Algorithmic Trading Model for Mean-Reversion with Relative Strength Indicator Using Python Take 1

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 examines a simple mean-reversion strategy for a stock. The model enters a position when the price reaches either the upper or lower Relative Strength Indicator thresholds for the last X number of days. The model will exit the trade when the stock price crosses the upper or the lower RSI line for the same window size.

In this Take1 iteration, we will set up the models using an RSI window size for long trades only. The window size will vary from 10 to 50 trading days at a 5-day increment.

ANALYSIS: In this Take1 iteration, we analyzed the stock prices for Costco Wholesale (COST) between January 1, 2016, and April 23, 2021. The top trading model produced a profit of 143.51 dollars per share. The buy-and-hold approach yielded a gain of 211.45 dollars per share.

CONCLUSION: For the stock of COST during the modeling time frame, the simple long-only trading strategy did not produce a better return than the buy-and-hold approach. We should consider modeling this stock 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.