Algorithmic Trading Model with Dual Moving Average Crossover Using Python Take 2

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 iteration Take1, we constructed the code modules to cover the tasks of downloading the daily price information for a stock symbol.

In this Take2 iteration, we will develop a trading model by constructing the coding segments to process the stock pricing data and generate trading signals. We will use the trading signals to “backtest” the trading model in another iteration of the modeling.

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.