After writing my recent post “Scaling a Simple Earnings Strategy to the NASDAQ Exchange” I started to research how I could implement that earnings strategy into a live brokerage account directly from R. Most of us have heard of Quantopian or other backtesting services, but things can get complicated quickly once you search for a way to implement a live strategy using R. I wanted to find an easy-to-use package or brokerage account that could allow me to implement live trades using only R. From my experience, R is a great tool to use for backtesting with packages like quantstrat offering a fast and powerful backtesting platform, but fails for live trade implementation. I think this might be due to a low number of exchanges that offer real-time and easy-to-use free API connection. I almost came to the conclusion that I would need to use other languages other than R to find a easy way to implement my earnings strategy until I found an online brokerage called Alpaca, creating AlpacaforR.
You often hear two things when watching or reading financial news. One is earnings season, and another is quantitative trading. Most people prefer to manage their own money and pick the companies their most familiar with when making trading decisions. I’m going to integrate the two to talk about the performance of a simple earnings trade strategy across the NASDAQ and how it compares against a basket of popular stocks.