Algorithmic Trading Model for Simple Moving Average Crossover Grid Search Batch Mode Using Colab

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: The purpose of this project is 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 series of simple moving average (MA) models via a grid search methodology. When the fast moving-average curve crosses above the slow moving-average curve, the strategy goes long (buys) on the stock. When the opposite occurs, we will exit the position.

The grid search methodology will search through all combinations between the two MA curves. The faster MA curve can range from 5 days to 20 days, while the slower MA can range from 10 days to 50 days. Both curves use a 5-day increment.

ANALYSIS: This is the Google Colab version of the iPython notebook posted on June 16, 2020. The script will save all output for each stock into a text file and on a Google Drive path. The Colab script contains an example of processing 100 different stocks in one batch.

CONCLUSION: Please refer to the individual output file for each stock.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.