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: CNBC’s Jim Cramer, the host of Mad Money show, presented a list of stocks on April 27 that he believes will work well in this coronavirus-plagued market. The ‘Cramer COVID-19 Index’ contains 100 companies that touch 17 sectors where investors can expect a positive return in this volatile market environment. The project aims to analyze these 100 stocks and develop strategies for trading these stocks, either individually or in groups.
In iteration Take1, we constructed the necessary code segments for downloading and visualizing the index and the stocks. The script leveraged various data sources, both free and paid subscriptions, for pulling the required dataset together.
In iteration Take2, we built on the previous analysis by constructing a dual moving average crossover trading model (20-day and 50-day) and applying the model to the 100 stocks in the index. For each stock, we also identified the entry and exit dates starting January 1, 2019.
In this Take3 iteration, we will build on the previous analysis by constructing a module to calculate profit and loss for the dual moving average crossover trading model and applying the model to the 100 stocks in the index. For each stock, we will identify the entry and exit dates starting around January 1, 2019.
NOTE: This script calculates the index value by using the number of outstanding shares from each company. Such an approach may not match how CNBC calculates this index (https://www.cnbc.com/cramer-covid-19-stocks/). This script is for educational purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.
ANALYSIS: Refer to the list of profit and loss amounts for each stock in the index under Task 4.
CONCLUSION: Refer to the list of profit and loss amounts for each stock in the index under Task 4.
Dataset ML Model: Time series analysis with numerical attributes
Dataset Used: IEX Cloud and Quandl
The HTML formatted report can be found here on GitHub.