def get_days_data(days=1, df=None):
rzrq_status = 1
# data=''
da = 0
i = 0
data2 = ''
while rzrq_status:
for x in range(days, 20):
yestoday = cct.last_tddate(x)
if yestoday in df.index:
data2 = df.loc[yestoday]
# log.info("yestoday:%s data:%s" % (yestoday, data2))
break
# print da
else:
log.error("%s:None" % (yestoday))
rzrq_status = 0
return data2
def get_days_data(days=1):
rzrq_status = 1
# data=''
da = 0
i = 0
while rzrq_status:
for x in range(days, 20):
yestoday = cct.last_tddate(x).replace('-', '/')
data2 = get_tzrq(url, yestoday)
log.info("yestoday:%s data:%s" % (yestoday, data2))
if len(data2) > 0:
i += 1
# if da ==days and days==0:
# i +=1
# if i >= days-1:
break
# elif da > days:
# break
# else: da+=1
# print da
else:
log.info("%s:%s" % (yestoday, data2))
rzrq_status = 0
return data2
# cut_num=10000
success = 0
top_all = pd.DataFrame()
time_s = time.time()
# delay_time = 3600
delay_time = cct.get_delay_time()
First = True
# base_path = tdd.get_tdx_dir()
# block_path = tdd.get_tdx_dir_blocknew() + '067.blk'
# blkname = '067.blk'
blkname = '063.blk'
block_path = tdd.get_tdx_dir_blocknew() + blkname
lastpTDX_DF = pd.DataFrame()
duration_date = ct.duration_date_l
resample = ct.resample_dtype
end_date = cct.last_tddate(days=3)
# all_diffpath = tdd.get_tdx_dir_blocknew() + '062.blk'
# market_sort_value, market_sort_value_key = ct.get_market_sort_value_key(ct.sort_value_key_perd)
# st_key_sort = '2'
# st_key_sort = '7'
st_key_sort = '4'
st = None
# st_key_sort = ct.sort_value_key_perd
while 1:
try:
# top_now = tdd.getSinaAlldf(market='sh', vol=ct.json_countVol, vtype=ct.json_countType)
time_Rt = time.time()
if st is None and st_key_sort in ['2', '3']:
st_key_sort = '%s %s' % (st_key_sort.split()[0],
cct.get_index_fibl())
def get_linear_model_histogramDouble(code,
ptype='low',
dtype='d',
start=None,
end=None,
vtype='f',
filter='n',
df=None,
dl=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = get_kdate_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# ptype='close'
# if ptype == 'close' or ptype==''
# ptype=
if start is not None and filter == 'y':
if code not in ['999999', '399006', '399001']:
index_d, dl = tdd.get_duration_Index_date(dt=start)
log.debug("index_d:%s dl:%s" % (str(index_d), dl))
else:
index_d = cct.day8_to_day10(start)
log.debug("index_d:%s" % (index_d))
start = tdd.get_duration_price_date(code, ptype='low', dt=index_d)
log.debug("start:%s" % (start))
if start is None and df is None and dl is not None:
start = cct.last_tddate(dl)
# print start
df = tdd.get_tdx_append_now_df_api(code, start=start,
end=end).sort_index(ascending=True)
if df is None:
# df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
df = tdd.get_tdx_append_now_df_api(code, start,
end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df,
dtype).sort_index(ascending=True)
if len(df) == 0:
raise Exception("Code:%s error, df is None" % (code))
asset = df[ptype].round(2)
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') and not code.startswith('399'):
# print "code:",code
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:2] in ['30']:
# print "cyb"
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
# if len(asset) < len(df1):
# asset1 = df1.loc[asset.index, ptype]
# else:
# asset1 = df1.loc[asset.index, ptype]
# startv = asset1[:1]
# asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset[:1].index[0] , df1[:1].index[0]
if asset[:1].index[0] > df1[:1].index[0]:
asset1 = df1.loc[asset.index, ptype]
startv = asset1[:1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
if code.startswith('399001'):
code2 = '399006'
elif code.startswith('399006'):
code2 = '399005'
else:
code2 = '399006'
if code2.startswith('3990'):
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
if len(df1) < int(len(df) / 4):
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(
code2, start, end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
if len(asset) < len(df1):
asset1 = df1.loc[asset.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print len(df),len(asset),len(df1),len(asset1)
if end is not None:
# print asset[-1:]
asset = asset[:-1]
dates = asset.index
asset1 = asset1[:-1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
# plt.ion()
fig = plt.figure(figsize=(16, 10))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# fig.autofmt_xdate() #(no fact)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(321)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
# start, end = ax1.get_xlim()
# print start, end, len(asset)
# print ticks, ticks[:-1]
# (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1))
ax1.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, 'k', alpha=0.9)
plt.plot(X, Y_hatlow, 'r', alpha=0.9)
plt.plot(X, Y_hathigh, 'r', alpha=0.9)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title(code + " | " + str(dates[-1])[:11], fontsize=14)
plt.legend([asset.iat[-1]], fontsize=12, loc=4)
plt.grid(True)
# #plot volume
# pad = 0.25
# yl = ax1.get_ylim()
# ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1])
# axx = ax1.twinx()
# axx.set_position(transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
# volume = np.asarray(df.vol)
# pos = df['open']-df['close']<0
# neg = df['open']-df['close']>=0
# idx = np.asarray([x for x in range(len(df))])
# axx.bar(idx[pos],volume[pos],color='red',width=1,align='center')
# axx.bar(idx[neg],volume[neg],color='green',width=1,align='center')
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(322)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
# if code.startswith('999') or code.startswith('399'):
if len(asset) > len(df1):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, 'r', alpha=0.9)
c = c + d
ax3.plot(asset)
## plt.xlabel('Date', fontsize=12)
plt.ylabel('Price-center price', fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ticks = ax3.get_xticks()
ax3.plot(asset1, '-r', linewidth=2)
# show volume bar !!!
# assvol = df.loc[asset.index]['vol']
# assvol = assvol.apply(lambda x: round(x / assvol[:1], 2))
# ax3.plot(assvol, '-g', linewidth=0.5)
ax3.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
if 'name' in df.columns:
plt.legend([df.name.values[-1:][0], df1.name.values[-1:][0]], loc=0)
else:
if code not in ['999999', '399006', '399001']:
indexIdx = False
else:
indexIdx = True
dm = tdd.get_sina_data_df(code, index=indexIdx)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
# plt.legend([code, code2], loc=0)
plt.legend([cname, code2], loc=0)
ax2 = fig.add_subplot(323)
# ax2.plot(asset)
# ticks = ax2.get_xticks()
ax2.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
# plt.plot(X, Y_hat, 'k', alpha=0.9)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y, 'r', alpha=0.9)
c = c + d
# asset=asset.apply(lambda x:round(x/asset[:1],2))
ax2.plot(asset)
# ax2.plot(asset1,'-r', linewidth=2)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.grid(True)
# plt.title(code, fontsize=14)
# plt.legend([code])
if len(df) > 10:
ax6 = fig.add_subplot(324)
h = df.loc[:, ['open', 'close', 'high', 'low']]
highp = h['high'].values
lowp = h['low'].values
openp = h['open'].values
closep = h['close'].values
# print len(closep)
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=1,
normalize=False)
xt = np.atleast_2d(np.linspace(0,
len(closep) + 200,
len(closep) + 200)).T
yt = lr.predict(xt)
bV = []
bP = []
for i in range(1, len(highp) - 1):
if highp[i] <= highp[i - 1] and highp[i] < highp[
i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i +
1]:
bV.append(lowp[i])
bP.append(i)
else:
bV.append(lowp[i - 1])
bP.append(i - 1)
if len(bV) > 0:
d, p = LIS(bV)
idx = []
for i in range(len(p)):
idx.append(bP[p[i]])
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
ax6.plot(closep, linewidth=2)
ax6.plot(idx, d, 'ko')
ax6.plot(xt, estV, '-r', linewidth=3)
ax6.plot(xt, yt, '-g', linewidth=3)
plt.grid(True)
# plt.tight_layout()
zp2 = zoompan.ZoomPan()
figZoom = zp2.zoom_factory(ax6, base_scale=scale)
figPan = zp2.pan_factory(ax6)
# 统计出每个区域内各股价的频数，得到直方图，为了更精细的显示各个区域的频数，这里将整个边界区间分成100份。
ax4 = fig.add_subplot(325)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat),
(asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
# now_distanse=int((asset.iat[-1]-Y_hat[-1])/Y_hat[-1]*100)
# log.debug("dis:%s now:%s"%(distance[:2],now_distanse))
# log.debug("now_distanse:%s"%now_distanse)
distance = (asset.values.T - Y_hat)
now_distanse = asset.iat[-1] - Y_hat[-1]
# distance = (asset.values.T-Y_hat)[0]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel(
'Undervalue ------------------------------------------> Overvalue',
fontsize=12)
plt.ylabel('Frequency', fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1], str(dates[-1])[5:11]], fontsize=12)
plt.grid(True)
# plt.show()
# import os
# print(os.path.abspath(os.path.curdir))
ax5 = fig.add_subplot(326)
# fig.figsize=(5, 10)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat),
(asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
distance = (asset.values.T - Y_hat) / Y_hat * 100
now_distanse = ((asset.iat[-1] - Y_hat[-1]) / Y_hat[-1] * 100)
log.debug("dis:%s now:%s" % (distance[:2], now_distanse))
log.debug("now_distanse:%s" % now_distanse)
# n, bins = np.histogram(distance, 50)
# print n, bins[:2]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel(
'Undervalue ------------------------------------------> Overvalue',
fontsize=14)
plt.ylabel('Frequency', fontsize=12)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]], fontsize=12)
plt.grid(True)
# plt.ion()
plt.draw()
plt.pause(0.001)
# plt.show(block=False)
# plt.draw()
# plt.pause(0.001)
# plt.close()
# print plt.get_backend()
# plt.show(block=True)
return df
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
