Patrick, a retail trader with a passion to learn & execute on the go

2 min read

The popularity of Algorithmic trading is mostly because of the speed and the accuracy it provides over manual trading. Speed, as the algorithms are able to carry out trades in fractions of seconds, much faster than humans can perceive. When it comes to accuracy, you have the option to backtest and avoid any human errors.

We all somewhat know the importance of using machines to do our trading for us, one learner grew up with that.

Growing up and living in the heart of Silicon Valley has made it clear to me that computers and algorithms can provide a level of execution and proficiency in trading that a human can't maintain for long. The next logical step......I should make one my slave!

Meet Patrick:

Tell us something about yourself: Your current work profile, your educational background. What motivated you to learn quantitative trading?

Hi, I am Patrick and I am a retail trader with a BS degree in Applied Economics. I've been a designer of medical devices for the past 20 years but have always had an interest in the markets.

I had been trading short volatility using options for the last few years until the late 2018 bear market. That was when I realized I was a one-trick pony and began to branch out a year ago to develop a new and different systematic approach. I have since realized my initial short vol system was flawed but could be tuned with some rule refinement. I am now in the process of working that out.

Growing up and living in the heart of Silicon Valley has made it clear to me that computers and algorithms can provide a level of execution and proficiency in trading that a human can't maintain for long. The next logical step......I should make one my slave!

Tell us about your learning experience at Quantra.

I purchased Laurent Bernut's course on ‘Short Selling in Trading’ to create the Floor/Ceiling python code for the new system I am working on. The Short Selling in Trading course has been fantastic. I was interested in learning to use it as a direction/regime indicator. So far, in the initial testing, it seems to be working very well, however, I have yet to roll out a production system.

Any message you would like to share with aspiring Algo traders and Quant traders?

Find an edge, develop a systematic approach, know your Gain Expectancy, calculate everything in terms of R, run stats in terms of R and don't cheat, know your system's standard deviation of returns (especially for the losses), trust your stats and form a mindset that allows for outlier result within the realm of the systems normal operation. Markets are interesting because the unlikely and highly improbable continue to happen.


Be it making the shift from manual to automated trading, or starting your own High-Frequency Desk, with QuantInsti, the goal will not be far. Quantra courses are self-paced bite-sized courses, a quick way to gain the skills that are necessary to create your own trading strategies using Statistics, Econometrics & Python. With specially curated content, become an algorithmic trader within a matter of hours! Looking for classroom coaching and one-on-one interaction with Hedge fund founders and world-leading quants? Enquire more here.

Disclaimer: In order to assist individuals who are considering pursuing a career in algorithmic and quantitative trading, this case study has been collated based on the personal experiences of learners from QuantInsti®. Case studies are for illustrative purposes only and are not meant to be used for investment purposes. The results achieved post completion of any QuantInsti programme may not be uniform for all individuals.