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R Machine Learning simple example
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R Machine Learning simple example

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R Machine Learning simple example

Courtesy:

http://inovancetech.com/blogML.html
http://inovancetech.com/blogML2.html


Quoting 
Step-By-Step Example in R

Now we will go through a very simple example in R. We will use the day of the week to predict whether todayís price of Apple stock will close up or down.
First, letís make sure we have all the libraries we need installed and loaded.

install.packages("quantmod")
library("quantmod")
#Allows us to import the data we need
install.packages("lubridate")
library("lubridate")
#Makes it easier to work with the dates
install.packages("e1071")
library("e1071")
#Gives us access to the NaÔve Bayes classifier

Next, letís get all the data we will need.
startDate = as.Date("2012-01-01")
# The beginning of the date range we want to look at
endDate = as.Date("2014-01-01")
# The end of the date range we want to look at
getSymbols("AAPL", src = "yahoo", from = startDate, to = endDate)
# Retrieving Appleís daily OHLCV from Yahoo Finance
Now that we have all the data we will need, letís get our indicator, the day of the week.
DayofWeek<-wday(AAPL, label=TRUE)
#Find the day of the week

And what we are trying to predict, whether the dayís price will close up or down, and create the final data set.
PriceChange<- Cl(AAPL) - Op(AAPL)
#Find the difference between the close price and open price
Class<-ifelse(PriceChange>0,"UP","DOWN")
#Convert to a binary classification. (In our data set, there are no bars with an exactly 0 price change so, for simplicity sake, we will not address bars that had the same open and close price.)
DataSet<-data.frame(DayofWeek,Class)
#Create our data set

Finally, we are ready to use the NaÔve Bayes classifier.
MyModel<-NaÔveBayes(DataSet[,1],DataSet[,2])
#The input, or independent variable (DataSet,1]), and what we are trying to predict, the dependent variable (DataSet[,2]).

Congratulations! We have now used a machine-learning algorithm to analyze Apple stock. Now, letís dive into the results.

NaÔve Bayes Output from R

This shows us the probability of a price increase or decrease over our entire data set (known as the prior probabilities). We can see there is a slight bearish bias, but not much.


NaÔve Bayes Output from R
This shows the conditional probabilities (Given that it is a certain day of the week, what is the probability that the price will close up or down.) These are much lower than 50% due the fact they are scaled down by the prior probabilities of the outcomes (probability of price increase over the entire data set).

We are able to see that this this not a very good model, i.e. it does not return very high probabilities. However, we can see that you are generally better off going long in the beginning of the week and short towards the end of the week.

Improving the Model

Obviously you are going to want a slightly more sophisticated strategy than just looking at the day of the week. Letís now add a moving average cross to our model. (You can get some more information on adding other indicators, or features, to your model here.)
I prefer using exponential moving averages, so letís look at a 5-period and 10-period exponential moving average (EMA) cross.

First, we need to calculate the EMAs:

EMA5<-EMA(Op(AAPL),n = 5)
#We are calculating a 5-period EMA off the open price
EMA10<-EMA(Op(AAPL),n = 10)
#Then the 10-period EMA, also off the open price
Then calculate the cross
EMACross <- EMA5 - EMA10
#Positive values correspond to the 5-period EMA being above the 10-period EMA

And rounding the values to 2 decimal places. This is important because if there is an instance that the NaÔve Bayes has never seen, it will automatically calculate the probability at 0%. For example, if we were looking at the EMA cross to 6 decimal places and it found a very high probability of a downward price movement when the difference was $2.349181 and then was presented with a new data point that had the difference as $2.349182, it would calculate a 0% probability leading to a price increase or decrease. By rounding to 2 decimal places, we greatly mitigate this risk as a large enough dataset it should have seen most values of the indicator. This is an important limitation to remember when building your own models.

EMACross<-round(EMACross,2)
Letís create a new dataset and split it into a training and test set so we are able to see how well our model does over new data
DataSet2<-data.frame(DayofWeek,EMACross, Class)
DataSet2<-DataSet2[-c(1:10),]
#We need to remove the instances where the 10-period moving average is still being calculated
TrainingSet<-DataSet2[1:328,]
#We will use ⅔ of the data to train the model
TestSet<-DataSet2[329:492,]
#And ⅓ to test it on unseen data

Now to build the model:
EMACrossModel<-naiveBayes(TrainingSet[,1:2],TrainingSet[,3])
NaÔve Bayes Output from R
The Conditional Probability of the EMA Cross, a numeric variable, shows the mean value for each case ([,1]), and the standard deviation ([,2]). We can see that the mean difference between the 5-period EMA and 10-period EMA for long and short trades was $0.54 and -$0.24, respectively.
And test it over new data:

table(predict(EMACrossModel,TestSet),TestSet[,3],dnn=list('predicted','actual'))
NaÔve Bayes Output from R
We can see overall it got 79 out of 164, or 48%, correct. It had a fairly large downward bias, predicting 95, or 58%, cases as ďDOWNĒ.
While these are not great results, this should give you all the information you need to build your own machine-learning based strategy.

In the next part of our series, we will go over how you can actually use this model to improve your own trading.

Mike

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