SUMMARY: The purpose of this project is to construct and test an algorithmic trading model and document the end-to-end steps using a template.
Additional Notes: This is an adaptation of the momentum trading strategy from Chapter 2 of Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh with Packt Publishing.
INTRODUCTION: This algorithmic trading model uses the support and resistance levels to generate trading signals for a mean-reversion trading strategy. The strategy is to create a buy order when the stock price stays in the support tolerance margin after a waiting period of two days. Conversely, the model generates a signal for a sell order when the stock price remains in the resistance tolerance margin after the same waiting period.
We apply the analysis on a stock for a fixed period and compare its return/loss to a simple long-only model. The long-only model will purchase the stock at the opening of day one and hold the stock through the entire time.
In this Take1 iteration, we will construct and test a mean-reversion trading model for the stock “GOOG” during the three years between 2017 and 2019 with an investment pool of 1,500 USD.
ANALYSIS: In this Take1 iteration, the mean-reversion trading strategy with a waiting period of two days returned 3.65%.
CONCLUSION: For this period, the mean-reversion trading strategy did not outperform the more straightforward long-only approach, so we should consider modeling more and different methods for this stock.
Dataset ML Model: Time series analysis with numerical attributes
Dataset Used: Various sources as illustrated below.
Dataset Reference: Various sources as documented below.
The HTML formatted report can be found here on GitHub.