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 simple 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 iteration Take2, we constructed and tested a trading strategy using the daily stock data from Alpha Vantage with an initial investment pool of 1,500 USD. We also leveraged the technical indicators available from Alpha Vantage.

In iteration Take3, we constructed and tested a trading strategy using the daily stock data from IEX Cloud with an initial investment pool of 1,500 USD. We also leveraged the technical indicators available from IEX Cloud.

In this Take4 iteration, we will construct and test a trading strategy using the daily stock data from Quandl with an initial investment pool of 1,500 USD.

ANALYSIS: In iteration Take1, the trading strategy returned 32.79%. In the meantime, the long-only approach achieved an accumulated return of 37.21%.

In iteration Take2, the trading strategy returned 32.79%. In the meantime, the long-only approach achieved an accumulated return of 37.21%.

In iteration Take3, the trading strategy returned 32.79%. In the meantime, the long-only approach achieved an accumulated return of 37.21%.

In this Take4 iteration, the trading strategy returned 32.79%. 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 Quandl to perform our modeling.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Quandl

The HTML formatted report can be found here on GitHub.