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
# top_now = tdd.getSinaAlldf(market='cx', vol=ct.json_countVol, vtype=ct.json_countType)
# top_now = tdd.getSinaAlldf(market='次新股',filename='cxg', vol=ct.json_countVol, vtype=ct.json_countType)
if st is None:
st_key_sort = '%s %s'%(st_key_sort.split()[0],cct.get_index_fibl())
time_Rt = time.time()
market_blk = '雄安特区'
top_now = tdd.getSinaAlldf(market=market_blk, filename='xatq', vol=ct.json_countVol, vtype=ct.json_countType)
# top_now = tdd.getSinaAlldf(market=u'次新股',filename='cxg', vol=ct.json_countVol, vtype=ct.json_countType)
# top_now = tdd.getSinaAlldf(market='���', filename='mnbk',vol=ct.json_countVol, vtype=ct.json_countType)
# top_dif = top_now
# top_now.to_hdf("testhdf5", 'marketDD', format='table', complevel=9)
now_count = len(top_now)
radio_t = cct.get_work_time_ratio()
# top_now = top_now[top_now.buy > 0]
time_d = time.time()
if time_d - time_s > delay_time:
status_change = True
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()
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_SHSZ(url=ct.DFCFW_ZS_SHSZ):
# sina = Sina()
dd = Sina().get_stock_code_data('999999,399001', index=True)
# sh = dd[dd.index == '000001']
if 'amount' not in dd.columns:
if 'turnover' in dd.columns:
dd.rename(columns={'turnover': 'amount'}, inplace=True)
sh = dd[dd.index == '999999']
if len(sh) > 0 and not sh.name.values[0] == '上证指数':
log.error('sh data is error')
sz = dd[dd.index == '399001']
if len(sh) == 0 or len(sz) == 0:
data = cct.get_url_data_R(url, timeout=15)
log.info("url:%s" % (url))
# vollist=re.findall('{data:(\d+)',code)
vol_l = re.findall('\"([\d\D]+?)\"', data)
dd = {}
# print vol_l
# print len(vol_l)
if len(vol_l) == 2:
# for x in range(len(vol_l):
data = vol_l[0].split(',')
data2 = vol_l[1].split(',')
# print data[3],data2[3],len(data[3]),len(data2[3])
if len(data[3]) > 2 and len(data2[3]) > 2:
dd['svol'] = round(float(data[3]) / 100000000, 1)
dd['zvol'] = round(float(data2[3]) / 100000000, 1)
else:
dd['svol'] = data[3]
dd['zvol'] = data2[3]
# print data[3],data2[3]
dd['scent'] = data[5]
dd['sup'] = data[6].split('|')[0]
dd['zcent'] = data2[5]
dd['zup'] = data2[7].split('|')[0]
df = get_zs_VolRatio()
if len(df['amount']) > 0:
radio_t = cct.get_work_time_ratio()
# print df.loc['999999','amount']
# print type(dd['svol'])
log.debug("type:%s radio_t:%s" % (type(dd['svol']), radio_t))
if isinstance(dd['svol'], str) and dd['svol'].find('-') == 0:
log.info("svol:%s" % dd['svol'])
else:
svol_r = round(
dd['svol'] / (df.loc['999999', 'amount'] / 10000000) /
radio_t, 1)
svol_v = round(
svol_r * (df.loc['999999', 'amount'] / 10000000), 1)
zvol_r = round(
dd['zvol'] / (df.loc['399001', 'amount'] / 10000000) /
radio_t, 1)
zvol_v = round(
svol_r * (df.loc['399001', 'amount'] / 10000000), 1)
dd['allvol'] = "%s-%s-%s" % (dd['svol'] + dd['zvol'],
svol_v + zvol_v,
round(
(svol_r + zvol_r) / 2, 1))
dd['svol'] = "%s-%s-%s" % ((dd['svol'], svol_v, svol_r))
dd['zvol'] = "%s-%s-%s" % ((dd['zvol'], zvol_v, zvol_r))
# dd['zzb']=data[1]
# dd['sjlr']=data[2]
# dd['sjzb']=data[3]
# dd['time']=vol_l[1]
else:
log.info("Fund_f NO Url:%s" % url)
else:
dd = {}
# print vol_l
# print len(vol_l)
#var C1Cache={quotation:["0000011,上证指数,3113.18,121762623488,4.41,0.14%,463|197|656|143,536|280|1187|217","3990012,
#深证成指,9816.71,145863270400,-10.08,-0.10%,463|197|656|143,536|280|1187|217"]}
if len(sh) == 1 and len(sz) == 1:
dd['svol'] = round(float(sh.amount) / 100000000, 1)
dd['zvol'] = round(float(sz.amount) / 100000000, 1)
# print data[3],data2[3]
dd['scent'] = str(
round((sh.close[0] - sh.llastp[0]) / sh.llastp[0] * 100,
2)) + '%'
dd['sup'] = round((sh.close[0] - sh.llastp[0]), 2)
dd['zcent'] = str(
round((sz.close[0] - sz.llastp[0]) / sz.llastp[0] * 100,
2)) + '%'
dd['zup'] = round((sz.close[0] - sz.llastp[0]), 2)
df = get_zs_VolRatio()
if len(df['amount']) > 0:
radio_t = cct.get_work_time_ratio()
# print type(dd['svol'])
svol_r = round(
dd['svol'] / (df.loc['999999', 'amount'] / 100000000) /
radio_t, 1)
svol_v = round(
svol_r * (df.loc['999999', 'amount'] / 100000000), 1)
zvol_r = round(
dd['zvol'] / (df.loc['399001', 'amount'] / 100000000) /
radio_t, 1)
zvol_v = round(
svol_r * (df.loc['399001', 'amount'] / 100000000), 1)
dd['allvol'] = "%s-%s-%s" % (dd['svol'] + dd['zvol'],
svol_v + zvol_v,
round((svol_r + zvol_r) / 2, 1))
dd['svol'] = "%s-%s-%s" % ((dd['svol'], svol_v, svol_r))
dd['zvol'] = "%s-%s-%s" % ((dd['zvol'], zvol_v, zvol_r))
return dd
