By Sushant Ratnaparkhi & Milind Paradkar

Machine Learning is the new buzz word today and some of the tech companies are doing wonderful unimaginable things with it. Today, we’re going to show you, how you can Predict Stock Movements (that’s either up or down) with the help of ‘Decision Trees’, one of the most commonly used ML algorithms.

Decision trees are used for building classification and regression models to be used in data mining and trading. A decision tree algorithm performs a set of recursive actions before it arrives at the end result and when you plot these actions on a screen, the visual looks like a big tree, hence the name ‘Decision Tree’.

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Here’s an example of a simple decision tree.

Basically a decision tree is a flowchart to make you help decisions. Machine Learning uses the same technique to make better decisions, let’s find out how.

Visualizing a sample dataset and decision tree structure

Now let’s come to the point, we want to predict which way your stock will go using decision trees. We’ll need past data of the stock for that. Consider a sample stock dataset as shown in the table below. The dataset comprises of Open, High, Low, Close Prices and Volume indicators (OHLCV). You can download historical data of any stock using Yahoo finance or Google finance.

We will be doing this exercise in R programming language, you’ll have to install the supporting software on your mac/pc. Here is a link to help you with that. And here’s the code for importing this data onto –

And here’s visual representation of the data.

Let us add some technical indicators (RSI, SMA, LMA, ADX) to this dataset. Technical indicators are calculated using basic stock values (OHLC) in our case and they help us predict stock movements. Our machine learning algorithm will make use of the values from the technical indicators to make more accurate predictions. We lag the technical indicator values to avoid look-ahead bias.

We also add the “Class” column which signifies the daily returns based on the close price of the stock. In the “Class” column, “Up” denotes positive return, whereas “Down” signifies a negative return. Here’s the code for the same

Now comes the fun part, we want to predict the daily change (Up/Down) using these technical indicators. We  will do this by applying a machine learning decision tree algorithm on this dataset. First, we will have to split our dataset into two parts; training dataset and test dataset. The algorithm uses the training data to learn about the stock’s movements and it makes certain assumptions, this is also called as ‘information gain’. And once the training is done  we apply it on a test dataset to make the predictions. This type of Machine Learning is called as supervised learning. Here’s the code for applying these steps

The resulting output from the training dataset can be viewed in the form of a tree structure as shown below.

Sample illustration of a Decision Tree

Before we understand how a decision tree algorithm works, let us first understand the tree structure.

Components of a decision tree

A decision tree structure consists of a root node, test nodes, and decision nodes (leaves). The root node is the main node in a decision tree. In the above decision tree, we start with RSI>50, thus the RSI indicator is used as the root node. You might ask why RSI indicator’s value is at 50 and why not say 34? Here’s where the training comes into picture, the algorithm was able to understand that RSI at 50 helps in making more accurate decisions than at any other value.

After the root node, each test node splits the data into further parts according to some set criteria. In the decision tree above, we have SMA> 80, LMA>55, and ADX>25 (again these values are result of the training). The data is split further based on these indicators which act as test nodes.

The final node is the leaf node. In the decision tree diagram, the nodes containing the Up/Down class along with the probability value indicating the probability of the target attribute are the leaf nodes. The left of any node is considered as ‘yes’, meaning the question asked at the node (e.g. RSI >50 if answered yes, the algorithm navigates to left side of the tree and if not then right)

Thus, the entire dataset gets classified by navigating from the root node of the tree decision tree down to the leaf, according to the set criteria.

Components of a Decision Tree

So for example, this stock on a given day had its RSI at 39 then, so travel to right of the tree check if the SMA is > 80, if yes then travel to the leaf which says, ‘the stock has a 0.52 probability of going down’.

How does a decision tree algorithm work?

Now that we have understood the decision tree structure, let us understand how a decision tree is constructed.

Decision Tree Inducer

A decision tree is constructed by a decision tree inducer (also called as classifier). There are various decision tree inducers such as ID3, C4.5, CART, CHAID, QUEST, CRUISE, etc. etc. (you can find more information on these inducers here and here) A decision tree inducer is basically an algorithm that automatically constructs a decision tree from a given (training) dataset. Typically, the goal of a decision tree inducer is to construct an optimal decision tree based on a specified target function. An example of a target function can be a minimization of the number of nodes of the decision tree, so as to reduce the complexity. Another example can be minimizing the generalization error (or get more accurate results).

Although, we said that the inducer constructs a decision tree automatically, in reality, the decision tree inducer follows a certain method. There are two prevalent methods used by the decision tree inducers in general. These include the widely used top-down method and the less popular, bottom-up method.

Top-down Method Explained

Under the top-down method, an inducer creates the decision tree in a top-down, recursive manner. Let us use our previously created technical indicators to understand this top-down, recursive method.

The technical indicators that we created were RSI, SMA, LMA, and the ADX indicator. These indicators are considered as the attributes by our decision tree algorithm.

From among these indicators (attributes), which of the indicator (attribute) will be used by the inducer as the root node to start with? How will the remaining attributes be split?

All this is decided by the inducer using a discrete splitting function. The discrete splitting function uses certain criteria (e.g. information gain, gini index,) to determine the best attribute to start with and split the training dataset. Here are some links to understand more about Information gain, Gini index.

In each iteration, the inducer algorithm partitions the training dataset using the outcome of a discrete function of the input attribute. After the selection of an appropriate split, each node further subdivides the training set into smaller subsets, until no further splitting is possible or when a stopping criterion is fulfilled. After the complete creation of the tree, it is pruned using certain pruning rules to reduce classification errors.

This is how a decision tree gets constructed which can be used for making predictions in machine learning. In our future posts, we will demonstrate how to construct a decision tree in python and will also explore some machine learning models based on decision trees.

Next Step

If you want to learn various aspects of Algorithmic trading then check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!

Or you can sign up for our short course series on Machine Learning for Trading on Quantra. The ML for trading series covers Regression, Classification and SVM concepts along with their practical implementation in trading strategy with the help of sample strategy and ample exercises. Enroll now!

Download Data Files

• Decision Tree R Code Part1.R
• Decision Tree R Code Part2.R

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