About Quantitative Automated Trading Systems.
Quantitative Automated trading is a form of trading that uses quantitative/financial models or a quantitative trading infrastructure. It is usually undertaken by a quantitative automated trading developer (quant dev), at a private fund, investment bank or other asset manager.
Quantitative automated trading is split into two parts: the development, implementation, and optimisation of a mathematical model, and the obtaining, refining and storing of financial pricing data feeds.
Implementing mathematical models requires a quant dev who is good at math, and has experience in a programming language such as C++, MATLAB, Python or R. Most firms will have a number of mathematical models — known as a library — to help them undertake quantitative trading. Quant devs will test and refine each model, as well as develop new ones alongside quantitative researchers. They’ll also help other traders understand and access the models by developing different interfaces.
Obtaining and refining financial pricing data feeds, essential for successful quantitative trading, is also undertaken by quant devs. They build systems to collect pricing feed such as equities, forex and fixed income along with other relevant data such as macroeconomic and internal/external trading signals. This data is then stored in a database until traders need to access it. These databases will include relational database management systems (RDBMS) such as SQL Server or Oracle, or proprietary time-series databases such as KDB+. Whatever storage method used, there will need to be access to the data from multiple stakeholders, in a reliable and timely way.
Are You Prepared To Create Your Own Quantitative Automated Trading Systems?
Quantitative trading requires quantitative developers who have worked with large data sets, and know how to perform rapid data analysis. It also requires researchers who can develop accurate models based on this data. For someone with strong computational skills, this field can be extremely lucrative and rewarding.
Focusing on highly liquid investments like the futures market is important. Our futures trading system which we offer to select clients performs exceptionally well.
To prepare for a career in quantitative trading, you should begin by working on large data sets. This could be an open source project, a small consulting gig, or a piece of hobby code. The key is to demonstrate your statistical, computational and software development skills in a real-world environment. Try and implement a few trading strategies in multiple languages, both to develop your skills and to get a nuanced understanding of each language’s pluses and minuses.
You will require a sophisticated computational skillset, covering database optimisation, systems administration, API design, as well as your core languages — C++, Python etc.