“If you feel like there’s something out there that you’re supposed to be doing if you have a passion for it, then stop wishing and just do it.” - Wanda Sykes
When it comes to talking about passion for trading, we’d advise one name that epitomizes it - Priyanka. An aspiring Algo Trader, a trading practitioner, and a really passionate individual when it comes to learning about Algorithmic Trading.
A person of few words, but possessing innate talent, here’s her journey in a nutshell.
Here’s our conversation with Priyanka:
Hi Priyanka, tell us about yourself.
Hi! I’m Priyanka S., I have done my Engineering (ECE) from VTU Karnataka, India. I have been trading in the markets for the past 5 years.
I was always looking forward to learning about the markets, and I have always found entrepreneurship quite an interesting facet. Algorithmic Trading caught my eye when I first learnt about it, and have been fascinated with it ever since.
Why did you choose to be a Quant Trader?
As I said, I had always been interested in trading. I wanted to design trading strategies in an orderly manner according to a fixed system or plan. I was very ambitious towards developing trading strategies through rigorous research and mathematical computations.
I knew I could develop strategies which involved the application of scientific methods in selecting the securities, choosing and filtering the data. I was particularly interested in studying the data to trade those securities.
Although I was trading by myself, it seemed to me that I could be doing much better with the right guidance, and that’s when my search for learning about the domain of Algorithmic Trading and Quantitative Trading landed me at QuantInsti’s EPAT course.
What piece of advice would you like to share with the Algo Traders out there?
There are many technical factors (SMA Crossovers) which do not necessarily carry a whole lot of information about "the future" of stock. The chart shows historical prices, but historical prices are not an indicator of a future price.
My best advice would be to think of some unique/clever factors which may carry some information about some near-future direction of the stock (1 to 10 days). This can be done by extrapolating algos which were taught in QuantInsti. Also when testing your factor, test on multiple portfolios with an equally weighted number of stocks per portfolio.
Test, test and test.
Also remember that what performs well in the bull market, may not perform well in the bear market.
What message do you have for people who want to enter the field of Algorithmic Trading?
If you need to learn Algo Trading from some pretty big names in the industry, the exposure to markets, without compromising on quality, EPAT is the right course to begin with. The programme comprehensively covers all the aspects of the Stock Market, how to build strategies and the ways to implement such strategies efficiently. It includes various modules covering each and every aspect of algorithmic trading.
Overall, if anybody wants to build a career in Algo trading, they should surely go for this programme.
Thank you, Priyanka, for shedding light on how your journey has been and for helping us share it with the world. We agree that with the right set of skills, guidance and support, one can be a part of the consistently growing domain of Algorithmic Trading. The Executive Programme in Algorithmic Trading (EPAT) does just that.
Its comprehensive curriculum covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading and equips you to be a successful trader. Learn more about it 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 a student or alumni from QuantInsti’s EPAT® programme. Case studies are for illustrative purposes only and are not meant to be used for investment purposes. The results achieved post completion of the EPAT® programme may not be uniform for all individuals.