The History of QuantStart

The portal soon expanded into providing extensive end-to-end tutorials on systematic trading research and implementation to the retail quant community, who were eager to make use of the 'democratisation' of both financial data and the associated analytical tools.

In 2014 we released our first beginner guide to systematic trading—Successful Algorithmic Trading—and followed it up with Advanced Algorithm Trading in 2015. Both books have been extremely popular and have introduced many prospective quant traders to the world of systematic trading.

In 2015 we released the first version of QSTrader, our popular open source event-driven backtesting software written in Python, now with over 1000+ Github stars.

In 2017 we launched the Quantcademy—a quantitative finance and systematic trading community of like-minded prospective quants and retail systematic traders.

In 2019 we overhauled and refreshed our extensive article database and began providing in-depth research notes and commentary to the quant finance community. In addition we carried out a major refactor of the QSTrader backtesting software to incorporate robust portfolio and risk management functionality.

Throughout 2020 we will be continuing to publish tutorials on brand new systematic trading ideas, based on the modern tools of econometrics, machine learning and optimisation. In addition we will be outlining the latest career tips and guidance for passing the technical interviews at prestigious quant firms.

What We Do

Tutorials: We provide freely available tutorials on systematic trading, quantitative finance, mathematics, statistics and machine learning. Our tutorials often contain end-to-end code for all of the theory presented, allowing you to gain familiarity with implementing the ideas we discuss. To date we have written over 200 tutorials and continue to discuss the latest topics and techniques. You can explore our comprehensive article database here.

Education: We offer ebook products for beginning and advanced retail quant practitioners, designed to provide a complete roadmap to developing quantitative trading strategies. We have developed the Quantcademy as an additional learning resource, for those who want to take their systematic trading and quantitative finance learning even further.

Development: We regularly develop our core backtesting framework QSTrader to add more benefits, improve current features and maintain the codebase. We work with the community to foster suggestions and critique, as well as aid those who wish to contribute to the project.

Outreach: QuantStart team members regularly speak at key quantitative finance, systematic trading and data science conferences. Notable appearances include QuantCon New York and Singapore, The New York Trading Show, OpenDataScienceLondon and The Quant Conference.

Our Principles

In our view the scientific approach to quantitative research is not only the most appropriate way to approach algo trading, but it is also the mindset applied in nearly all institutional quantitative hedge funds. We teach systematic trading through this lens.

We emphasise a rigourous backtesting methodology, utilising robust approaches to portfolio construction and risk management. We discuss and exploit market anomalies backed by comprehensive academic research and avoid the arbitrary use of 'indicators', 'technical analysis' and 'chart patterns', which so often lead to poor and inconsistent results.

Reproducibility of research is extremely important to us. All of our published research is implemented in our fully open source backtesting framework—QSTrader—with full code, parameters, assumptions and data specifications outlined. It should be straightforward to replicate our results.

Where possible, we try to publish our results net of simulated transaction costs in order to provide a realistic assessment of a strategy's performance. All too often promising quant trading strategies fail to live up to their initial backtests and poor estimation of transaction costs is a common cause.

Contact Us

You can contact us at support@quantstart.com.