Algorithmic Trading Model for Mean-Reversion vs. Trend-Following Strategy for a Group of Stocks Using Python

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model compares a simple mean-reversion and trend-following strategy for a group of stocks. The model will use a trend window size of ten days for long trades only.

ANALYSIS: In this modeling iteration, we analyzed ten stocks between January 1, 2016, and July 9, 2021. The models’ performance appeared at the end of the script.

CONCLUSION: For all the stocks during the modeling time frame, the long-only trading strategy with either mean-reversion or trend-following approach did not produce a better return than the buy-and-hold approach, except for LUV and PFE. We should consider modeling these stocks further by experimenting with more variations of the strategy.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.