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 uses the 20-day and 50-day exponential moving averages to generate trading signals. 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. We apply the analysis on the GOOG stock for the three years of 2017-01-01 thru 2019-12-31.
In iteration Take1, we constructed and tested a trading strategy using the daily stock data from Yahoo! Finance with an initial investment pool of 1,500 USD.
In this Take2 iteration, we will construct and test a trading strategy using the daily stock data from Alpha Vantage with an initial investment pool of 1,500 USD. We also will leverage the technical indicators available from Alpha Vantage.
ANALYSIS: In iteration Take1, the trading strategy returned 27.94%. In the meantime, the long-only approach achieved an accumulated return of 37.21%.
In this Take2 iteration, the trading strategy returned 27.94%. In the meantime, the long-only approach achieved an accumulated return of 37.21%.
CONCLUSION: For this period, the trading strategy did not outperform the more straightforward long-only approach. We should consider more modeling with different methods for this stock. However, we were able to successfully use the data sources from Alpha Vantage to perform our modeling.
Dataset ML Model: Time series analysis with numerical attributes
Dataset Used: Alpha Vantage
The HTML formatted report can be found here on GitHub.