Machine Learning Algorithms for Trading (Part 2)

Assess various Learners

There are two parts:

1) Implement decision tree learner, random tree learner, bag learner and insane learner

• DTLearner.py: The random tree learner is based on J.R. Quinlan's paper. Other than addEvidence and query, this learner also has:

  • __build_tree: A private function called by addEvidence. It builds the decision tree recursively by choosing the best feature to split on and the splitting value. The best feature has the highest absolute correlation with dataY. If all features have the same absolute correlation, choose the first feature. The splitting value is the median of the data according to the best feature.
  • __tree_search(self, point, row): A private function called by query. It recursively searches the decision tree matrix and returns a predicted value for a given query.
  • get_learner_info: It print out a tree in the form of a pandas dataframe if verbose is set to True.

• RTLearner.py: The random tree learner is based on A. Cutler's algorithm. It has a similar API to DTLearner, but has a few key differences regarding __build_tree:

  • The choice of feature to split on is be made randomly.
  • For the chosen feature, calculate the mean of feature values from two randomly-chosen rows. This mean will be the splitting value for the feature.

• BagLearner.py: Implement Bootstrap Aggregating as a Python class named BagLearner. BagLearner can accept any learner (e.g., RTLearner, LinRegLearner, etc.) as input and use it to generate a learner ensemble.

• InsaneLearner.py: InsaneLearner should contain 20 BagLearner instances where each instance is composed of 20 instances of LinRegLearner or another learner.

2) Evaluate learners

• analyze_learners_util.py: contains the helper functions to proccess, train, test and plot data.

• analyze_learners.ipynb: uses helper functions from analyze_learners_util.py to evaluate different learners.

Setup

You need Python 2.7.x or 3.x, and the following packages: pandas, numpy, and scipy.

Run

To run any script file, use:

python <script.py>

To run any IPython Notebook, use:

jupyter notebook <notebook_name.ipynb>

Source: Part 3 of Machine Learning for Trading by Georgia Tech