def get_linear_model_status(code, ptype='f', dtype='d', type='l', start=None, end=None):
df = tdd.get_tdx_append_now_df(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)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
asset = df['close']
log.info("df:%s" % asset[:1])
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:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
if Y_hat[-1] > Y_hat[1]:
log.debug("u:%s" % Y_hat[-1])
log.debug("price:" % asset.iat[-1])
if type.upper() == 'M':
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
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
else:
log.debug("d:%s" % Y_hat[1])
return False, 0, 0
return False, 0, 0
def longsklearn(code='999999', ptype='f', dtype='d', start=None, end=None):
# code='999999'
# dtype = 'w'
# start = '2014-09-01'
# start = None
# end='2015-12-23'
# end = None
df = tdd.get_tdx_append_now_df(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)
dw = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
# print df[:1]
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)
# plt.plot(xt,yt,'-g',linewidth=5)
# plt.plot(closep)
bV = []
bP = []
uV = []
uP = []
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]:
if lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
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]:
if highp[i] >= highp[i - 1] and highp[i] > highp[i + 1]:
uV.append(highp[i])
uP.append(i)
print(highp)
print("uV:%s" % uV[:1])
print("uP:%s" % uP[:1])
print("bV:%s" % bV[:1])
print("bP:%s" % bP[:1])
sV, sP = LIS(uV)
dV, dP = LIS(bV)
print("sV:%s" % sV[:1])
print("sP:%s" % sP[:1])
print("dV:%s" % dV[:1])
print("dP:%s" % dP[:1])
sidx = []
didx = []
for i in range(len(sP)):
# idx.append(bP[p[i]])
sidx.append(uP[sP[i]])
for i in range(len(dP)):
# idx.append(bP[p[i]])
didx.append(bP[dP[i]])
print("sidx:%s" % sidx[:1])
print("didx:%s" % didx[:1])
# plt.plot(closep)
# plt.plot(idx,d,'ko')
lr = LinearRegression()
X = np.atleast_2d(np.array(sidx)).T
Y = np.array(sV)
lr.fit(X, Y)
estV = lr.predict(xt)
fig = plt.figure(figsize=(16, 10), dpi=72)
# 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))
ax = fig.add_subplot(111)
plt.grid(True)
# print h.index[:5], h['close']
ax = h['close'].plot()
# ax.plot(pd.datetime(h.index),h['close'], linewidth=1)
# ax.plot(uP, uV, linewidth=1)
# ax.plot(uP, uV, 'ko')
# ax.plot(bP, bV, linewidth=1)
# ax.plot(bP, bV, 'bo')
# # ax.plot(sP, sV, linewidth=1)
# # ax.plot(sP, sV, 'yo')
# ax.plot(sidx, sV, linewidth=1)
# ax.plot(sidx, sV, 'ro')
# ax.plot(didx, dV, linewidth=1)
# ax.plot(didx, dV, 'co')
df['mean'] = list(
map(lambda h, l: (h + l) / 2, df.high.values, df.low.values))
print(df['mean'][:1])
# d=df.mean
dw = dw.set_index('date')
# print dw[:2]
# ax.plot(df.index,df['mean'],'g',linewidth=1)
ax.plot(df.index, pd.rolling_mean(df['mean'], 60), 'g', linewidth=1)
ax.plot(dw.index, pd.rolling_mean(dw.close, 5), 'r', linewidth=1)
ax.plot(dw.index, pd.rolling_min(dw.close, 5), 'bo')
ax.plot(dw.index, pd.rolling_max(dw.close, 5), 'yo')
ax.plot(dw.index, pd.expanding_max(dw.close, 5), 'ro')
ax.plot(dw.index, pd.expanding_min(dw.close, 5), 'go')
# print pd.rolling_min(df.close,20)[:1],pd.rolling_min(df.close,20)[-1:]
# print pd.rolling_min(df.close,20)
# print pd.rolling_max(df.close,20)[:1],pd.rolling_max(df.close,20)[-1:]
# print pd.rolling_max(df.close,20)
# ax.plot(idx, d, 'ko')
# ax.plot(xt, estV, '-r', linewidth=5)
# ax.plot(xt, yt, '-g', linewidth=5)
# ax2 = fig.add_subplot(122)
# print len(closep),len(idx),len(d),len(xt),len(estV),len(yt)
# f=lambda x:x[-int(len(x)/10):]
# ax2.plot(f(closep))
# ax2.plot(f(idx),f(d),'ko')
# ax2.plot(f(xt),f(estV),'-r',linewidth=5)
# ax2.plot(f(xt),f(yt),'-g',linewidth=5)
# # plt.show()
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax, base_scale=scale)
figPan = zp.pan_factory(ax)
show()
def longsklearn(code='999999'):
# code='999999'
df = tdd.get_tdx_append_now_df(code, 'f').sort_index(ascending=True)
# print df[:1]
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)
# plt.plot(xt,yt,'-g',linewidth=5)
# plt.plot(closep)
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]])
# plt.plot(closep)
# plt.plot(idx,d,'ko')
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
fig = plt.figure(figsize=(16, 10), dpi=72)
# 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))
ax = fig.add_subplot(111)
plt.grid(True)
ax.plot(closep, linewidth=2)
ax.plot(idx, d, 'ko')
ax.plot(xt, estV, '-r', linewidth=5)
ax.plot(xt, yt, '-g', linewidth=5)
# ax2 = fig.add_subplot(122)
# print len(closep),len(idx),len(d),len(xt),len(estV),len(yt)
# f=lambda x:x[-int(len(x)/10):]
# ax2.plot(f(closep))
# ax2.plot(f(idx),f(d),'ko')
# ax2.plot(f(xt),f(estV),'-r',linewidth=5)
# ax2.plot(f(xt),f(yt),'-g',linewidth=5)
# # plt.show()
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax, base_scale=scale)
figPan = zp.pan_factory(ax)
show()
def longsklearn(code='999999', ptype='f',dtype='d',start=None,end=None):
# code='999999'
# dtype = 'w'
# start = '2014-09-01'
# start = None
# end='2015-12-23'
# end = None
df = tdd.get_tdx_append_now_df(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)
dw = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
# print df[:1]
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)
# plt.plot(xt,yt,'-g',linewidth=5)
# plt.plot(closep)
bV = []
bP = []
uV = []
uP = []
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]:
if lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
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]:
if highp[i] >= highp[i - 1] and highp[i] > highp[i + 1]:
uV.append(highp[i])
uP.append(i)
print highp
print "uV:%s" % uV[:1]
print "uP:%s" % uP[:1]
print "bV:%s" % bV[:1]
print "bP:%s" % bP[:1]
sV, sP = LIS(uV)
dV, dP = LIS(bV)
print "sV:%s" % sV[:1]
print "sP:%s" % sP[:1]
print "dV:%s" % dV[:1]
print "dP:%s" % dP[:1]
sidx = []
didx = []
for i in range(len(sP)):
# idx.append(bP[p[i]])
sidx.append(uP[sP[i]])
for i in range(len(dP)):
# idx.append(bP[p[i]])
didx.append(bP[dP[i]])
print "sidx:%s"%sidx[:1]
print "didx:%s"%didx[:1]
# plt.plot(closep)
# plt.plot(idx,d,'ko')
lr = LinearRegression()
X = np.atleast_2d(np.array(sidx)).T
Y = np.array(sV)
lr.fit(X, Y)
estV = lr.predict(xt)
fig = plt.figure(figsize=(16, 10), dpi=72)
# 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))
ax = fig.add_subplot(111)
plt.grid(True)
# print h.index[:5], h['close']
ax = h['close'].plot()
# ax.plot(pd.datetime(h.index),h['close'], linewidth=1)
# ax.plot(uP, uV, linewidth=1)
# ax.plot(uP, uV, 'ko')
# ax.plot(bP, bV, linewidth=1)
# ax.plot(bP, bV, 'bo')
# # ax.plot(sP, sV, linewidth=1)
# # ax.plot(sP, sV, 'yo')
# ax.plot(sidx, sV, linewidth=1)
# ax.plot(sidx, sV, 'ro')
# ax.plot(didx, dV, linewidth=1)
# ax.plot(didx, dV, 'co')
df['mean']=map(lambda h,l:(h+l)/2,df.high.values,df.low.values)
print df['mean'][:1]
# d=df.mean
dw=dw.set_index('date')
# print dw[:2]
# ax.plot(df.index,df['mean'],'g',linewidth=1)
ax.plot(df.index,pd.rolling_mean(df['mean'], 60), 'g',linewidth=1)
ax.plot(dw.index,pd.rolling_mean(dw.close, 5), 'r',linewidth=1)
ax.plot(dw.index,pd.rolling_min(dw.close, 5), 'bo')
ax.plot(dw.index,pd.rolling_max(dw.close, 5), 'yo')
ax.plot(dw.index,pd.expanding_max(dw.close, 5), 'ro')
ax.plot(dw.index,pd.expanding_min(dw.close, 5), 'go')
# print pd.rolling_min(df.close,20)[:1],pd.rolling_min(df.close,20)[-1:]
# print pd.rolling_min(df.close,20)
# print pd.rolling_max(df.close,20)[:1],pd.rolling_max(df.close,20)[-1:]
# print pd.rolling_max(df.close,20)
# ax.plot(idx, d, 'ko')
# ax.plot(xt, estV, '-r', linewidth=5)
# ax.plot(xt, yt, '-g', linewidth=5)
# ax2 = fig.add_subplot(122)
# print len(closep),len(idx),len(d),len(xt),len(estV),len(yt)
# f=lambda x:x[-int(len(x)/10):]
# ax2.plot(f(closep))
# ax2.plot(f(idx),f(d),'ko')
# ax2.plot(f(xt),f(estV),'-r',linewidth=5)
# ax2.plot(f(xt),f(yt),'-g',linewidth=5)
# # plt.show()
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax, base_scale=scale)
figPan = zp.pan_factory(ax)
show()
def get_linear_model_histogram(code, ptype="f", dtype="d", start=None, end=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)
df = tdd.get_tdx_append_now_df(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["close"]
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(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, "close"]
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)
# 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()
ax1.set_xticklabels([dates[i] for i in ticks[:-1]]) # 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=14)
plt.ylabel("Price", fontsize=14)
plt.title(code, fontsize=14)
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)
ticks = ax2.get_xticks()
ax2.set_xticklabels([dates[i] for i in ticks[:-1]])
# 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=14)
plt.ylabel("Price", fontsize=14)
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 ticks[:-1]])
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=14)
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=14)
plt.ylabel("Frequency", fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]])
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=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]])
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)
show()
