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创建页面，内容为“==原理== HMM的原理参考[http://wiki.swarma.net/index.php/HMM%E9%9A%90%E6%80%A7%E9%A9%AC%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B 这里] 使用HMM拟合股票市…”
==原理==
HMM的原理参考[http://wiki.swarma.net/index.php/HMM%E9%9A%90%E6%80%A7%E9%A9%AC%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B 这里]
使用HMM拟合股票市场数据实验参考[http://cs229.stanford.edu/proj2009/ShinLee.pdf 这篇]论文
==准备数据==
<syntaxhighlight lang="python">
import datetime
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
from sklearn.hmm import GaussianHMM
# parepare data
date1 = datetime.date(1995, 1, 1) # start date
date2 = datetime.date(2012, 1, 6) # end date
# get quotes from yahoo finance
quotes = quotes_historical_yahoo("INTC", date1, date2)
#data:
#Date Open Close High Low Volume
# unpack quotes
dates = np.array([q[0] for q in quotes], dtype=int)
close_v = np.array([q[2] for q in quotes])
volume = np.array([q[5] for q in quotes])[1:]
# take diff of close value
# this makes len(diff) = len(close_t) - 1
# therefore, others quantity also need to be shifted
diff = close_v[1:] - close_v[:-1]
dates = dates[1:]
close_v = close_v[1:]
# pack diff and volume for training
X = np.column_stack([diff, volume])
#plt.plot(close_v,volume,"bo")
</syntaxhighlight>
==拟合模型==
<syntaxhighlight lang="python">
#fit Gaussian HMM
n_components = 5
# make an HMM instance and execute fit
model = GaussianHMM(n_components, covariance_type="diag", n_iter=1000)
model.fit([X])
# predict the optimal sequence of internal hidden state
hidden_states = model.predict(X)
</syntaxhighlight>
==打印模型训练的结果并绘图==
<syntaxhighlight lang="python">
# print trained parameters and plot
print(np.round(model.transmat_,2))
print("means and vars of each hidden state")
for i in range(n_components):
print("%dth hidden state" % i)
print("mean = ", model.means_[i])
print("var = ",np.diag(model.covars_[i]) )
print()
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
fig = plt.figure()
ax = fig.add_subplot(111)
for i in range(n_components):
# use fancy indexing to plot data in each state
idx = (hidden_states == i)
ax.plot_date(dates[idx], close_v[idx], 'o', label="%dth hidden state" % i)
# format the figure
ax.legend()
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.fmt_ydata = lambda x: '$%1.2f' % x
ax.grid(True)
fig.autofmt_xdate()
#plt.plot()
</syntaxhighlight>
==HMM模型拟合==
[[File:stockmarket_HMM_1.png|400px]]
HMM的原理参考[http://wiki.swarma.net/index.php/HMM%E9%9A%90%E6%80%A7%E9%A9%AC%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B 这里]
使用HMM拟合股票市场数据实验参考[http://cs229.stanford.edu/proj2009/ShinLee.pdf 这篇]论文
==准备数据==
<syntaxhighlight lang="python">
import datetime
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
from sklearn.hmm import GaussianHMM
# parepare data
date1 = datetime.date(1995, 1, 1) # start date
date2 = datetime.date(2012, 1, 6) # end date
# get quotes from yahoo finance
quotes = quotes_historical_yahoo("INTC", date1, date2)
#data:
#Date Open Close High Low Volume
# unpack quotes
dates = np.array([q[0] for q in quotes], dtype=int)
close_v = np.array([q[2] for q in quotes])
volume = np.array([q[5] for q in quotes])[1:]
# take diff of close value
# this makes len(diff) = len(close_t) - 1
# therefore, others quantity also need to be shifted
diff = close_v[1:] - close_v[:-1]
dates = dates[1:]
close_v = close_v[1:]
# pack diff and volume for training
X = np.column_stack([diff, volume])
#plt.plot(close_v,volume,"bo")
</syntaxhighlight>
==拟合模型==
<syntaxhighlight lang="python">
#fit Gaussian HMM
n_components = 5
# make an HMM instance and execute fit
model = GaussianHMM(n_components, covariance_type="diag", n_iter=1000)
model.fit([X])
# predict the optimal sequence of internal hidden state
hidden_states = model.predict(X)
</syntaxhighlight>
==打印模型训练的结果并绘图==
<syntaxhighlight lang="python">
# print trained parameters and plot
print(np.round(model.transmat_,2))
print("means and vars of each hidden state")
for i in range(n_components):
print("%dth hidden state" % i)
print("mean = ", model.means_[i])
print("var = ",np.diag(model.covars_[i]) )
print()
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
fig = plt.figure()
ax = fig.add_subplot(111)
for i in range(n_components):
# use fancy indexing to plot data in each state
idx = (hidden_states == i)
ax.plot_date(dates[idx], close_v[idx], 'o', label="%dth hidden state" % i)
# format the figure
ax.legend()
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.fmt_ydata = lambda x: '$%1.2f' % x
ax.grid(True)
fig.autofmt_xdate()
#plt.plot()
</syntaxhighlight>
==HMM模型拟合==
[[File:stockmarket_HMM_1.png|400px]]