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)
print "Error2sleep:%s" % (sleeptime)
cct.sleep(sleeptime)
def get_roll_mean_all(single=True,
tdx=False,
app=True,
duration=100,
ma_250_l=1.02,
ma_250_h=1.11):
# df = tdd.search_Tdx_multi_data_duration('tdx_all_df_300', 'all_300', df=None,code_l=code_list, start=start, end=None, freq=None, col=None, index='date')
block_path = tdd.get_tdx_dir_blocknew() + '060.blk'
if not app and cct.get_file_size(
block_path) > 100 and cct.creation_date_duration(block_path) == 0:
print "It's Today Update"
return True
code_list = sina_data.Sina().market('all').index.tolist()
print "all code:", len(code_list)
if duration < 300:
h5_fname = 'tdx_all_df' + '_' + str(300)
h5_table = 'all' + '_' + str(300)
else:
h5_fname = 'tdx_all_df' + '_' + str(900)
h5_table = 'all' + '_' + str(900)
# df = tdd.search_Tdx_multi_data_duration('tdx_all_df_300', 'all_300', df=None,code_l=code_list, start='20150501', end=None, freq=None, col=None, index='date')
df = tdd.search_Tdx_multi_data_duration(h5_fname,
h5_table,
df=None,
code_l=code_list,
start=None,
end=None,
freq=None,
col=None,
index='date')
# df = tdd.search_Tdx_multi_data_duration(h5_fname, h5_table, df=None,code_l=code_list, start=None, end=None, freq=None, col=None, index='date',tail=1)
code_uniquelist = df.index.get_level_values('code').unique()
code_select = code_uniquelist[random.randint(0, len(code_uniquelist) - 1)]
print round(time.time() - time_s, 2), df.index.get_level_values(
'code').unique().shape, code_select, df.loc[code_select].shape
# df.groupby(level=[0]),df.index.get_level_values(0)
# len(df.index.get_level_values('code').unique())
# df = df[~df.index.duplicated(keep='first')]
dfs = df
def get_groupby_mean_median_close(dfs):
groupd = dfs.groupby(level=[0])
df = groupd['close'].agg({'median': 'median', 'mean': 'mean'})
df['close'] = groupd.tail(1).reset_index().set_index(['code'])['close']
# dfs['mean'] = groupd['close'].agg('mean')
# dfs['median'] = groupd['close'].agg('median')
# dfs = dfs.fillna(0)
# idx = pd.IndexSlice
# mask = ( (dfs['mean'] > dfs['median'])
# & (dfs['close'] > dfs['mean'])
# )
# df=dfs.loc[idx[mask, :]]
df = df[(df['mean'] > df['median']) & (df['close'] > df['mean'])]
# dt_low = None
# if dl == 1:
# dfs = groupd.tail(1)
# print("dfs tail1")
# else:
# dl = 30
# dindex = tdd.get_tdx_Exp_day_to_df(
# '999999', dl=dl).sort_index(ascending=False)
# dt = tdd.get_duration_price_date('999999', df=dindex)
# dt = dindex[dindex.index >= dt].index.values
# dt_low = dt[-1]
# dtlen = len(dt) if len(dt) >0 else 1
# dfs = groupd.tail(dtlen)
# print("dfs tail:%s dt:%s"%(dtlen,dt))
# dfs = get_multi_date_duration(dfs,dt[-1])
return df
groupd = dfs.groupby(level=[0])
# rollma = ['5','10','60','100','200']
# rollma = ['5','10','250']
if duration < 300:
rollma = ['10']
else:
rollma = ['10', '250']
rollma.extend([str(duration)])
# import ipdb;ipdb.set_trace()
# df.loc['300130'][:2]
# dfs['mean'] = groupd['close'].agg('mean')
# dfs['median'] = groupd['close'].agg('median')
for da in rollma:
cumdays = int(da)
dfs['ma%d' % cumdays] = groupd['close'].apply(pd.rolling_mean, cumdays)
if cumdays == 10:
dfs['upper'] = dfs['ma%d' % cumdays].apply(lambda x: round(
(1 + 11.0 / 100) * x, 1))
dfs['lower'] = dfs['ma%d' % cumdays].apply(lambda x: round(
(1 - 9.0 / 100) * x, 1))
dfs['ene'] = map(lambda x, y: round((x + y) / 2, 1), dfs['upper'],
dfs['lower'])
# 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)
# dfs['amount%d'%cumdays] = groupd['amount'].apply(pd.rolling_mean, cumdays)
# df.ix[df.index.levels[0]]
#df.ix[df.index[len(df.index)-1][0]] #last row
# dfs = tdd.search_Tdx_multi_data_duration(df=dfs,code_l=code_list, start='20170918', end='20170918', freq=None, col=None, index='date')
# print dfs[:1],len(dfs)
# groupd.agg({'low': 'min'})
# '''idx mask filter'''
# '''
dt_low = None
df_idx = None
if single:
dfs = groupd.tail(1)
print("dfs tail1")
else:
dl = 30
dindex = tdd.get_tdx_Exp_day_to_df('999999',
dl=dl).sort_index(ascending=False)
dt = tdd.get_duration_price_date('999999', df=dindex)
dt = dindex[dindex.index >= dt].index.values
dt_low = dt[-1]
dtlen = len(dt) if len(dt) > 0 else 1
dfs = groupd.tail(dtlen)
# import ipdb;ipdb.set_trace()
df_idx = get_groupby_mean_median_close(dfs)
print("dfs tail:%s dt:%s" % (dtlen, dt))
dfs = get_multi_date_duration(dfs, dt[-1])
# groupd2 = dfs.groupby(level=[0])
# dfs['ma%d'%cumdays] = groupd['close'].apply(pd.rolling_mean, cumdays)
# dfs.reset_index().groupby(['code'])['date'].transform('count')
single = True
dfs = dfs.fillna(0)
idx = pd.IndexSlice
# mask = (dfs[('ma%s')%(rollma[0])] > dfs[('ma%s')%(rollma[1])]) & (dfs[('ma%s')%(rollma[-1])] > 0) & (dfs[('close')] > dfs[('ma%s')%(rollma[0])]) & (dfs[('close')] > dfs[('ma%s')%(rollma[-1])])
# mask = (dfs[('ma%s')%(rollma[0])] > dfs[('ma%s')%(rollma[1])]) & (dfs[('ma%s')%(rollma[-1])] > 0) & (dfs[('close')] > dfs[('ma%s')%(rollma[1])]) & (dfs[('close')] > dfs[('ma%s')%(rollma[-1])])
# mask = (dfs[('ma%s')%(rollma[0])] > dfs[('ma%s')%(rollma[1])]) & (dfs[('ma%s')%(rollma[-1])] > 0) & (dfs[('close')] > dfs[('ma%s')%(rollma[-1])])
# mask = ( (dfs[('ma%s')%(rollma[0])] > 0) & (dfs[('ma%s')%(rollma[-1])] > 0) & (dfs[('close')] > dfs[('ma%s')%(rollma[-1])]) & (dfs[('close')] > dfs[('ma%s')%(rollma[0])]))
# mask = ( (dfs[('ma%s')%(rollma[0])] > 0) & (dfs[('ma%s')%(rollma[-1])] > 0)
# & (dfs[('close')] > dfs[('ma%s')%(rollma[-1])]*ma_250_l)
# & (dfs[('close')] < dfs[('ma%s')%(rollma[-1])]*ma_250_h)
# & (dfs[('close')] > dfs[('ma%s')%(rollma[0])]))
# & (dfs['mean'] > dfs['median'])
# & (dfs['close'] > dfs['mean'])
if len(rollma) > 1:
mask = ((dfs[('ma%s') % (rollma[0])] > 0) & (dfs[('ma%s') %
(rollma[-1])] > 0)
& (dfs[('ma%s') % (rollma[0])] > dfs[('ma%s') % (rollma[-1])])
& (dfs[('close')] > dfs[('ma%s') % (rollma[0])])
& (dfs[('close')] > dfs[('ma%s') % (rollma[-1])] * ma_250_h)
& ((dfs[('close')] > dfs['ene']) |
(dfs[('close')] > dfs['upper'])))
else:
mask = ((dfs[('ma%s') % (rollma[0])] > 0)
& (dfs[('close')] > dfs[('ma%s') % (rollma[0])])
& ((dfs[('close')] > dfs['ene']) |
(dfs[('close')] > dfs['upper'])))
# mask = ((dfs[('close')] > dfs[('ma%s')%(rollma[-1])]))
df = dfs.loc[idx[mask, :]]
df = get_multi_code_count(df)
print(df.couts[:5])
# import ipdb;ipdb.set_trace()
# df.sort_values(by='couts',ascending=0)
# groupd.first()[:2],groupd.last()[:2]
# groupd = df250.groupby(level=[0])
# '''
# groupd.transform(lambda x: x.iloc[-1])
# groupd.last()
# groupd.apply(lambda x: x.close > x.ma250)
# df.shape,df.sort_index(ascending=False)[:5]
# ?groupd.agg
# groupd = df.groupby(level=[0])
# groupd['close'].apply(pd.rolling_mean, 250, min_periods=1)
#ex:# Group df by df.platoon, then apply a rolling mean lambda function to df.casualties
# df.groupby('Platoon')['Casualties'].apply(lambda x:x.rolling(center=False,window=2).mean())
code_uniquelist = df.index.get_level_values('code').unique()
code_select = code_uniquelist[random.randint(0, len(code_uniquelist) - 1)]
if app:
print round(time.time() - time_s, 2), 's', df.index.get_level_values(
'code').unique().shape, code_select, df.loc[code_select][-1:]
if single:
# groupd = df.groupby(level=[0])
if tdx:
# block_path = tdd.get_tdx_dir_blocknew() + '060.blk'
# if cct.get_work_time():
# codew = df[df.date == cct.get_today()].index.tolist()
if dt_low is not None:
groupd2 = df.groupby(level=[0])
df = groupd2.tail(1)
df = df.reset_index().set_index('code')
# import ipdb;ipdb.set_trace()
# df = df[(df.date >= dt_low) & (df.date <= cct.get_today())]
dd = df[(df.date == dt_low)]
df = df[(df.date >= cct.last_tddate(1))]
# import ipdb;ipdb.set_trace()
print("df:%s df_idx:%s" % (len(df), len(df_idx)))
if df_idx is not None and len(df_idx) > 0:
df = df.loc[df_idx.index, :].dropna()
print("Main Down dd :%s MainUP df:%s couts std:%0.1f " %
(len(dd), len(df), df.couts.std()))
# print df.date.mode()[0]
df = df.sort_values(by='couts', ascending=1)
df = df[df.couts > df.couts.std()]
# df = df[(df.date >= df.date.mode()[0]) & (df.date <= cct.get_today())]
codew = df.index.tolist()
if app:
print round(time.time() - time_s, 2), 'groupd2', len(df)
else:
df = df.reset_index().set_index('code')
df = df[(df.date >= cct.last_tddate(days=10))
& (df.date <= cct.get_today())]
codew = df.index.tolist()
top_temp = tdd.get_sina_datadf_cnamedf(codew, df)
top_temp = top_temp[(~top_temp.index.str.contains('688'))
& (~top_temp.name.str.contains('ST'))]
codew = top_temp.index.tolist()
#clean st and 688
if app:
hdf5_wri = cct.cct_raw_input("rewrite code [Y] or append [N]:")
if hdf5_wri == 'y' or hdf5_wri == 'Y':
append_status = False
else:
append_status = True
else:
append_status = False
if len(codew) > 10:
cct.write_to_blocknew(block_path,
codew,
append_status,
doubleFile=False,
keep_last=0)
print "write:%s block_path:%s" % (len(codew), block_path)
else:
print "write error:%s block_path:%s" % (len(codew), block_path)
# df['date'] = df['date'].apply(lambda x:(x.replace('-','')))
# df['date'] = df['date'].astype(int)
# print df.loc[code_select].T,df.shape
MultiIndex = False
else:
MultiIndex = True
h5a.write_hdf_db('all300',
df,
table='roll200',
index=False,
baseCount=500,
append=False,
MultiIndex=MultiIndex)
return df
def get_hot_countNew(changepercent, rzrq, fibl=None, fibc=10):
global fibcount
if fibcount == 0 or fibcount >= fibc:
if fibcount >= fibc:
fibcount = 1
else:
fibcount += 1
if fibl is not None:
int = 0
for f in fibl:
code, op, ra, daysData, fib, st = f[0], f[1], f[2], f[3], f[
4], f[5]
int += 1
if int % 2 != 0:
print "%s op:%s ra:%s d:%s fib:%s m5:%s %s" % (
code, f_print(3, op), f_print(
5, ra), f_print(2, daysData[0]), f_print(
3, fib), f_print(4, daysData[1]), st),
else:
print "%s op:%s ra:%s d:%s fib:%s m5:%s " % (
st, f_print(3, op), f_print(
5, ra), f_print(2, daysData[0]), f_print(
3, fib), f_print(4, daysData[1]))
else:
fibcount += 1
allTop = pd.DataFrame()
indexKeys = ['sh', 'sz', 'cyb']
ffindex = ffu.get_dfcfw_fund_flow('all')
ffall = {}
ffall['zlr'] = 0
ffall['zzb'] = 0
for market in indexKeys:
# market = ct.SINA_Market_KEY()
# df = rd.get_sina_Market_json(market, False)
df = sina_data.Sina().market(market)
# count=len(df.index)
log.info("market:%s" % df[:1])
df = df.dropna()
df = df[df.close > 0]
if 'percent' not in df.columns:
df['percent'] = map(lambda x, y: round((x - y) / y * 100, 1),
df.close.values, df.llastp.values)
if 'percent' in df.columns.values:
# and len(df[:20][df[:20]['percent']>0])>3:
# if 'code' in df.columns:
# top = df[df['percent'] > changepercent]
# topTen = df[df['percent'] > 9.9]
# crashTen = df[df['percent'] < -9.8]
# crash = df[df['percent'] < -changepercent]
# else:
top = df[df['percent'] > changepercent]
topTen = df[df['percent'] > 9.9]
crashTen = df[df['percent'] < -9.8]
crash = df[df['percent'] < -changepercent]
else:
log.info("market No Percent:%s" % df[:1])
top = '0'
topTen = '0'
crashTen = '0'
crash = '0'
# top=df[ df['changepercent'] <6]
print("%s topT: %s top>%s: %s " %
(f_print(4, market), f_print(
3, len(topTen)), changepercent, f_print(4, len(top)))),
# url = ct.DFCFW_FUND_FLOW_URL % ct.SINA_Market_KEY_TO_DFCFW[market]
# log.debug("ffurl:%s" % url)
print(u"crashT:%s crash<-%s:%s" % (f_print(
4, len(crashTen)), changepercent, f_print(4, len(crash)))),
ff = ffindex[market]
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
ffall['zlr'] = ffall['zlr'] + zlr
ffall['zzb'] = ffall['zzb'] + zzb
# zt=str(ff['time'])
# modfprint=lambda x:f_print(4,x) if x>0 else "-%s"%(f_print(4,str(x).replace('-','')))
# print modfprint(zlr)
# print (u"流入: %s亿 比: %s%%" % (modfprint(zlr), modfprint(zzb))),
print(u"流入: %s亿 比: %s%%" % (f_print(4, zlr), f_print(4, zzb))),
print(u"%s %s%s" %
(f_print(4, ff['close']),
f_print(1, '!' if ff['open'] > ff['lastp'] else '?'),
f_print(2, '!!' if ff['close'] > ff['lastp'] else '??')))
allTop = allTop.append(df, ignore_index=True)
allTop = allTop.drop_duplicates()
df = allTop
count = len(df.index)
top = df[df['percent'] > changepercent]
topTen = df[df['percent'] > 9.9]
crashTen = df[df['percent'] < -9.8]
crash = df[df['percent'] < -changepercent]
print(u" \tA:%s topT:%s top>%s:%s" %
(f_print(4, count), f_print(
3, len(topTen)), changepercent, f_print(4, len(top)))),
print(u"crashT:%s crash<-%s:%s" %
(f_print(3, len(crashTen)), changepercent, f_print(4, len(crash)))),
# ff = ffu.get_dfcfw_fund_flow(ct.DFCFW_FUND_FLOW_ALL)
ffall['time'] = ff['time']
ff = ffall
zzb = 0
if len(ff) > 0:
zlr = round(float(ff['zlr']), 1)
zzb = round(float(ff['zzb']) / 3, 1)
zt = str(ff['time'])
print(u"流入: %s亿 占比: %s%% %s" %
(f_print(4, zlr), f_print(4, zzb), f_print(4, zt)))
ff = ffu.get_dfcfw_fund_SHSZ()
hgt = ffu.get_dfcfw_fund_HGT()
szt = ffu.get_dfcfw_fund_HGT(url=ct.DFCFW_FUND_FLOW_SZT)
log.debug("shzs:%s" % ff)
log.debug("hgt:%s" % hgt)
# if len(ff) > 0:
# print ("\tSH: %s u:%s vo: %s sz: %s u:%s vo: %s" % (
# f_print(4, ff['scent']), f_print(4, ff['sup']), f_print(5, ff['svol']), f_print(4, ff['zcent']),
# f_print(4, ff['zup']),
# f_print(5, ff['zvol']))),
if len(ff) > 0:
print(u"\t\tSh: %s Vr:%s Sz: %s Vr:%s " %
(f_print(4, ff['scent']), f_print(5, ff['svol']),
f_print(4, ff['zcent']), f_print(5, ff['zvol'])))
else:
print(u"\t\tSh: \t%s Vr: \t%s Sz: \t%s Vr: \t%s ") % (0, 0, 0, 0)
if len(hgt) > 0:
print("\t\tHgt: %s Ggt: %s Sgt: %s Gst: %s" %
(hgt['hgt'], hgt['ggt'], szt['hgt'], szt['ggt']))
else:
print("\t\tHgt: \t%s Ggt: \t%s Sgt: %s Gst: %s" % (0, 0, 0, 0))
if len(rzrq) > 0:
shpcent = round(
(rzrq['shrz'] / rzrq['sh'] * 100), 1) if rzrq['sh'] > 0 else '?'
szpcent = round(
(rzrq['szrz'] / rzrq['sz'] * 100), 1) if rzrq['sz'] > 0 else '?'
print(u"\tSh: %s rz:%s :%s%% sz: %s rz:%s :%s%% All: %s diff: %s亿" %
(f_print(5, rzrq['sh']), f_print(4, rzrq['shrz']), shpcent,
f_print(5, rzrq['sz']), f_print(4, rzrq['szrz']), szpcent,
f_print(4, rzrq['all']), f_print(5, rzrq['dff'])))
bigcount = rd.getconfigBigCount(count=None, write=True)
# print bigcount
cct.set_console(width,
height,
title=[
'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], bigcount[1]),
'ZL: %s' % (zlr if len(ff) > 0 else 0),
'To:%s' % len(topTen),
'D:%s' % len(crash),
'Sh: %s ' % ff['scent'] if len(ff) > 0 else '?',
'Vr:%s%% ' % ff['svol'] if len(ff) > 0 else '?',
'MR: %s' % zzb,
'ZL: %s' % (zlr if len(ff) > 0 else '?')
])
return allTop
def get_hot_countNew(changepercent, rzrq, fibl=None, fibc=10):
global fibcount
INDEX_LIST_TDX = {'999999': 'sh', '399001': 'sz', '399006': 'cyb'}
# {v: k for k, v in m.items()}
# >>> zip(m.values(), m.keys())
# mi = dict(zip(m.values(), m.keys()))
if fibcount == 0 or fibcount >= fibc:
if fibcount >= fibc:
fibcount = 1
else:
fibcount += 1
if fibl is not None:
int = 0
for f in fibl:
code, op, ra, daysData, fib, st = f[0], f[1], f[2], f[3], f[
4], f[5]
# cumin_index[INDEX_LIST_TDX[code]]=cumin
int += 1
if int % 2 != 0:
print "%s op:%s ra:%s d:%s fib:%s m5:%s %s" % (
code, f_print(3, op), f_print(
5, ra), f_print(2, daysData[0]), f_print(
3, fib), f_print(4, daysData[1]), st),
else:
print "%s op:%s ra:%s d:%s fib:%s m5:%s " % (
st, f_print(3, op), f_print(
5, ra), f_print(2, daysData[0]), f_print(
3, fib), f_print(4, daysData[1]))
else:
fibcount += 1
allTop = pd.DataFrame()
indexKeys = ['sh', 'sz', 'cyb']
# ffindex = ffu.get_dfcfw_fund_flow('all')
ffindex = ffu.get_dfcfw_fund_flow2020('all')
ffall = {}
topTen_all = 0
crashTen_all = 0
ffall['zlr'] = 0
ffall['zzb'] = 0
for market in indexKeys:
# market = ct.SINA_Market_KEY()
# df = rd.get_sina_Market_json(market, False)
df = sina_data.Sina().market(market)
# count=len(df.index)
# log.info("market:%s" % df[:1])
df = df.dropna()
df = df[df.close > 0]
if 'percent' not in df.columns:
df['percent'] = map(lambda x, y: round((x - y) / y * 100, 1),
df.close.values, df.llastp.values)
if 'percent' in df.columns.values:
# and len(df[:20][df[:20]['percent']>0])>3:
# if 'code' in df.columns:
# top = df[df['percent'] > changepercent]
# topTen = df[df['percent'] > 9.9]
# crashTen = df[df['percent'] < -9.8]
# crash = df[df['percent'] < -changepercent]
# else:
top = df[df['percent'] > changepercent]
topTen = df[df['percent'] > 9.9]
# dropcode = [ x for x in topTen.index.tolist() if x not in top_Ten_Dropcxg]
# if len(dropcode) >0:
# topT_l = tdd.get_tdx_exp_all_LastDF_DL(dropcode, dt=ct.duration_date_l,newdays=10,showRunTime=False)
# if isinstance(topT_l, pd.DataFrame):
# top_Ten_Dropcxg.extend(topT_l.index.tolist())
crashTen = df[df['percent'] < -9.8]
crash = df[df['percent'] < -changepercent]
else:
log.info("market No Percent:%s" % df[:1])
top = '0'
topTen = '0'
crashTen = '0'
crash = '0'
topTen_all += len(topTen)
crashTen_all += len(crashTen)
# top=df[ df['changepercent'] <6]
# print("\033[1;31;40m您输入的帐号或密码错误！\033[0m")
print("%s topT: %s top>%s: %s" %
(f_print(4, market), f_print(
3, len(topTen)), changepercent, f_print(4, len(top)))),
# url = ct.DFCFW_FUND_FLOW_URL % ct.SINA_Market_KEY_TO_DFCFW[market]
# log.debug("ffurl:%s" % url)
print(u"crashT:%s crash<-%s:%s" % (f_print(
4, len(crashTen)), changepercent, f_print(4, len(crash)))),
# print(u"-5:%s" %
# (f_print(4, len(crash[crash < -5])))),
ff = ffindex[market]
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
ffall['zlr'] = ffall['zlr'] + zlr
ffall['zzb'] = ffall['zzb'] + zzb
# zt=str(ff['time'])
# modfprint=lambda x:f_print(4,x) if x>0 else "-%s"%(f_print(4,str(x).replace('-','')))
# print modfprint(zlr)
# print (u"流入: %s亿 比: %s%%" % (modfprint(zlr), modfprint(zzb))),
print(u"流入: %s亿 比: %s%% " %
(f_print(6, zlr, 32), f_print(4, zzb, 32))),
if 'close' in ff.keys():
if ff['close'] == 0:
_percent = 0
else:
_percent = round(
(ff['close'] - ff['lastp']) * 100 / ff['close'], 2)
else:
_percent = 0
ff['close'] = 0.0
ff['open'] = 0.0
ff['lastp'] = 0.0
# print (u" %s"%(f_print(2,cumin_index[market],31))),
print(
u"%s %s%% %s%s" %
(f_print(7, ff['close']), f_print(4, _percent, 31),
f_print(1, '!' if ff['open'] > ff['lastp'] else '?'),
f_print(2, '!!' if ff['close'] > ff['lastp'] else '??', 32)))
allTop = allTop.append(df.reset_index(), ignore_index=True)
allTop = allTop.drop_duplicates()
df = allTop
df = tdd.get_single_df_lastp_to_df(df.set_index('code'), resample='d')
count = len(df.index)
top = df[df['percent'] > changepercent]
topTen = df[df['percent'] >= 9.9]
if 'max5' in df.columns:
top_Max = (df[(df.close >= df.hmax) & (df.close >= df.max5)])
# top_low = len(df[df.low < df.min5])
top_min = (df[(df.close <= df.lmin) & (df.close <= df.min5)])
cct.GlobalValues().setkey('top_max', top_Max)
cct.GlobalValues().setkey('top_min', top_min)
else:
top_Max = []
top_low = 0
top_min = []
# topTen = str(len(topTen)) +'('+str(len(top_Ten_Dropcxg))+')' +'(H:'+str(len(top_Max))+')'
topTen = str(topTen_all) + '(' + str(len(topTen)) + ')' + \
'(H:' + str(len(top_Max)) + ')'
# print "top_Ten_Dropcxg:%s",top_Ten_Dropcxg
# crashTen = df[df['percent'] < -9.8]
crashTen = str(crashTen_all) + '(L:' + str(len(top_min)) + ')'
crash = df[df['percent'] < -changepercent]
print(u"AL:%s topT:%s top>%s:%s" %
(f_print(4, count), f_print(
3, (topTen), 31), changepercent, f_print(4, len(top), 31))),
print(
u"crashT:%s crash<-%s:%s" %
(f_print(3,
(crashTen), 32), changepercent, f_print(4, len(crash), 31))),
print(u"-5:%s" % (f_print(4, len(crash[crash.percent < -5]), 32))),
# ff = ffu.get_dfcfw_fund_flow(ct.DFCFW_FUND_FLOW_ALL)
ffall['time'] = ff['time']
ff = ffall
zzb = 0
if len(ff) > 0:
zlr = round(float(ff['zlr']), 1)
zzb = round(float(ff['zzb']) / 3, 1)
zt = str(ff['time'])
print(u"流入: %s亿 占比: %s%% %s" %
(f_print(4, zlr, 31), f_print(4, zzb, 31), f_print(4, zt)))
ff = ffu.get_dfcfw_fund_SHSZ()
# hgt = ffu.get_dfcfw_fund_HGT()
# szt = ffu.get_dfcfw_fund_HGT(url=ct.DFCFW_FUND_FLOW_SZT)
hgt = ffu.get_dfcfw_fund_HGSZ2021('bei')
szt = ffu.get_dfcfw_fund_HGSZ2021('nan')
log.debug("shzs:%s hgt:%s" % (ff, hgt))
# if len(ff) > 0:
# print ("\tSH: %s u:%s vo: %s sz: %s u:%s vo: %s" % (
# f_print(4, ff['scent']), f_print(4, ff['sup']), f_print(5, ff['svol']), f_print(4, ff['zcent']),
# f_print(4, ff['zup']),
# f_print(5, ff['zvol']))),
bigcount = rd.getconfigBigCount(count=None, write=True)
if len(ff) > 0:
print(u"\tSh: %s Vr:%s Sz: %s Vr:%s " %
(f_print(4, ff['scent']), f_print(5, ff['svol'], 31),
f_print(4, ff['zcent']), f_print(5, ff['zvol'], 31))),
print(u'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], f_print(4, bigcount[1])))
else:
print(u"\tSh: \t%s Vr: \t%s Sz: \t%s Vr: \t%s ") % (0, 0, 0, 0),
print(u'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], f_print(4, bigcount[1])))
if len(hgt) > 0:
print("\tSgt: %s Gst: %s Hgt: %s Ggt: %s SSVol:%s" %
(hgt['ggt'], szt['ggt'], hgt['hgt'], szt['hgt'],
f_print(10, ff['allvol'], 31)))
else:
print("\t%s Sgt: %s Gst: %s \tHgt: \t%s Ggt: " % (0, 0, 0, 0))
if len(rzrq) > 0:
if 'shrz' not in rzrq.keys() and 'szrz' not in rzrq.keys():
rzrq['shrz'] = 0
rzrq['szrz'] = 0
shpcent = round(
(rzrq['shrz'] / rzrq['sh'] * 100), 1) if rzrq['sh'] > 0 else '?'
szpcent = round(
(rzrq['szrz'] / rzrq['sz'] * 100), 1) if rzrq['sz'] > 0 else '?'
print(u"\tSh: %s rz:%s :%s%% sz: %s rz:%s :%s%% All: %s diff: %s亿" %
(f_print(5, rzrq['sh']), f_print(4, rzrq['shrz']), shpcent,
f_print(5, rzrq['sz']), f_print(4, rzrq['szrz']), szpcent,
f_print(4, rzrq['all'], 31), f_print(5, rzrq['dff'], 31)))
# print "bigcount:",bigcount
cct.set_console(width,
height,
title=[
'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], bigcount[1]),
'ZL: %s' % (zlr if len(ff) > 0 else 0),
'To:%s' % len(topTen),
'D:%s' % len(crash),
'Sh: %s ' % ff['scent'] if len(ff) > 0 else '?',
'Vr:%s%% ' % ff['svol'] if len(ff) > 0 else '?',
'MR: %s' % zzb,
'ZL: %s' % (zlr if len(ff) > 0 else '?')
],
closeTerminal=True)
log.debug("set_console:bigcount[0]%s bigcount[2]:%s" %
(bigcount[0], bigcount[2]))
return allTop