Check out my new ebook on quant trading where I teach you how to build profitable systematic trading strategies with Python tools.

Hi! My name is Mike and I'm the guy behind QuantStart.com. I used to work in a hedge fund as a quantitative trading developer in London.

Now I research, develop, backtest and implement my own intraday algorithmic trading strategies using C++ and Python.

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Welcome to your FREE Algorithmic Trading resource where you will learn how to develop profitable algorithmic trading strategies and gain a career in quantitative trading.

Latest Articles

Value at Risk (VaR) for Algorithmic Trading Risk Management - Part I

Estimating the risk of loss to an algorithmic trading strategy, or portfolio of strategies, is of extreme importance for long-term capital growth. Many techniques for risk management have been developed for use in institutional settings. One technique in particular, known as Value at Risk or VaR, will be the topic of this article. Read more...

A Day in the Life of a Quantitative Developer

A lot of you have emailed recently asking what it is actually like to work in a quant fund. I've written before about my experiences as a quant dev but I thought I'd outline a normal day so you can get a feel for whether you would enjoy the role. Read more...

How To Get A Quant Job Once You Have A PhD

In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD. There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-paying financial role. While this may have been true 10-15 years ago, the reality of the current job market is such that quant roles are now highly competitive and candidates need to stand out if they are to get the best jobs. Read more...

Top 5 Essential Books for Python Machine Learning

We've discussed the importance of statistical modelling and machine learning in various articles on QuantStart. Machine learning is particularly important if one is interested in becoming a quantitative trading researcher. In this article I want to highlight some books that discuss machine learning from a programmatic perspective, rather than a mathematical one. This route is more appropriate for the quantitative developer or traditional software developer who wishes to eventually break into quantitative trading. Read more...

Money Management via the Kelly Criterion

Risk and money management are absolutely critical topics in quantitative trading. We have yet to explore these concepts in any reasonable amount of detail beyond stating the different sources of risk that might affect strategy performance. In this article we will be considering a quantitative means of managing account equity in order to maximise long-term account growth and limiting downside risk. Read more...