下面给出计算隐马尔科夫模型的编程代码：from hmmlearn.hmm importGaussianHMMimportdatetimeimportnumpy as npfrom matplotlib importcm, pyplot as pltimportmatplotlib.dates as datesimportpandas as pdimport seaborn as sns#导入模块

beginDate= '20100401'endDate= '20160401'data=DataAPI.MktIdxdGet(ticker='000001',beginDate=beginDate,endDate=endDate,field=['tradeDate','closeIndex','lowestIndex','highestIndex','turnoverVol'],pandas="1")

data1=DataAPI.FstTotalGet(exchangeCD=u"XSHE",beginDate=beginDate,endDate=end

Date,field=['tradeVal'],pandas="1")#深圳交易所融资融券信息,'tradeval'是指当日 融资融券余额

data2=DataAPI.FstTotalGet(exchangeCD=u"XSHG",beginDate=beginDate,endDate=end

Date,field=['tradeVal'],pandas="1")#上海交易所融资融券信息,'tradeval'是指当日 融资融券余额

tradeVal = data1 + data2 #数据汇总

tradeDate = pd.to_datetime(data['tradeDate'][5:])

volume= data['turnoverVol'][5:] #成交量

closeIndex = data['closeIndex']#收盘价

deltaIndex=(np.log(np.array(data['highestIndex']))-np.log(np.array(data['lowestIndex'])))[5:]

logReturn1= np.array(np.diff(np.log(closeIndex)))[4:]#取对数收益率

logReturn5=np.log(np.array(closeIndex[5:]))- np.log(np.array(closeIndex[:-5]))#5 日对数收益率

logReturnFst = (np.array(np.diff(np.log(tradeVal['tradeVal']))))[4:]

closeIndex= closeIndex[5:]#收盘价截取

from scipy import stats #To perform box-cox transformation

from sklearn import preprocessing #导入模块

boxcox_volume,lmbda = stats.boxcox(volume/100000000000)#Standardize the observation sequence distribution

rescaled_boxcox_volume=preprocessing.scale(boxcox_volume,axis=0, with_mean=True,

with_std=True, copy=False)#数据处理

boxcox_deltaIndex,lmbda =stats.boxcox(deltaIndex)#Standardize the observation sequence distribution

rescaled_boxcox_deltaIndex=preprocessing.scale(boxcox_deltaIndex, axis=0,

with_mean=True, with_std=True, copy=False)

X=np.column_stack([logReturn1,logReturn5,rescaled_boxcox_deltaIndex,rescaled_boxco

x_volume,logReturnFst])#数据整合#Make an HMM instance and execute fit

model=GaussianHMM(n_components=5,covariance_type="diag", n_iter=10000).fit([X])#Predict the optimal sequence of internal hidden state#计算模型

hidden_states = model.predict(X)#估计状态

print("Transition matrix")print(model.transmat_)print()#print("Means and vars of each hidden state")

for i inrange(model.n_components):print("{0}th hidden state".format(i))print("mean =", model.means_[i])print("var =", np.diag(model.covars_[i]))

plt.figure(figsize=(15, 8))

plt.title('hidden states')for i inrange(model.n_components):

idx= (hidden_states==i)

plt.plot_date(tradeDate[idx],closeIndex[idx],'.',label='%dth hidden state'%i,lw=1)

plt.legend()

plt.grid(True)

model.transmat_importxlwtimportxlrd

wb3=xlwt.Workbook()

wb3.add_sheet('first',cell_overwrite_ok=True)

ws_1=wb3.get_sheet(0)for r in range(model.transmat_.shape[0]): #行数

for c in range(model.transmat_.shape[1]): #列数

ws_1.write(r,c,model.transmat_[r,c])

wb3.save('文件夹 0/上证指数转移概率矩阵.xls')#整合数据

res=pd.DataFrame({'tradeDate':tradeDate,'logReturn1':logReturn1,'logReturn5':logReturn5,'volume':volume,'zstate':hidden_states}).set_index('tradeDate')

plt.figure(figsize=(15, 8))

plt.xlabel('time')

plt.ylabel('earninigs multiplier')for i inrange(model.n_components):

idx= (hidden_states==i)

idx= np.append(0,idx[:-1])#获得状态结果后第二天进行买入操作#fast factor backtest

df =res.logReturn1

res['sig_ret%s'%i] = df.multiply(idx,axis=0)

plt.plot(np.exp(res['sig_ret%s'%i].cumsum()),label='%dth hidden state'%i)

plt.legend()

plt.grid(1)