原文链接：http://tecdat.cn/?p=12187
金融分析师通常关心检测市场何时“发生变化”：几个月或至几年内市场的典型行为可以立即转变为非常不同的行为。投资者希望及时发现这些变化，以便可以相应地调整其策略，但是这样做可能很困难。
RHmm从CRAN不再可用，因此我想使用其他软件包复制功能实现马尔科夫机制转换(Markov regime switching)模型从而对典型的市场行为进行预测，并且增加模型中对参数的线性约束功能。
library(SIT) load.packages('quantmod') # find regimes load.packages('RHmm', repos ='http://R-Forge.R-project.org') y=returns ResFit = HMMFit(y, nStates=2) VitPath = viterbi(ResFit, y)
DimObs = 1
matplot(fb$Gamma, type='l', main='Smoothed Probabilities', ylab='Probability') legend(x='topright', c('State1','State2'), fill=1:2, bty='n')
fm2 = fit(mod, verbose = FALSE)
使用logLik在迭代69处收敛：125.6168
probs = posterior(fm2) layout(1:2) plot(probs$state, type='s', main='Implied States', xlab='', ylab='State') matplot(probs[,-1], type='l', main='Probabilities', ylab='Probability') legend(x='topright', c('State1','State2'), fill=1:2, bty='n')
#***************************************************************** # Add some data and see if the model is able to identify the regimes #****************************************************************** bear2 = rnorm( 100, -0.01, 0.20 ) bull3 = rnorm( 100, 0.10, 0.10 ) bear3 = rnorm( 100, -0.01, 0.25 ) true.states = c(true.states, rep(2,100),rep(1,100),rep(2,100)) y = c( bull1, bear, bull2, bear2, bull3, bear3 )
DimObs = 1
plota(data, type='h', x.highlight=T) plota.legend('Returns + Detected Regimes')
#***************************************************************** # Load historical prices #****************************************************************** data = env() getSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T) price = Cl(data$SPY) open = Op(data$SPY) ret = diff(log(price)) ret = log(price) - log(open) atr = ATR(HLC(data$SPY))[,'atr'] fm2 = fit(mod, verbose = FALSE)
使用logLik在迭代30处收敛：18358.98
print(summary(fm2))
