> df.hmax) & (df.high > df.hmax))]
else:
if 935 < cct.get_now_time_int() < 1400:
df = df[((df.buy > df.ene) | (df.llastp > df.ene) |
(df.llastp >= df.max5) | (df.high >= df.max5) |
(df.max5 >= df.upper))]
else:
df = df[((df.buy > df.ene) | (df.llastp > df.ene) |
(df.llastp >= df.max5) | (df.high >= df.max5) |
(df.max5 >= df.upper))]
# df['cumins'] = round(cum_counts.index[0], 2)
# df['cumine'] = round(cumdf[-1], 2)
# df['cumax']
# df.rename(columns={'cumin': 'df2'}, inplace=True)
if df is None:
print "dataframe is None"
return None
else:
# top10 = df[ (df.percent >= 9.99) & (df.b1_v > df.a1_v)]
if resample in ['d', 'w']:
# if not top10 or (cct.get_now_time_int() < 950 or cct.get_now_time_int() > 1502):
# df.loc[((df.b1_v > df.a1_v) & (df.percent > 9)), 'percent'] = 10.1
# df.loc[((df.percent >= 9.97) & (df.percent < 10.1)), 'percent'] = 10
# else:
# df.loc[((df.b1_v > df.a1_v) & (df.percent > 9)), 'percent'] = 10.1
# df.loc[((df.percent >= 9.97) & (df.percent < 10.1)), 'percent'] = 10
# df.loc[df.per1d >= 9.99, 'per1d'] = 10
df['percent'] = df['percent'].apply(lambda x: round(x, 2))
# time_ss = time.time()
perc_col = [co for co in df.columns if co.find('perc') == 0]
per_col = [co for co in df.columns if co.find('per') == 0]
# per_col = list(set(per_col) - set(perc_col) - set(['per1d', 'perlastp']))
per_col = list(
set(per_col) - set(perc_col) - set(['per1d', 'perlastp']))
perc_col.remove('percent')
for co in perc_col:
df[co] = df[co].apply(lambda x: round(x, 2))
# da, down_zero, down_dn, percent_l = 1, 0, 0, 2
# da, down_zero, down_dn, percent_l = 1, 0, -1, 1
# df['perc_n'] = map((lambda h, lh, l, ll, c, lc: (1 if (h - lh) > 0 else down_dn) + (1 if c - lc > 0 else down_dn) + (1 if (l - ll) > 0 else down_dn) + (2 if (c - lh) > 0 else down_zero) + (2 if (l - lc) > 0 else down_zero) + (0 if (h - lc) > 0 else down_dn)), df['high'], df['lasth%sd' % da], df['low'], df['lastl%sd' % da], df['close'],df['lastp%sd' % da])
# df['perc_n'] = map((lambda c, lc: (1 if (c - lc) > 0 else down_zero) + (1 if (c - lc) / lc * 100 > 3 else down_zero) +
# (down_dn if (c - lc) / lc * 100 < -3 else down_zero)), df['close'], df['lastp%sd' % da])
# idx_rnd = random.randint(0, len(df) - 10) if len(df) > 10 else 0
# print "idx_rnd",idx_rnd,df.ix[idx_rnd].lastp0d ,df.ix[idx_rnd].close,df.ix[idx_rnd].lastp0d != df.ix[idx_rnd].close
# if not upper:
# if cct.get_work_time() or df.ix[idx_rnd].lastp0d <> df.ix[idx_rnd].close:
# nowd, per1d = 0, 1
# if 'nlow' in df.columns:
# df['perc_n'] = map(cct.func_compute_percd2, df['close'], df['llastp'], df['open'],df['lasto%sd' % nowd], df['lasth%sd' %
# (nowd)], df['lastl%sd' % (nowd)], df['nhigh'], df['nlow'],df['nvol']/radio_t,df['lastv%sd'%(nowd)], df['hmax'],df['df2'])
# else:
# df['perc_n'] = map(cct.func_compute_percd2, df['close'], df['llastp'], df['open'],df['lasto%sd' % nowd], df['lasth%sd' %
# (nowd)], df['lastl%sd' % (nowd)], df['high'], df['low'],df['nvol']/radio_t,df['lastv%sd'%(nowd)], df['hmax'],df['df2'])
# else:
# nowd, per1d = 1, 2
# # print df['per%sd' % da+1], df['lastp%sd' % (da)], df['lasth%sd' % (da)], df['lastl%sd' % (da)], df['high'], df['low']
# df['perc_n'] = map(cct.func_compute_percd2, df['close'], df['llastp'], df['open'],df['lasto%sd' % nowd], df['lasth%sd' %
# (nowd)], df['lastl%sd' % (nowd)], df['high'], df['low'], df['hmax'])
'''
idx_rnd = random.randint(0, len(df) - 10) if len(df) > 10 else 0
if not upper:
# if cct.get_work_time() or df.ix[idx_rnd].lastp1d <> df.ix[idx_rnd].close:
if cct.get_work_time() :
nowd, per1d=1, 1
if 'nlow' in df.columns:
df['perc_n']=map(cct.func_compute_percd2020, df['open'], df['close'], df['nhigh'], df['nlow'], df['lasto%sd' % nowd], df['llastp'],
df['lasth%sd' % (nowd)], df['lastl%sd' % (nowd)], df['ma5d'], df['ma10d'], df['nvol'] / radio_t, df['lastv%sd' % (nowd)])
else:
df['perc_n']=map(cct.func_compute_percd2020, df['open'], df['close'], df['high'], df['low'], df['lasto%sd' % nowd], df['llastp'],
df['lasth%sd' % (nowd)], df['lastl%sd' % (nowd)], df['ma5d'], df['ma10d'], df['nvol'] / radio_t, df['lastv%sd' % (nowd)])
else:
nowd, per1d=1, 1
# print df['per%sd' % da+1], df['lastp%sd' % (da)], df['lasth%sd' % (da)], df['lastl%sd' % (da)], df['high'], df['low']
df['perc_n']=map(cct.func_compute_percd2020, df['open'], df['close'], df['high'], df['low'], df['lasto%sd' % nowd], df['llastp'],
df['lasth%sd' % (nowd)], df['lastl%sd' % (nowd)], df['ma5d'], df['ma10d'], df['nvol'] / radio_t, df['lastv%sd' % (nowd)])
for co in perc_col:
df[co] = (df[co] + df['perc_n']).map(lambda x: int(x))
'''
# for co in per_col:
# df[co] = (df[co] + df['percent']).map(lambda x: int(x))
# print "percT:%.2f"%(time.time()-time_ss)
else:
df['percent'] = (map(
lambda x, y: round((x - y) / y * 100, 1)
if int(y) > 0 else 0, df.buy, df.lastp1d))
# df.loc['002204'].percent
# -15.4
# ipdb> df.loc['002204'].ma10d
# 3.2000000000000002
# ipdb> df.loc['002204'].ma5d
# 3.2999999999999998
if 'fib' not in df.columns:
df['fib'] = 0
# else:
co2int = ['boll', 'op', 'ratio', 'fib', 'fibl', 'df2']
# co2int.extend([co for co in df.columns.tolist()
# if co.startswith('perc') and co.endswith('d')])
co2int.extend(['top10', 'topR'])
co2int = [inx for inx in co2int if inx in df.columns]
for co in co2int:
df[co] = df[co].astype(int)
# if down:
# # if 'nlow' in df.columns:
# # if 'max5' in df.columns:
# # # df = df[(df.hmax >= df.ene) & (df.nlow >= df.low) & (df.low <> df.high) & (((df.low < df.hmax) & (df.high > df.cmean)) | (df.high > df.max5))]
# # df = df[ (((df.buy > df.ene) & (df.volume / radio_t > 1.5)) ) | (((df.lastv1d > df.lvol * 1.5) & (df.lastv1d > df.lvol * 1.5)) & (df.lastp1d > df.ene) )]
# # else:
# # df = df[ (((df.buy > df.ene) & (df.volume / radio_t > 1.5)) ) | (((df.lastv1d > df.lvol * 1.5) ) )]
# # else:
# # # df = df[ (df.max5 >= df.ene) & (df.low <> df.high) & (((df.low < df.hmax) & (df.close > df.cmean)) | (df.high > df.cmean))]
# # df = df[ (((df.buy > df.ene) & (df.volume / radio_t > 1.5)) ) | (((df.lastp1d > df.ene) ) | ((df.buy > df.max5)|(df.buy > df.ene)) | (df.max5 >= df.ene) | (df.high > df.cmean) )]
# return df
if cct.get_work_time() and 'b1_v' in df.columns and 'nvol' in df.columns:
df = df[(df.b1_v > 0) | (df.nvol > 0)]
if (cct.get_now_time_int() > 915 and cct.get_now_time_int() < 926):
df['b1_v'] = df['volume']
else:
dd = df[df.percent < 10]
dd['b1_v'] = dd['volume']
df = cct.combine_dataFrame(df, dd.loc[:, ['b1_v']])
# print "t:%0.2f"%(time.time()-time_ss)
# if int(market_value) < 3 and 930 < cct.get_now_time_int():
# if 'nlow' in df.columns:
# # df = df[((df.per1d < 3) & (df.nlow >= df.llastp)) | ((df.per1d > 5) & (df.buy > df.llastp))]
# df = df[((df.per1d < 3) & (df.nlow >= df.llastp)) | ((df.per1d > 5) & (df.buy > df.llastp))]
# else:
# df = df[((df.per1d < 3) & (df.open > df.llastp)) | ((df.per1d > 5) & (df.buy > df.llastp))]
# else:
# if 'fib' in df.columns:
# df = df[df.fib <= 5]
# log.error("perc%sd"%(market_value))
# elif market_key is not None and market_key == '2':
# if market_value in ['1', '2']:
# df['dff'] = (map(lambda x, y, z: round((x + (y if z > 20 else 3 * y)), 1), df.dff.values, df.volume.values, df.ratio.values))
# df['df2'] = (map(lambda x, y, z: w=round((x - y) / z * 100, 1), df.high.values, df.low.values, df.llastp.values))
# df['df2'] = (map(func_compute_df2, df.close.values, df.llastp.values,df.high.values, df.low.values,df.ratio.values))
# df['df2'] = (map(func_compute_df2, df.close.values, df.llastp.values,df.high.values, df.low.values))
'''
filter_up= 'ma5d'
filter_dn= 'ma10d'
if ma5d and filter_up in df.columns:
if filter_dn in df.columns:
if market_key is not None and market_key == '3':
df= df[(df.buy >= df[filter_up]) & (
df.buy * 1.02 >= df[filter_dn] * ct.changeRatio)]
else:
df= df[(df.buy >= df[filter_up])]
else:
df= df[df.buy >= df[filter_up] * ct.changeRatio]
'''
# df = df[(df.lvolume > df.lvol * 0.9) & (df.lvolume > df.lowvol * 1.1)]
# if 'nlow' in df.columns and 932 < cct.get_now_time_int() < 1030:
if ene:
pass
if 'nclose' in df.columns:
# df = df[ ((df.buy > df.ene) ) | ((df.buy > df.upper) & ( df.nclose > df.cmean))]
df = df[((df.buy > df.llastp)) |
((df['nclose'] > df['ene'] * ct.changeRatio) &
(df['percent'] > -3) & (df.volume > 1.5)) |
((df['nclose'] > df['upper'] * ct.changeRatio) &
(df['buy'] > df['upper'] * ct.changeRatioUp)) |
((df['llastp'] > df['upper']) &
(df['nclose'] > df['upper']))]
else:
df = df[((df.buy > df.llastp)) | ((df.buy > df.ene)) |
((df.buy > df.upper) & (df.close > df.cmean))]
if 'nlow' in df.columns and 930 < cct.get_now_time_int():
# for col in ['nhigh', 'nclose', 'nlow','nstd']:
# df[col] = df[col].apply(lambda x: round(x, 2))
if 'nhigh' in df.columns and 'nclose' in df.columns:
# ncloseRatio = map(lambda x, y: x * ct.changeRatio if x * ct.changeRatio > y else y, df.nclose, df.nlow)
df['ncloseRatio'] = map(lambda x: x * 0.99, df.nclose)
df['nopenRatio'] = map(lambda x: x * 0.99, df.open)
# nhighRatio = map(lambda x, y: x * ct.changeRatio if x * ct.changeRatio > y else y, df.nhigh, df.nclose)
# if cct.get_now_time_int() > ct.nlow_limit_time:
# name open low high close nclose nlow nhigh nstd ticktime
# code
# 601899 紫金矿业 4.25 4.24 4.69 4.69 4.52 4.24 4.42 0.12 15:00:00
# 603917 合力科技 27.95 27.69 31.54 31.54 30.46 27.69 30.00 0.70 15:00:00
# 300713 英可瑞 81.00 81.00 89.82 89.77 87.53 80.90 86.36 1.75 15:02:03
# 000933 神火股份 9.07 9.02 10.00 10.00 9.71 9.02 9.44 0.24 15:02:03
# 600050 中国联通 6.49 6.37 6.51 6.41 6.44 6.44 6.51 0.02 15:00:00
# 002350 北京科锐 10.17 10.17 10.75 10.23 10.31 10.17 10.75 0.07 15:02:03
# 603363 傲农生物 17.49 16.91 18.35 18.35 17.35 17.18 18.00 0.34 15:00:00
# 000868 安凯客车 8.77 8.53 8.82 8.82 8.82 8.53 8.82 0.01 15:02:03
# 002505 大康农业 2.91 2.86 2.99 2.89 2.91 2.90 2.99 0.02 15:02:03
# 601939 建设银行 7.50 7.41 7.61 7.55 7.50 7.43 7.52 0.05 15:00:00
# 600392 盛和资源 16.85 16.81 18.63 18.63 17.84 16.81 17.51 0.69 15:00:00 (11, 41)
# 603676 卫信康 17.44 16.95 17.96 17.82 17.34 17.03 17.77 0.18 15:00:00
if 'nstd' in df.columns:
df['stdv'] = map(lambda x, y: round(x / y * 100, 1),
df.nstd, df.open)
# top_temp[:40][['nstd','stdv','name','volume','dff','percent']]
# top_temp.loc['603363'][['nstd','stdv','name','volume','high','nhigh','percent','open','nclose']]
# df['stdv'] = map(lambda x, y, z: round(z / (x / y * 100), 1), df.nstd, df.open, df.volume)
# df.loc['002906'][['open','nclose','close','ncloseRatio','nopenRatio','low','nlow']]
df = df[(df.low > df.upper) |
(((df.nopenRatio <= df.nclose) & (df.low >= df.nlow) &
(df.close >= df.ncloseRatio)) |
((df.nopenRatio <= df.nclose) &
(df.close >= df.ncloseRatio) &
(df.high >= df.nhigh)))]
# else:
# df = df[(((df.low >= df.nlow) & (df.close >= ncloseRatio)) | ((df.close >= ncloseRatio) & (df.close > nhighRatio)))]
else:
df = df[((df.low >= df.nlow) &
(df.close > df.llastp * ct.changeRatio))]
# if 'nhigh' in df.columns and 'nclose' in df.columns:
# if cct.get_now_time_int() > ct.nlow_limit_time:
# df = df[(df.low >= df.nlow) & ((df.open > df.llastp * ct.changeRatio) & (df.nclose > df.llastp * ct.changeRatio)) &
# (((df.low >= df.nlow) & (df.close >= df.nclose)) | ((df.close >= df.nclose) & (df.close > df.nhigh * ct.changeRatio) & (df.high >= df.nhigh)))]
# else:
# df = df[((df.open > df.llastp * ct.changeRatio) & (df.close > df.llastp * ct.changeRatio)) &
# (((df.low >= df.nlow) & (df.close >= df.nclose)) | ((df.close >= df.nclose) & (df.close > df.nhigh * ct.changeRatio)))]
# else:
# df = df[((df.low >= df.nlow) & (df.close > df.llastp))]
if filter:
if cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 1000:
df = df[((df.buy > df.llastp)) |
(df.buy > df.hmax * ct.changeRatio) |
(df.buy < df.lmin * ct.changeRatioUp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) & (df.low > df.llastp)]
# df = df[(df.buy > df.cmean * ct.changeRatioUp) | (df.open > df.llastp)]
# df = df[df.buy > df.cmean]
elif cct.get_now_time_int() > 1000 and cct.get_now_time_int(
) <= 1430:
df = df[((df.buy > df.llastp)) |
(df.buy > df.hmax * ct.changeRatio) |
(df.buy < df.lmin * ct.changeRatioUp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) | (df.low > df.llastp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) & (df.low > df.llastp)]
# df = df[(df.buy > df.cmean * ct.changeRatioUp) | (df.open > df.llastp)]
# df = df[df.buy > df.cmean]
else:
df = df[((df.buy > df.llastp)) |
(df.buy > df.hmax * ct.changeRatio) |
(df.buy < df.lmin * ct.changeRatioUp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) | (df.low > df.llastp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) & (df.low > df.llastp)]
# df = df[(df.buy > df.cmean * ct.changeRatioUp) | (df.open > df.llastp)]
# df = df[df.buy > df.cmean]
if 'vstd' in df.columns:
# df = df[(df.lvol * df.volume > (df.vstd + df.lvol)) | ((df.percent > -10) & (df.hv / df.lv > 1.5))]
# dd = df.loc['000760'] dd.hv/dd.lv,dd.volume
df['hvRatio'] = map(
lambda x, y: round(x / y / cct.get_work_time_ratio(), 1),
df.hv, df.lv)
df['volRatio'] = (map(lambda x, y: round(x / y / radio_t, 1),
df.nvol.values, df.lv.values))
# df = df[(df.lvol * df.volume > (df.vstd + df.lvol)) | ((df.percent > -5) & (df.hv / df.lv > 3))]
# if 1000 < cct.get_now_time_int() < 1450:
# df = df[ (3 < df.ratio) & (df.ratio < 20)]
if 'nclose' in df.columns:
# df = df[((df.hvRatio > df.volRatio) & ((df.buy > df.lastp) & (df.nclose > df.lastp))) | ((
df = df[(((df.buy > df.lastp) & (df.nclose > df.lastp))) |
(((df.percent > -5) &
((df.vstd + df.lvol) * 1.2 < df.nvol) &
(df.nvol > (df.vstd + df.lvol))))]
else:
# df = df[((df.hvRatio > df.volRatio) & ((df.buy > df.lastp))) | ((
df = df[(((df.buy > df.lastp))) |
(((df.percent > -5) &
((df.vstd + df.lvol) * 1.2 < df.nvol) &
(df.nvol > (df.vstd + df.lvol))))]
# [dd.lvol * dd.volume > (dd.vstd + dd.lvol) | dd.lvol * dd.volume >(dd.ldvolume + dd.vstd]
# print df.loc['000801']
if percent:
pass
'''
if 'stdv' in df.columns and 926 < cct.get_now_time_int():
# df = df[((df.volume > 2 * cct.get_work_time_ratio()) & (df.percent > -3)) | ((df.stdv < 1) &
# (df.percent > 2)) | ((df.lvolume > df.lvol * 0.9) & (df.lvolume > df.lowvol * 1.1))]
df_index = tdd.getSinaIndexdf()
if isinstance(df_index, type(pd.DataFrame())):
df_index['volume'] = (map(lambda x, y: round(x / y / radio_t, 1), df_index.nvol.values, df_index.lastv1d.values))
index_vol = df_index.loc['999999'].volume
if 'percent' in df_index.columns and '999999' in df_index.index:
index_percent = df_index.loc['999999'].percent
else:
# import ipdb;ipdb.set_trace()
log.error("index_percent" is None)
index_percent = 0
index_boll = df_index.loc['999999'].boll
index_op = df_index.loc['999999'].op
# df = df[((df.percent > -3) | (df.volume > index_vol)) & (df.percent > index_percent)]
# df = df[((df.percent > -1) | (df.volume > index_vol * 1.5)) & (df.percent > index_percent) & (df.boll >= index_boll)]
df = df[((df.percent > -1) | (df.volume > index_vol * 1.5)) & (df.percent > index_percent)]
else:
print ("df_index is Series",df_index.T)
else:
df = df[((df.volume > 1.2 * cct.get_work_time_ratio()) & (df.percent > -3))]
'''
# df = df[((df['buy'] >= df['ene'])) | ((df['buy'] < df['ene']) & (df['low'] > df['lower'])) | ((df['buy'] > df['upper']) & (df['low'] > df['upper']))]
# df = df[(( df['ene'] * ct.changeRatio < df['open']) & (df['buy'] > df['ene'] * ct.changeRatioUp)) | ((df['low'] > df['upper']) & (df['close'] > df['ene']))]
# df = df[(( df['ene'] < df['open']) & (df['buy'] < df['ene'] * ct.changeRatioUp))]
# df = df[(df.per1d > 9) | (df.per2d > 4) | (df.per3d > 6)]
# df = df[(df.per1d > 0) | (df.per2d > 4) | (df.per3d > 6)]
# time_ss=time.time()
# codel = df.index.tolist()
# dm = tdd.get_sina_data_df(codel)
# results = cct.to_mp_run_async(getab.Get_BBANDS, codel,'d',5,duration,dm)
# bolldf = pd.DataFrame(results, columns=['code','boll'])
# bolldf = bolldf.set_index('code')
# df = cct.combine_dataFrame(df, bolldf)
# print "bollt:%0.2f"%(time.time()-time_ss),
per3d_l = 2
percent_l = -1
op_l = 3
# if 'boll' in df.columns:
# if 915 < cct.get_now_time_int() < 950:
# # df = df[(df.boll >= boll) | ((df.percent > percent_l) & (df.op > 4)) | ((df.percent > percent_l) & (df.per3d > per3d_l))]
# # df = df[((df.percent > percent_l) & (df.op > 4)) | ((df.percent > percent_l) & (df.per3d > per3d_l))]
# pass
# elif 950 < cct.get_now_time_int() < 1501:
# # df = df[(df.boll >= boll) | ((df.low <> 0) & (df.open == df.low) & (((df.percent > percent_l) & (df.op > op_l)) | ((df.percent > percent_l) & (df.per3d > per3d_l))))]
# # df = df[(df.boll >= boll) & ((df.low <> 0) & (df.open >= df.low *
# # ct.changeRatio) & (((df.percent > percent_l)) | ((df.percent >
# # percent_l) & (df.per3d > per3d_l))))]
# df = df[(df.boll >= boll)]
# # else:
# df = df[(df.boll >= boll) | ((df.low <> 0) & (df.open == df.low) &
# (((df.percent > percent_l) & (df.op > op_l)) | ((df.percent > percent_l)
# & (df.per3d > per3d_l))))]
# if 945 < cct.get_now_time_int() and market_key is None:
# df.loc[df.percent >= 9.94, 'percent'] = -10
# else:
# # df['upper'] = map(lambda x: round((1 + 11.0 / 100) * x, 1), df.ma10d)
# # df['lower'] = map(lambda x: round((1 - 9.0 / 100) * x, 1), df.ma10d)
# # df['ene'] = map(lambda x, y: round((x + y) / 2, 1), df.upper, df.lower)
# # df = df[((df['buy'] >= df['ene'])) | ((df['buy'] < df['ene']) & (df['low'] > df['lower'])) | ((df['buy'] > df['upper']) & (df['low'] > df['upper']))]
# # df = df[(( df['ene'] * ct.changeRatio < df['open']) & (df['buy'] > df['ene'] * ct.changeRatioUp)) | ((df['low'] > df['upper']) & (df['close'] > df['ene']))]
# if 'nclose' in df.columns:
# # df = df[((df['nclose'] > df['ene'] * ct.changeRatio) & (df['percent'] > -3) & (df.volume > 1.5)) |
# # ((df['nclose'] > df['upper'] * ct.changeRatio) & (df['buy'] > df['upper'] * ct.changeRatioUp)) | ((df['llastp'] > df['upper']) & (df['nclose'] > df['upper']))]
# df = df[(df.nclose > df.cmean)]
# else:
# df = df[(df.buy > df.cmean)]
print("bo:%0.1f" % (time.time() - time_s)),
df['ral'] = (map(lambda x, y: round((x + y), 1), df.percent, df.ral))
df['ra'] = (map(lambda x, y: round((x + y), 1), df.percent, df.ra))
return df
def get_dfcfw_fund_flow2020(market):
indexall = ['sh', 'sz', 'cyb']
url = ''
if market == "all":
single = False
url = ct.DFCFW_FUND_FLOW_URL_2020All
else:
log.error("market is not all")
log.info("url:%s" % (url))
data = cct.get_url_data_R(url, timeout=20).split('=')
# vollist=re.findall('{data:(\d+)',code)
# vol_l = []
if len(data) > 0:
# vol_l = re.findall('\"([\d\D]+?)\"', data[1])
# ['jQuery18308448273886036106_1606189025852({"rc":0,"rt":11,"svr":182994506,"lt":1,"full":1,\
# "data":{"total":3,"diff":[{"f62":-9060049408.0,"f184":-4.05},{"f62":-9359993344.0,"f184":-3.16},{"f62":-3487613184.0,"f184":-3.08}]}});']
data_s = eval(re.findall('{[\d\D]*}', data[0])[0])
data_s = data_s['data']['diff']
else:
data_s = ''
# data_s = data[1]
dk = {}
start_inx = 0
for i in range(len(indexall)):
dd = {}
# start_inx = start_inx + i * 24
# end_inx = start_inx + (i+1)*24
if len(data_s) > 0:
data = (data_s[i])
# [{'f184': -4.05, 'f62': -9060049408.0}, \
# {'f184': -3.16, 'f62': -9359993344.0}, {'f184': -3.08, 'f62': -3487613184.0}]
dd['zlr'] = round(float(data['f62']) / 1000 / 1000 / 100, 1)
dd['zzb'] = round(float(data['f184']), 1)
dd['sjlr'] = 0
dd['sjzb'] = 0
dd['time'] = cct.get_now_time_int()
# dd['zlr'] = round(float(data[5+i*25] if data[5+i*25] != '-' else 0)/10000, 1)
# dd['zzb'] = round(float(data[23+i*25].replace("%","")), 1)
# dd['sjlr'] = round(float(data[9+i*25])/10000, 1)
# dd['sjzb'] = round(float(data[10+i*25].replace("%","")), 1)
# dd['time'] = data[24+i*25].split(" ")[1][:5]
# print dd['time']
else:
dd['zlr'] = 0.0
dd['zzb'] = 0.0
dd['sjlr'] = 0.0
dd['sjzb'] = 0.0
dd['time'] = 0.0
log.error("Fund_f NO Url:%s" % url)
if not single:
url = ct.SINA_JSON_API_URL % ct.INDEX_LIST[indexall[i]]
data = cct.get_url_data_R(url, timeout=20, headers=sinaheader)
vol_l = re.findall('\"([\d\D]+?)\"', data)
if len(vol_l) == 1:
data = vol_l[0].split(',')
try:
dd['open'] = round(float(data[1]), 2)
dd['lastp'] = round(float(data[2]), 2)
dd['close'] = round(float(data[3]), 2)
dd['high'] = round(float(data[4]), 2)
dd['low'] = round(float(data[5]), 2)
dd['vol'] = round(float(data[8]) / 100000, 1)
dd['amount'] = round(float(data[9]) / 100000000, 1)
except Exception, e:
print e
return dd
else:
pass
finally:
pass
# 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'] = (list(
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'] = (list(
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]
# 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'] = (list(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')
top_dif = top_dif[top_dif.low >= top_dif.llow * ct.changeRatio]
log.debug('diff2-1:%s' % len(top_dif))
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 930 < int_time < 1000:
# cct.sleep(45)
# else:
# cct.sleep(ct.duration_sleep_time)
int_time = cct.get_now_time_int()
if cct.get_work_time():
log.debug('into get_work_time:%s' % (int_time))
if 915 < int_time < 926:
while 1:
cct.sleeprandom(15)
break
elif 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))
# print "."
if len(codelist) > 0:
# log.info('toTDXlist:%s' % len(codelist))
# top_all = tdd.get_append_lastp_to_df(top_all)
# log.info("df:%s" % top_all[:1])
radio_t = cct.get_work_time_ratio()
log.debug("Second:vol/vol/:%s" % radio_t)
# top_all['volume'] = top_all['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['dff'] = (map(
lambda x, y: round(
((float(x) - float(y)) / float(y) * 100), 1),
top_all['buy'].values, top_all['lastp'].values))
if cct.get_now_time_int() > 915 and cct.get_now_time_int(
) < ct.checkfilter_end_time:
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))
# if cct.get_now_time_int() > 930 and 'lastp' in top_all.columns:
# top_all = top_all[top_all.trade >= top_all.llastp]
# top_all = top_all.loc[:,
# ['name', 'percent', 'ma5d','dff', 'couts', 'volume', 'trade', 'prev_p', 'ratio']]
# top_dif = top_all[(top_all.topR < 2) & (top_all.open > top_all.lastp1d)]
# # top_all[(top_all.low >= top_all.nlow)& (top_all.high > top_all.nhigh)]
# else:
# # top_dif = top_all[(top_all.topR < 2) & ((top_all.low > top_all.lastp1d) | (top_all.low == top_all.open))]
# if 'nlow' in top_all.columns and 'nhigh' in top_all.columns:
# top_dif = top_all[(top_all.low >= top_all.nlow)& (top_all.close >= top_all.nhigh)]
# # top_dif = top_all[(top_all.low >= top_all.nlow)& (top_all.close >= top_all.nhigh) &(top_all.open == top_all.low)]
# else:
# top_dif = top_all[(top_all.topR >0) & (top_all.low >= top_all.lastp1d)]
# market_sort_value, market_sort_value_key = ct.get_market_sort_value_key(st_key_sort, top_all=top_all)
if 'trade' in top_dif.columns:
top_dif['buy'] = (list(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]
if st_key_sort.split()[0] == '4' and 926 < cct.get_now_time_int() < 1455 and 'lastbuy' in top_dif.columns:
top_dif['dff'] = (list(map(lambda x, y: round((x - y) / y * 100, 1),
top_dif['buy'].values, top_dif['lastbuy'].values)))
top_dif['dff2'] = (list(map(lambda x, y: round((x - y) / y * 100, 1),
top_dif['buy'].values, top_dif['lastp'].values)))
else:
# 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()
market_sort_value, market_sort_value_key = ct.get_market_sort_value_key(
st_key_sort, top_all=top_all)
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'] = (list(
map(lambda x, y: round(x / y / radio_t, 1),
top_dif['volume'], top_dif['lvol'])))
# top_dif['dff'] = map(lambda x, y: round((x - y) / y * 100, 1),
# top_dif['buy'].values, top_dif['lastp'].values)
if st_key_sort.split()[0] in [
'4', '9'
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'))
]
time_s = time.time()
h5 = h5a.load_hdf_db(self.hdf_name,
self.table,
code_l=self.stock_codes,
limit_time=self.sina_limit_time)
log.info("h5a stocksTime:%0.2f" % (time.time() - time_s))
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 < 6:
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)))
else:
log.info("no return hdf5:%s" %
(len(h5) if h5 is not None else 'None'))
# 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()
top_all = cct.combine_dataFrame(top_all, top_now, col=None)
# top_all=top_all.sort_values(by=['dff','percent','couts'],ascending=[0,0,1])
# top_all=top_all.sort_values(by=['dff','ratio','percent','couts'],ascending=[0,1,0,1])
# 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()
if cct.get_now_time_int(
) > 925 and 'lastbuy' in top_dif.columns:
top_dif['dff'] = (map(
lambda x, y: round((x - y) / y * 100, 1),
top_dif['buy'].values, top_dif['lastbuy'].values))
else:
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 cct.get_now_time_int() > 915 and cct.get_now_time_int(
) < ct.checkfilter_end_time:
top_dif = top_dif[top_dif.lvol > ct.LvolumeSize]
# log.info(top_dif[:1])
# top_dif = top_dif[top_dif.buy > top_dif.llastp * ct.changeRatio]
top_dif = top_dif[top_dif.low > top_dif.llow *
# # ((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()
market_sort_value, market_sort_value_key = ct.get_market_sort_value_key(st_key_sort, top_all=top_all)
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))
# 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, 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]))
top_all = top_all[top_all['llow'] > 0]
else:
log.info("start symbol code :%0.2f" %
(cct.get_now_time_int()))
log.info("diff co:%s" %
(set(top_all.columns) - set(top_now.columns)))
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:
# if 'couts' in top_now.columns.values:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
# else:
# top_all.loc[symbol, ct.columns_now] = top_now.loc[symbol, ct.columns_now]
# no_index = top_all.drop([inx for inx in top_all.index if inx not in top_now.index], axis=0)
# no_index.drop([col for col in no_index.columns if col in top_now.columns], axis=1,inplace=True)
# no_index = no_index.merge(top_now, left_index=True, right_index=True, how='left')
