Anybody can be a quantitative trader!

In part 5 of my blog, I talk about a crowd sourced online quantitative investment firm called Quantopian. The website provides a platform to write investment algorithms in the Python language by providing minute by minute stock data and a development platform for both backtesting and implementation. It then uses an API with the brokerage firm Interactive Brokers to help traders on the website receive real-time quotes and implement their trading algorithms. The best part is that the website is mostly free to use.

A basic quant algorithm essentially has the following structure. A data structure used to store data passed to it (data such as stock price information), a function written that performs technical analysis (our trading ideas) on the stock data, and an output that shows what stocks the analysis says we should trade. Since the development platform can be hooked up to an API, it can also automatically be told to make certain trades that the function has told us to make. These systems are used on a wide variety of information ranging from basic stock prices, earnings, sentiment indexes, dividend announcements and even regulatory filings.


Above is a sample of a simple trading algorithm. As shown, there are many more variables to keep in mind when creating an algorithm. These things include dynamically selecting stocks based on information that is continually being passed to it through the API or creating multi-factor models that assess stocks on a wide variety of variables as I talked about in my last post.

The world of investing is becoming more and more quantitatively driven every day and as information is becoming more widely available with the advent of the internet, speed is as valuable as ever. By creating a stock trading algorithm, it allows investors to analyse a huge database of stocks based on certain criteria in seconds instead of days.

Writing & Image References:

https://www.quantopian.com/home