Decision Trees in Trading

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Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.

We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.

Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.

No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.

You can learn with or without coding knowledge. If you would like to do the analysis on excel, we would suggest you to start with course on Statistical Arbitrage in Trading. You can create and test your trading strategies using excel.

Alternatively, you can do the course on Python for Trading which will help you gain knowledge in all these fields: Python, Analysis and Financial markets.

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There are two main kinds of Decision Trees. One is the Regression tree. Regression Trees give continuous real value as output. For example, 1, 5 etc. This can be used when we want to predict the exact future price. The other is the Classification Tree. Classification Trees give a categorical value as output, for example, gender, nationality etc. It can be an unordered label of any kind. Classification Trees can be used when we want to predict the direction of market movement.
You can learn more about this in section 3 & 4 of the course.

Decision Trees are a tree-like structure that have two kinds of nodes. The internal nodes are the decision nodes. The outer nodes or leaf nodes give an Output. In the internal decision nodes, the dataset is split into more homogenous subsets using various criteria like Gini index. Broadly, Gini index takes a set of data points and a feature and calculates a numerical score for the feature. This score tells how homogenous the split subsets are in terms of the Output after splitting based on the values of the feature.

For example, imagine a dataset with where each point has two feature columns: Gender (male and female) and Age (young and old). The Output is a boolean value (1 or 0) which, say, tells us if the person is religious.
Now, here is how we split the dataset. We take each feature column one by one and calculate the Gini index for each based on the Output labels. Say, we get more homogenous subsets when we split on age than on gender. This tells us that when we split the dataset such that one subset is all the young people and the other subset is all the old people, we are able to predict religiosity better. In our example, it turned out that the subset having the old aged people has many more religious people than in the subset with younger people. This splitting procedure repeats top-down to create the entire decision tree.

Decision trees are non-parametric models which are extremely easy to visualise and interpret. This helps in ranking the input attributes on their significance to predict the Output. For example, in a financial trading setting, this can help identify indicator inputs that are better predictors than others. This is an advantage. Another advantage of decision trees is that they do not get affected by outlier values. Reason being that points aren't weighted by parameters but discriminated based on decision boundaries. So, extreme distances do not matter. The biggest disadvantage of decision trees is that they often overfit. This leads to methods like Random Forests.

Decision trees can be made with the popular library sklearn. The tree submodule of sklearn can be used to create variants of trees. Common hyperparameters required are tree-depth, split-criterion etc.
In section 3 and 4, we explore how to create decision trees using sklearn on indicator inputs to predict the direction and value of futures respectively.