library(caret)## Loading required package: lattice
## Loading required package: ggplot2library(wikipediatrend)
views<-wp_trend(page = "Citigroup",from = "2010-01-01",to = "2014-12-31",lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/Citigroup
## http://stats.grok.se/json/en/201002/Citigroup
## http://stats.grok.se/json/en/201003/Citigroup
## http://stats.grok.se/json/en/201004/Citigroup
## http://stats.grok.se/json/en/201005/Citigroup
## http://stats.grok.se/json/en/201006/Citigroup
## http://stats.grok.se/json/en/201007/Citigroup
## http://stats.grok.se/json/en/201008/Citigroup
## http://stats.grok.se/json/en/201009/Citigroup
## http://stats.grok.se/json/en/201010/Citigroup
## http://stats.grok.se/json/en/201011/Citigroup
## http://stats.grok.se/json/en/201012/Citigroup
## http://stats.grok.se/json/en/201101/Citigroup
## http://stats.grok.se/json/en/201102/Citigroup
## http://stats.grok.se/json/en/201103/Citigroup
## http://stats.grok.se/json/en/201104/Citigroup
## http://stats.grok.se/json/en/201105/Citigroup
## http://stats.grok.se/json/en/201106/Citigroup
## http://stats.grok.se/json/en/201107/Citigroup
## http://stats.grok.se/json/en/201108/Citigroup
## http://stats.grok.se/json/en/201109/Citigroup
## http://stats.grok.se/json/en/201110/Citigroup
## http://stats.grok.se/json/en/201111/Citigroup
## http://stats.grok.se/json/en/201112/Citigroup
## http://stats.grok.se/json/en/201201/Citigroup
## http://stats.grok.se/json/en/201202/Citigroup
## http://stats.grok.se/json/en/201203/Citigroup
## http://stats.grok.se/json/en/201204/Citigroup
## http://stats.grok.se/json/en/201205/Citigroup
## http://stats.grok.se/json/en/201206/Citigroup
## http://stats.grok.se/json/en/201207/Citigroup
## http://stats.grok.se/json/en/201208/Citigroup
## http://stats.grok.se/json/en/201209/Citigroup
## http://stats.grok.se/json/en/201210/Citigroup
## http://stats.grok.se/json/en/201211/Citigroup
## http://stats.grok.se/json/en/201212/Citigroup
## http://stats.grok.se/json/en/201301/Citigroup
## http://stats.grok.se/json/en/201302/Citigroup
## http://stats.grok.se/json/en/201303/Citigroup
## http://stats.grok.se/json/en/201304/Citigroup
## http://stats.grok.se/json/en/201305/Citigroup
## http://stats.grok.se/json/en/201306/Citigroup
## http://stats.grok.se/json/en/201307/Citigroup
## http://stats.grok.se/json/en/201308/Citigroup
## http://stats.grok.se/json/en/201309/Citigroup
## http://stats.grok.se/json/en/201310/Citigroup
## http://stats.grok.se/json/en/201311/Citigroup
## http://stats.grok.se/json/en/201312/Citigroup
## http://stats.grok.se/json/en/201401/Citigroup
## http://stats.grok.se/json/en/201402/Citigroup
## http://stats.grok.se/json/en/201403/Citigroup
## http://stats.grok.se/json/en/201404/Citigroup
## http://stats.grok.se/json/en/201405/Citigroup
## http://stats.grok.se/json/en/201406/Citigroup
## http://stats.grok.se/json/en/201407/Citigroup
## http://stats.grok.se/json/en/201408/Citigroup
## http://stats.grok.se/json/en/201409/Citigroup
## http://stats.grok.se/json/en/201410/Citigroup
## http://stats.grok.se/json/en/201411/Citigroup
## http://stats.grok.se/json/en/201412/Citigrouplibrary(quantmod)## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Loading required package: TTR
## Version 0.4-0 included new data defaults. See ?getSymbols.startDate = as.Date("2010-01-01")
endDate = as.Date("2014-12-31")
getSymbols("c", src = "yahoo", from = startDate, to = endDate) ## As of 0.4-0, 'getSymbols' uses env=parent.frame() and
## auto.assign=TRUE by default.
##
## This behavior will be phased out in 0.5-0 when the call will
## default to use auto.assign=FALSE. getOption("getSymbols.env") and
## getOptions("getSymbols.auto.assign") are now checked for alternate defaults
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for more details.## [1] "C"RSI3<-RSI(Op(C), n= 3)
#Calculate a 3-period relative strength index (RSI) off the open price
EMA5<-EMA(Op(C),n=5)
#Calculate a 5-period exponential moving average (EMA)
EMAcross<- Op(C)-EMA5
#Let’s explore the difference between the open price and our 5-period EMA
DEMA10<-DEMA(Cl(C),n = 10, v = 1, wilder = FALSE)
DEMA10c<-Cl(C) - DEMA10
MACD<-MACD(Op(C),fast = 12, slow = 26, signal = 9)
#Calculate a MACD with standard parameters
MACDsignal<-MACD[,2]
#Grab just the signal line to use as our indicator.
SMI<-SMI(Op(C),n=13,slow=25,fast=2,signal=9)
#Stochastic Oscillator with standard parameters
SMI<-SMI[,1]
#Grab just the oscillator to use as our indicator
BB<-BBands(Op(C),n=20,sd=2)
BBp<-BB[,4]
CCI20<-CCI(C[,3:5],n=20)
#A 20-period Commodity Channel Index calculated of the High/Low/Close of our data
PriceChange<- Cl(C) - Op(C)
#Calculate the difference between the close price and open price
Class<-ifelse(PriceChange>0,"UP","DOWN")
#Create a binary classification variable, the variable we are trying to predict.
DJIADF<-data.frame(date = index(C), C, row.names=NULL)
CombDF<-merge(views,DJIADF, by.x='date', by.y='date')
DataSet<-data.frame(RSI3,EMAcross,MACDsignal,SMI,BBp,CCI20,DEMA10c)
DataSet<-DataSet[-c(1:33),]
Alldata<-cbind(DataSet,CombDF[34:1258,2])
Normalized <-function(x) {(x-min(x))/(max(x)-min(x))}
NormalizedData<-as.data.frame(lapply(Alldata,Normalized))
ClassDF<-data.frame(date = index(Class), Class, row.names=NULL)
AlldataNormalized<-data.frame(NormalizedData,ClassDF[34:1258,2])
colnames(AlldataNormalized)<-c("RSI3","EMAcross","MACDsignal","SMI","BBp","CCI20","DEMA10c","Views","Class")
TrainingSet<-AlldataNormalized[1:1000,]
TestSet<-AlldataNormalized[1001:1225,]TrainClass<-TrainingSet[,9]
TrainPred<-TrainingSet[,-9]
TestClass<-TestSet[,9]
TestPred<-TestSet[,-9] library(h2o)## Loading required package: statmod
##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit http://docs.h2o.ai
##
## ----------------------------------------------------------------------
##
##
## Attaching package: 'h2o'
##
## The following objects are masked from 'package:stats':
##
## sd, var
##
## The following objects are masked from 'package:base':
##
## %*%, apply, as.factor, as.numeric, colnames, colnames<-,
## ifelse, %in%, is.factor, is.numeric, log, trunclocalH2O <- h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)## Successfully connected to http://localhost:54321/
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 26 minutes 10 seconds
## H2O cluster version: 3.6.0.8
## H2O cluster name: H2O_started_from_R_mitra2_ogx890
## H2O cluster total nodes: 1
## H2O cluster total memory: 0.66 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 2
## H2O cluster healthy: TRUElocalH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE,
Xmx = '2g')## Warning in h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, Xmx =
## "2g"): Xmx is a deprecated parameter. Use `max_mem_size` and `min_mem_size`
## to set the memory boundaries. Using `Xmx` to set these.## Successfully connected to http://localhost:54321/
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 26 minutes 10 seconds
## H2O cluster version: 3.6.0.8
## H2O cluster name: H2O_started_from_R_mitra2_ogx890
## H2O cluster total nodes: 1
## H2O cluster total memory: 0.66 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 2
## H2O cluster healthy: TRUETrainH2o<-as.h2o(TrainingSet, destination_frame = "TrainH2o")##
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|=================================================================| 100%head(TrainH2o)## RSI3 EMAcross MACDsignal SMI BBp CCI20 DEMA10c
## 1 0.9331516 0.1236991 0.09775004 0.3548534 0.6627022 0.14498935 0.1561449
## 2 0.9331516 0.1229697 0.10464865 0.4170325 0.6388570 0.18036729 0.1528981
## 3 0.3934442 0.1209599 0.11062878 0.4554274 0.5330026 0.19056025 0.1552461
## 4 0.5136515 0.1215789 0.11598789 0.4891637 0.5485061 0.17375920 0.1533669
## 5 0.5136515 0.1215562 0.12074544 0.5191934 0.5380094 0.12010509 0.1536277
## 6 0.7088161 0.1221941 0.12521381 0.5534579 0.5664558 0.09677432 0.1533921
## Views Class
## 1 0.2220165 UP
## 2 0.2130447 DOWN
## 3 0.1915409 UP
## 4 0.1693250 DOWN
## 5 0.1706067 UP
## 6 0.1815722 DOWNTestH2o<-as.h2o(TestPred, destination_frame = "TestH2o")##
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|=================================================================| 100%Deeplearningmodel1 <- h2o.deeplearning(x = 1:8,y = 9,training_frame = TrainH2o, activation = "TanhWithDropout",hidden = c(50,50,50,100),epochs = 100)##
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|=================================================================| 100%Deeplearningmodel2 <- h2o.deeplearning(x = 1:8,y = 9,training_frame = TrainH2o, activation = "Rectifier",hidden = c(50,50,50,200),epochs = 200)##
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|=================================================================| 100%Deeplearningmodel3 <- h2o.deeplearning(x = 1:8,y = 9,training_frame = TrainH2o, activation = "Tanh",hidden = c(100,100),epochs = 300)##
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|=================================================================| 100%Deeplearningmodel4 <- h2o.deeplearning(x = 1:8,y = 9,training_frame = TrainH2o, activation = "TanhWithDropout",hidden = c(100,100),epochs = 400)##
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|=================================================================| 100%h2o_yhat_test1 <- h2o.predict(Deeplearningmodel1,TestH2o)
df_yhat_test1 <- as.data.frame(h2o_yhat_test1)
h2o_yhat_test2 <- h2o.predict(Deeplearningmodel2,TestH2o)
df_yhat_test2 <- as.data.frame(h2o_yhat_test2)
h2o_yhat_test3 <- h2o.predict(Deeplearningmodel3,TestH2o)
df_yhat_test3 <- as.data.frame(h2o_yhat_test3)
h2o_yhat_test4 <- h2o.predict(Deeplearningmodel4,TestH2o)
df_yhat_test4 <- as.data.frame(h2o_yhat_test4)
DeepLearninPred1<-as.data.frame(df_yhat_test1[,1])
DeepLearninPred2<-as.data.frame(df_yhat_test2[,1])
DeepLearninPred3<-as.data.frame(df_yhat_test3[,1])
DeepLearninPred4<-as.data.frame(df_yhat_test4[,1])prediction1 <-df_yhat_test1[,1]
table(prediction1,TestClass)## TestClass
## prediction1 DOWN UP
## DOWN 91 15
## UP 17 102confusionMatrix(prediction1,TestClass)## Confusion Matrix and Statistics
##
## Reference
## Prediction DOWN UP
## DOWN 91 15
## UP 17 102
##
## Accuracy : 0.8578
## 95% CI : (0.8052, 0.9006)
## No Information Rate : 0.52
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.7149
## Mcnemar's Test P-Value : 0.8597
##
## Sensitivity : 0.8426
## Specificity : 0.8718
## Pos Pred Value : 0.8585
## Neg Pred Value : 0.8571
## Prevalence : 0.4800
## Detection Rate : 0.4044
## Detection Prevalence : 0.4711
## Balanced Accuracy : 0.8572
##
## 'Positive' Class : DOWN
## prediction2 <-df_yhat_test2[,1]
table(prediction2,TestClass)## TestClass
## prediction2 DOWN UP
## DOWN 101 27
## UP 7 90confusionMatrix(prediction2,TestClass)## Confusion Matrix and Statistics
##
## Reference
## Prediction DOWN UP
## DOWN 101 27
## UP 7 90
##
## Accuracy : 0.8489
## 95% CI : (0.7953, 0.893)
## No Information Rate : 0.52
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.6994
## Mcnemar's Test P-Value : 0.00112
##
## Sensitivity : 0.9352
## Specificity : 0.7692
## Pos Pred Value : 0.7891
## Neg Pred Value : 0.9278
## Prevalence : 0.4800
## Detection Rate : 0.4489
## Detection Prevalence : 0.5689
## Balanced Accuracy : 0.8522
##
## 'Positive' Class : DOWN
## prediction3 <-df_yhat_test3[,1]
table(prediction3,TestClass)## TestClass
## prediction3 DOWN UP
## DOWN 92 21
## UP 16 96confusionMatrix(prediction3,TestClass)## Confusion Matrix and Statistics
##
## Reference
## Prediction DOWN UP
## DOWN 92 21
## UP 16 96
##
## Accuracy : 0.8356
## 95% CI : (0.7805, 0.8815)
## No Information Rate : 0.52
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.6712
## Mcnemar's Test P-Value : 0.5108
##
## Sensitivity : 0.8519
## Specificity : 0.8205
## Pos Pred Value : 0.8142
## Neg Pred Value : 0.8571
## Prevalence : 0.4800
## Detection Rate : 0.4089
## Detection Prevalence : 0.5022
## Balanced Accuracy : 0.8362
##
## 'Positive' Class : DOWN
## prediction4 <-df_yhat_test4[,1]
table(prediction4,TestClass)## TestClass
## prediction4 DOWN UP
## DOWN 97 20
## UP 11 97confusionMatrix(prediction4,TestClass)## Confusion Matrix and Statistics
##
## Reference
## Prediction DOWN UP
## DOWN 97 20
## UP 11 97
##
## Accuracy : 0.8622
## 95% CI : (0.8102, 0.9044)
## No Information Rate : 0.52
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.7249
## Mcnemar's Test P-Value : 0.1508
##
## Sensitivity : 0.8981
## Specificity : 0.8291
## Pos Pred Value : 0.8291
## Neg Pred Value : 0.8981
## Prevalence : 0.4800
## Detection Rate : 0.4311
## Detection Prevalence : 0.5200
## Balanced Accuracy : 0.8636
##
## 'Positive' Class : DOWN
## MetaData<-cbind(DeepLearninPred1,DeepLearninPred2,DeepLearninPred3,DeepLearninPred4,TestClass)
colnames(MetaData)<-c("DeepLearninPred1","DeepLearninPred2","DeepLearninPred3","DeepLearninPred4","TestClass")
MetaDataH2o<-as.h2o(MetaData, destination_frame = "MetaData")##
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|=================================================================| 100%MetaClass<-MetaData[,5]
MetaPred<-MetaData[,-5]
Deeplearningmodelmeta <- h2o.deeplearning(x = 1:3,y = 4,training_frame = MetaDataH2o, activation = "TanhWithDropout",hidden = c(50,50,50,100),epochs = 10000)##
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|=================================================================| 100%summary(Deeplearningmodelmeta)## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model Key: DeepLearning_model_R_1453199765449_50
## Status of Neuron Layers: predicting DeepLearninPred4, 2-class classification, bernoulli distribution, CrossEntropy loss, 10,902 weights/biases, 135.3 KB, 429,750 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 9 Input 0.00 %
## 2 2 50 TanhDropout 50.00 % 0.000000 0.000000 0.334743 0.466854
## 3 3 50 TanhDropout 50.00 % 0.000000 0.000000 0.000199 0.000093
## 4 4 50 TanhDropout 50.00 % 0.000000 0.000000 0.004020 0.003805
## 5 5 100 TanhDropout 50.00 % 0.000000 0.000000 0.038027 0.051878
## 6 6 2 Softmax 0.000000 0.000000 0.001639 0.000189
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.000000 -0.045485 1.659931 -0.064849 0.743728
## 3 0.000000 0.036474 2.127906 -0.065104 1.151216
## 4 0.000000 0.000183 0.787920 0.031078 0.421215
## 5 0.000000 -0.002099 0.221158 -0.005316 0.194145
## 6 0.000000 0.016725 0.388165 -0.001375 0.018936
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.05079234
## R^2: 0.7965051
## LogLoss: 0.2120665
## AUC: 0.9187638
## Gini: 0.8375277
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 112 5 0.042735 =5/117
## UP 7 101 0.064815 =7/108
## Totals 119 106 0.053333 =12/225
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.954975 0.943925 0
## 2 max f2 0.954975 0.938662 0
## 3 max f0point5 0.954975 0.949248 0
## 4 max accuracy 0.954975 0.946667 0
## 5 max precision 0.954975 0.952830 0
## 6 max absolute_MCC 0.954975 0.893228 0
## 7 max min_per_class_accuracy 0.954975 0.935185 0
##
##
## Scoring History:
## timestamp duration training_speed epochs
## 1 2016-01-19 12:06:04 0.000 sec 0.00000
## 2 2016-01-19 12:06:05 0.501 sec 4591 rows/sec 10.00000
## 3 2016-01-19 12:06:10 5.520 sec 5729 rows/sec 140.00000
## 4 2016-01-19 12:06:15 10.619 sec 5525 rows/sec 260.00000
## 5 2016-01-19 12:06:20 15.787 sec 5573 rows/sec 390.00000
## 6 2016-01-19 12:06:25 20.930 sec 5711 rows/sec 530.00000
## 7 2016-01-19 12:06:30 26.149 sec 5778 rows/sec 670.00000
## 8 2016-01-19 12:06:36 31.322 sec 5832 rows/sec 810.00000
## 9 2016-01-19 12:06:41 36.323 sec 5960 rows/sec 960.00000
## 10 2016-01-19 12:06:46 41.620 sec 6068 rows/sec 1120.00000
## 11 2016-01-19 12:06:51 46.756 sec 6125 rows/sec 1270.00000
## 12 2016-01-19 12:06:56 51.977 sec 6204 rows/sec 1430.00000
## 13 2016-01-19 12:07:01 56.999 sec 6290 rows/sec 1590.00000
## 14 2016-01-19 12:07:07 1 min 2.246 sec 6340 rows/sec 1750.00000
## 15 2016-01-19 12:07:12 1 min 7.362 sec 6394 rows/sec 1910.00000
## 16 2016-01-19 12:07:12 1 min 7.385 sec 6394 rows/sec 1910.00000
## iterations samples training_MSE training_r2 training_logloss
## 1 0 0.000000
## 2 1 2250.000000 0.05214 0.79110 0.30365
## 3 14 31500.000000 0.05286 0.78821 0.30866
## 4 26 58500.000000 0.05221 0.79083 0.24884
## 5 39 87750.000000 0.05199 0.79170 0.23988
## 6 53 119250.000000 0.05111 0.79523 0.21719
## 7 67 150750.000000 0.05099 0.79572 0.21526
## 8 81 182250.000000 0.05079 0.79651 0.21207
## 9 96 216000.000000 0.05328 0.78653 0.21779
## 10 112 252000.000000 0.05454 0.78148 0.22186
## 11 127 285750.000000 0.05475 0.78064 0.22467
## 12 143 321750.000000 0.05460 0.78123 0.22262
## 13 159 357750.000000 0.05459 0.78129 0.22241
## 14 175 393750.000000 0.05457 0.78135 0.22219
## 15 191 429750.000000 0.05462 0.78119 0.22279
## 16 191 429750.000000 0.05079 0.79651 0.21207
## training_AUC training_classification_error
## 1
## 2 0.98374 0.05333
## 3 0.98334 0.05333
## 4 0.97353 0.05333
## 5 0.97353 0.05333
## 6 0.97353 0.05333
## 7 0.97353 0.05333
## 8 0.91876 0.05333
## 9 0.96569 0.05333
## 10 0.96569 0.05333
## 11 0.96569 0.05333
## 12 0.96170 0.05778
## 13 0.94195 0.05778
## 14 0.94195 0.05778
## 15 0.94195 0.05778
## 16 0.91876 0.05333perf<-h2o.performance(Deeplearningmodelmeta)
perf@metrics$r2## [1] 0.7965051perf@metrics$AUC## [1] 0.9187638perf@metrics$max_criteria_and_metric_scores$value[4]## [1] 0.9466667