R语言金融深度学习 .pdf

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1. R语言金融深度学习
李智
Q群261821669,本篇数据下载
July 31, 2017

Chapter 1
深度学习
1.1

Deep Learning

使用R语言可以比较容易地实现深度学习(Deep Learning)。深度学习的具
体程序编写都是使用神经网络,这是利用了神经网络可以有多个层级的特
性。本篇资料使用deepnet包,当然也有其他的包可以做深度学习。深度学
习又被称作分级学习(Hierarchical Learning),是因为数据本身都是分层级
的,数据处理也必须分层级。细节请参考传统Bayesian理论。首先我们还
是使用features()函数提取数据里的特色变量。
features<-function(price,p = 14){
temp <- subset(price[,5:8])
Med
<- (Hi(price) + Lo(price))/2
price <- cbind(temp, Med)
adx<-ADX(price, n = p)
ar<-aroon(price[ ,c(’High’, ’Low’)], n=p)[ ,’oscillator’]
cci<-CCI(price[ ,2:4], n = p)
chv<-chaikinVolatility(price[ ,2:4], n = p)
1

cmo<-CMO(price[ ,’Med’], n = p)
macd<-MACD(price[ ,’Med’], 12, 26, 9)[ ,’macd’]
osma<-macd - MACD(price[ ,’Med’],12, 26, 9)[ ,’signal’]
rsi<-RSI(price[ ,’Med’], n = p)
stoh<-stoch(price[ ,2:4],14, 3, 3)
vol<-volatility(price[ ,1:4],n = p,calc="yang.zhang", N=96)
xavg<-EMA(price[,4],n=p)
trend<-price[,4]-xavg;
atr5<-ATR(HLC(price),5)
atr5<-atr5[,2]
Input<-cbind(adx, ar, cci, chv, cmo, macd, osma, rsi, stoh,vol,
xavg,trend,atr5)
return(Input)
}

1.2

Zigzag

Zigzag是把价格序列的折返(pullback)点用直线连接。这就必须定义价格变
化的大小change,它可以是dollar或percent。这里用percent=T,计算方法
是ch=500*sd(na.omit(ROC(Cl(price)))) 。然后给zigzag线做差,如果差是
正的标记1说明价格上升,如果差是负的说明价格下降标记0。
trend<-function(price){
ch=500*sd(na.omit(ROC(Cl(price))))
zz<-ZigZag(Cl(price), change = ch, percent = T,
retrace = F, lastExtreme = T)
ts.plot(zz)
for(i in 1:length(zz)) { if(is.na(zz[i])) zz[i] = zz[i-1]}
dz<-c(diff(zz), NA)
sig<-ifelse(dz>0, 1, ifelse(dz<0, 0, NA))
return(sig)
}
2

1.3

DNN SAE

主程序读取两组数据,2011-2014年的xom1,2015-2016年的xom2。用xom1建
模,用xom2测试模型。xom1提取features,然后预处理,生成X。xom2提
取features, 然 后 预 处 理 生 成vali。Y是xom1的zigzag生 成 的 。 用X和Y建
立SAE模型。vali输入SAE模型,可以生成15-16年的预测。把这个预测乘
以xom2的回报率r,就可以作图了。最后的结果是1.1。
library(caret)
library(deepnet)
library(quantmod)
library(PerformanceAnalytics)
set.seed(1)
setwd("C:/Users/User/Dropbox/Writting Outlines/Deep Learning/")
xom1<-read.csv("XOM_110101_141231.csv",header=TRUE)
xom2<-read.csv("XOM_150101_161231.csv",header=TRUE)
training
<- na.omit(cbind(features(xom1),trend(xom1)))
validation <- na.omit(features(xom2))
preProc<- preProcess(training[,-ncol(training)],
method = "spatialSign")
X
<- predict(preProc, training[,-ncol(training)])
Y
<- training[,ncol(training)]
vali
<- predict(preProc, validation)
SAE<-sae.dnn.train(x= X, y=Y, hidden=c(50,50,50),
activationfun = "tanh", learningrate = 0.6,
momentum = 0.5, learningrate_scale = 1.0,
output = "sigm", sae_output = "linear",
numepochs = 10, batchsize = 100,
hidden_dropout = 0, visible_dropout = 0)
predict.vali <-round(nn.predict(SAE, vali))
vali.date <-as.Date(as.character(xom2$Date),format="%Y%m%d")
3

Figure 1.1: 深度学习回报率曲线图
r <-ROC(Cl(xom2))
m <- length(r)-length(predict.vali)
r <- tail(r,-m)
vali.date <- tail(vali.date,-m)
profit<-predict.vali*r
profit<-xts(profit,vali.date)
charts.PerformanceSummary(profit)

4

1.4

R语
语言 文 库 资 源

1. 统计套利2017
2. R语言量化金融工程2016
3. R语言时间序列中文教程
4. R语言金融工程中文教程
5. R语言样品比较应用举例
6. R语言德国坦克问题
7. R语言医药研究
8. R语言形成三角形的概率
9. R语言小波分析
10. R语言太空总署的蓄电池

5


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