Machine Learning Stocks

Abstract

1. Introduction

MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions. My hope is that this project will help you understand the overall workflow of using machine learning to predict stock movements and also appreciate some of its subtleties. And of course, after following this guide and playing around with the project, you should definitely make your own improvements – if you're struggling to think of what to do, at the end of this readme I've included a long list of possiblilities: take your pick.

This is a very generalised overview, but in principle this is all you need to build a fundamentals-based ML stock predictor.

Concretely, we will be cleaning and preparing a dataset of historical stock prices and fundamentals using pandas, after which we will apply a scikit-learn classifier to discover the relationship between stock fundamentals (e.g PE ratio, debt/equity, float, etc) and the subsequent annual price change (compared with the an index). We then conduct a simple backtest, before generating predictions on current data.

2. Objective

Using python and scikit-learn to make stock predictions

This project demonstrates how Python, Machine Learning, scikit-learn can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/robertmartin8/MachineLearningStocks.git
cd MachineLearningStocks

cd Users/User/Desktop/MachineLearningStocks

pip install -r requirements.txt
python download_historical_prices.py
python parsing_keystats.py
python backtesting.py
python current_data.py
pytest -v
python stock_prediction.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by robertmartin8 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.