Module_14_Challenge

Creating a Machine Learning Trading Bot: The goal of of this project is to enhance existing trading signals with machine learning algorithms that can adapt to new data.

Technologies

We used the following libraries in our notebook:

• Pandas

• NumPy

• hvPlot

• Matplotlib

• scikit-learn

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Usage

The data used to create the models and evaluations is located in the 'Resources' folder of this repository.

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Evalutation Report

Baseline SVM

• This is the model we used as a baseline to compare and evaluate changes we made to try to improve accuracy.

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First Tune:

What impact resulted from increasing or decreasing the training window?

• You can see that by incerasing the trainging window we increased the accruracy of the model.

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Second Tune:

What impact resulted from increasing or decreasing either or both of the SMA windows?

• The accuracy of the model went down when the short window SMA was increased to 30 days from 4 days

Using a new classifier: LogisticRegression

Did this new model perform better or worse than the provided baseline model?

• This model performed worse than the baseline model.

Did this new model perform better or worse than your tuned trading algorithm?

• This model also performed worse than the tuned trading algorithm.

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Summary:

• The first tune we performed, increasing the training window on the SVM model, gave us the highest accuracy.

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Contributors

G. Cale McDowell

@gcm107

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License