Algorithmic Trading Model with Dual Moving Average Crossover Using Python Take 1

Code Credit: Adapted from code samples used in O’Reilly Media’s Learning Path: Hands-On Algorithmic Trading with Python by Deepak Kanungo.

SUMMARY: The purpose of this project is to construct an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This algorithmic trading model uses the 20-day and 50-day moving averages to generate trading signals. We apply the analysis on the MSFT stock for the three years of 2017-01-01 thru 2019-12-31.

In this Take1 iteration, we will construct the code modules to cover the tasks of downloading the daily price information for a stock symbol. We will use the stock data acquired to fit a trading model in future iterations of the project.

ANALYSIS: Not available yet. To be developed further.

CONCLUSION: Not available yet. To be developed further.

Dataset ML Model: Time series forecast 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.