def get_duration_filter(code, df=None, dtype='d', start=None, end=None, dl=None, filter=True, ptype='low', power=True):
if start is not None and end is None and filter:
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
log.debug("start is not None start: %s index_d:%s" % (start, index_d))
elif end is not None and filter:
df = tdd.get_tdx_append_now_df_api(code, start=start, end=end, df=df, dl=dl, power=power).sort_index(ascending=True)
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
df = df[df.index >= start]
if len(df) > 2 and dl is None:
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if dl is not None:
if power:
start, index_d, df = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=True)
else:
start, index_d = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=False)
log.debug("dl not None code:%s start: %s index_d:%s" % (code, start, index_d))
if len(df) > 0 and df is not None:
df = df.sort_index(ascending=True)
df = df[df.index >= start]
if len(df) == 0 or df is None:
if start is not None and len(start) > 8 and int(start[:4]) > 2500:
log.warn("code:%s ERROR:%s" % (code, start))
start = '2016-01-01'
df = tdd.get_tdx_append_now_df_api(
code, start, end).sort_index(ascending=True)
if start is None:
start = df.index.values[0]
if len(df) > 2 and dl is None and start is not None and filter:
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(
df, dtype).sort_index(ascending=True)
return df
def get_linear_model_histogramDouble(code, ptype='f', dtype='d', start=None, end=None, vtype='close', filter='n',
df=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = ts.get_hist_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# vtype='close'
# if vtype == 'close' or vtype==''
# 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 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, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
asset = df[vtype]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') or not code.startswith('399'):
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:1] in ['3']:
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, ptype, 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)
asset1 = df1.loc[asset.index, vtype]
startv = asset1[:1]
# asset_v=asset[:1]
# print startv,asset_v
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset1[:4]
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
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[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)
# 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)
ax2 = fig.add_subplot(323)
# ax2.plot(asset)
# ticks = ax2.get_xticks()
ax2.set_xticklabels([dates[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])
# 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'):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels([dates[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)
ax3.plot(asset1, '-r', linewidth=2)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 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)
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
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)
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)
# plt.ion()
plt.show(block=False)
ptype = "high"
elif p_t == "y":
filter = "y"
elif p_t == "n":
filter = "n"
else:
print ("arg error:%s" % p_t)
elif len(dl) == 4:
dt = dl[1]
ptype = dl[2]
filter = dl[3]
else:
dt = ""
if len(str(dt)) > 0:
if len(str(dt)) < 8:
duration_date = tdd.get_duration_price_date("999999", dl=dt, ptype=ptype)
else:
duration_date = dt
top_all = pd.DataFrame()
time_s = time.time()
status = False
lastpTDX_DF = ""
elif st == "w" or st == "a":
codew = (top_dd.index).tolist()
if st == "a":
cct.write_to_blocknew(block_path, codew)
# sl.write_to_blocknew(all_diffpath, codew)
else:
cct.write_to_blocknew(block_path, codew, False)
# sl.write_to_blocknew(all_diffpath, codew, False)
print "wri ok:%s" % block_path
def get_linear_model_status(code, df=None, dtype='d', type='m', start=None, end=None, days=1, filter='n',
dl=None, countall=True, ptype='low', power=True):
"""[pct test get_linear_model_status]
[description]
Arguments:
code {[type]} -- [description]
Keyword Arguments:
df {[type]} -- [description] (default: {None})
dtype {str} -- [description] (default: {'d'})
type {str} -- [description] (default: {'m'})
start {[type]} -- [description] (default: {None})
end {[type]} -- [description] (default: {None})
days {number} -- [description] (default: {1})
filter {str} -- [description] (default: {'n'})
dl {[type]} -- [description] (default: {None})
countall {bool} -- [description] (default: {True})
ptype {str} -- [description] (default: {'low'})
power {bool} -- [description] (default: {True})
Returns:
[type] -- [description]
"""
if start is not None and end is None and filter == 'y':
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
log.debug("start is not None start: %s index_d:%s" % (start, index_d))
elif end is not None and filter == 'y':
df = tdd.get_tdx_append_now_df_api(code, start=start, end=end, df=df, dl=dl, power=power).sort_index(ascending=True)
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
df = df[df.index >= start]
if len(df) > 2 and dl is None:
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if dl is not None:
if power:
start, index_d, df = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=power)
else:
start, index_d = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=power)
log.debug("dl not None code:%s start: %s index_d:%s" % (code, start, index_d))
if len(df) > 0 and df is not None:
df = df.sort_index(ascending=True)
df = df[df.index >= start]
if len(df) == 0 or df is None:
if start is not None and len(start) > 8 and int(start[:4]) > 2500:
log.warn("code:%s ERROR:%s" % (code, start))
start = '2016-01-01'
df = tdd.get_tdx_append_now_df_api(
code, start, end).sort_index(ascending=True)
if start is None:
start = df.index.values[0]
if len(df) > 2 and dl is None and start is not None and filter == 'y':
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(
df, dtype).sort_index(ascending=True)
df = df.fillna(0)
if len(df) > 1 + days:
if days != 0:
asset = df[:-days]
else:
asset = df
else:
asset = df
if len(asset) > 1:
operationcount = 0
# ratio_l = []
if countall:
assetratio = asset
nowpratio = df['close'][-days] if len(df) > 1 + days else None
# print assetratio
op, ratio_l, status, sdl = get_linear_model_rule(code, df=assetratio, nowP=nowpratio, ptype=ptype)
# print op,ratio,status,sdl
# ratio_l.append(round(ratio, 2))
operationcount += op
else:
assetratio = asset
nowpratio = df['close'][-days] if len(df) > 1 + days else None
op, ratio_l, status, sdl = get_linear_model_rule(code, df=assetratio, nowP=nowpratio, ptype=ptype)
# ratio_l.append(round(ratio, 2))
operationcount += op
return operationcount, (ratio_l), df[:1].index.values[0], [len(df), df[:1]]
elif len(asset) == 1:
## log.error("powerCompute code:%s"%(code))
if ptype == 'high':
if df.close[-1] >= df.high[-1] * 0.99 and df.close[-1] >= df.open[-1]:
return 12, 0, df.index.values[0], [len(df), df[:1]]
elif df.close[-1] > df.open[-1]:
if df.close[-1] > df.high[-1] * 0.97:
if len(df) > 2 and df.close[-1] > df.close[-2]:
return 10, 0, df.index.values[0], [len(df), df[:1]]
else:
return 11, 0, df.index.values[0], [len(df), df[:1]]
else:
return 9, 0, df.index.values[0], [len(df), df[:1]]
else:
if len(df) >= 2:
if df.close[-1] > df.close[-2] * 1.01:
return 9, 0, df.index.values[0], [len(df), df[:1]]
elif df.close[-1] > df.close[-2]:
return 8, 0, df.index.values[0], [len(df), df[:1]]
elif df.low[-1] > df.low[-2]:
return 6, 0, df.index.values[0], [len(df), df[:1]]
else:
return 3, 0, df.index.values[0], [len(df), df[:1]]
else:
return 1, 0, df.index.values[0], [len(df), df[:1]]
else:
return -10, 0, df.index.values[0], [len(df), df[:1]]
else:
if ptype == 'high':
return 13, 1, cct.get_today(), [len(df), df[:1]]
else:
return -10, -10, cct.get_today(), [len(df), df[:1]]
def get_linear_model_histogram(code,
ptype='low',
dtype='d',
start=None,
end=None,
vtype='f',
filter='n',
df=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 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)
asset = df[ptype]
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 = '999999'
elif code.startswith('399006'):
code2 = '399005'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# print df1[:1]
# 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()
fig = plt.figure(figsize=(16, 5))
# 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(121)
# 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)
# 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(122)
# 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)
ax3.plot(asset1, '-r', linewidth=2)
# assvol = df.loc[asset.index]['vol']
# assvol = assvol.apply(lambda x: round(x / assvol[:1], 2))
# ax3.plot(assvol, '-g', linewidth=2)
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[-1], df1.name[-1]], loc=0)
else:
dm = tdd.get_sina_data_df(code)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
# plt.legend([code, code2], loc=0)
plt.legend([cname, code2], loc=0)
plt.show(block=False)
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_linear_model_status(code, ptype="f", df=None, dtype="d", type="m", start=None, end=None, days=1, filter="y"):
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 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, ptype, start, end).sort_index(ascending=True)
log.info("Code:%s start:%s end:%s" % (code, start, df[-1:].index.values[0]))
if not dtype == "d":
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
def get_linear_model_ratio(asset):
log.info("asset:%s" % asset[-1:])
duration = asset[-1:].index.values[0]
log.debug("duration:%s" % duration)
log.debug("duration:%s" % cct.get_today_duration(duration))
# log.debug("duration:%s"%cct.get_duration_date(duration))
asset = asset.dropna()
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("X:%s a:%0.1f b:%0.1f" % (len(asset), a, b))
Y = np.append(X, X[-1] + int(days))
log.debug("X:%s Y:%s" % (X[-1], Y[-1]))
# print ("X:%s" % (X[-1]))
Y_hat = X * b + a
# Y_hat_t = Y * b + a
# log.info("Y_hat:%s " % (Y_hat))
# log.info("asset:%s " % (asset.values))
ratio = b / a * 100
operation = 0
if Y_hat[-1] > Y_hat[1]:
log.debug("u-Y_hat[-1]:%0.1f" % (Y_hat[-1]))
log.debug("price:%0.1f" % asset.iat[-1])
log.debug("u:%0.1f" % Y_hat[1])
log.debug("price:%0.1f" % asset.iat[1])
if type.upper() == "M":
Y_Future = Y * b + a
# ratio = b/a*100
log.info("ratio: %0.1f %0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
# diff = asset.iat[-1] - Y_hat[-1]
# if diff > 0:
# return True, len(asset), diff
# else:
# return False, len(asset), diff
elif type.upper() == "L":
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
# Y_hatlow = X * b + a - c_low
Y_Future = Y * b + a - c_low
log.info("b: %0.1f ratio:%0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
# diff = asset.iat[-1] - Y_hatlow[-1]
# if asset.iat[-1] - Y_hatlow[-1] > 0:
# return True, len(asset), diff
# else:
# return False, len(asset), diff
elif type.upper() == "H":
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
# Y_hathigh = X * b + a - c_high
Y_Future = Y * b + a - c_high
log.info("ratio: %0.1f %0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
diff = asset[-1] - Y_Future[-1]
# print ("as:%s Y:%s"%(asset[-1] ,Y_Future[-1]))
if diff > 0:
operation += 1
log.info("UP !!%s Y_Future: %0.1f b:%0.1f ratio:%0.1f " % (type.upper(), Y_Future[-1], b, ratio))
else:
log.info("Down %s Y_Future: %0.1f b:%0.1f ratio:%0.1f" % (type.upper(), Y_Future[-1], b, ratio))
return operation, ratio
else:
log.debug("down !!! d:%s" % Y_hat[1])
print ("down !!! d:%s" % Y_hat[1])
return 0, 0
# print "high:",
operationcount = 0
ratio_l = []
for co in ["high", "close", "low"]:
op, ratio = get_linear_model_ratio(df[co])
ratio_l.append(round(ratio, 2))
operationcount += op
log.info("op:%s min:%s ratio_l:%s" % (operationcount, min(ratio_l), ratio_l))
return operationcount, ratio.min()