Skills Needed to become a Quantitative Trader
How do you get from being a Data Scientist, Software Engineer, or Markets Enthusiast to being a Quantitative Algo Developer? Algorithmic Trading requires both technical, and functional skills.
Over my career, I have seen many people try. Most fail. This post covers the Non-Technical and then the technical skills that are really required to become a quantitative trader.
Most want to know the technical skills needed to become a Quant. Wrong place to start. Any fool can have the technical skills. You need to first cultivate your own non-technical skills first.
Inquisitive Nature — Be a two year old and never stop asking “Why?”
The markets went up today. Why? It isn’t enough to say that there were more buyers than sellers (because everyone knows it is a 1:1 ratio). An aspiring quant needs to ask why and look to the data to find out. And when he or she finds out, then the next step is to ask Why again!
The scientific approach is to form a hypothesis and either prove or disprove it. Most people stop at the first answer they find. A good quant will believe in the research approach that a scientist takes and allow the process to lead to ever improving answers. In the world of quantitative trading this results in ever improving trading strategies and profits.
Tenacity — Don’t give up
Studying the data and using the scientific method can be boring. Trading is exciting. A good quantitative trader will not give up on the boring data and research because it leads to profits. Distractions by the excitement of trading leads to lost profit potential.
Overview of Core Technical Skills
Programming is the ability to express your trading ideas so that a computer can repeat the process. You need this skill to be able to code your algo. Structured backtesting is another use of your programming skills.
CloudQuant, where I am currently consulting, uses Python, a high level language that is easy for anyone to learn who has ever worked with any programming or macro language, like MS Excel VBA.
To test your algo you will need to test it against historical data. This is called “Backtesting.” Backtesting is more than checking to see if you made a profit or loss. It includes understand how and why you made a profit and loss and systematically improving that algo.
CloudQuant’s backtesting provides several reports that are full of statistics. Understanding what each of these statistics mean is essential to improving your algo. Having a base understanding of statistics is also important.
Management of Risk
Order processing and trading involves risk. CloudQuant meets regulatory required risk and their own functional requirements for pre-trade and post trade risk management within their production trading system. This is also incorporated into the backtesting market simulator. Understanding how risk works will help you improve your algorithm skills.