In algorithmic trading, we say don't let emotions get the best of you. EPATian Balamurugan G. is literally implementing that professionally and trying to achieve that in his personal life as well. It does make a difference when it comes to working and not only trading because all this discipline would eventually have a cascading effect on your life.
Bala has done his CFA, is an MBA and has done the EPAT course. He has grown as a trader rapidly and worked with multiple companies in various roles - a work journey that people just wish for.
He has worked with well-known firms like Bank of America, BNP Paribas, CRISIL and Verizon, to name a few, and today he is established in the domain of Quantitative Finance. We present to you, his success story.
Here's how our conversation went:
Tell us about yourself!
Hi, I am Balamurugan Ganesan. I am a Product Professional with around 9 years of experience as a Business Analyst / Product Manager with a proven track record in building products in Fintech and CRM domains. I also have 2 years of experience as a developer.
I am very passionate about this domain. I think it is more about the subject ie. algorithmic trading that I am really interested in learning, and that's why I got into this. I have a keen interest in Python, Trading and Machine Learning concepts.
I used to read a lot once I got into trading. I've read a lot of books like 'Reminiscences of a Stock Operator', 'Market Wizards', Alexander Elder's 'Trade for a living' and then psychology books like Mark Douglas' 'Trading in the zone'.
I have done my Engineering from NIT Calicut. After which I joined Verizon as a software developer working for a couple of years. After a few years of work experience, I took the CAT exam and got into SP Jain and pursued an MBA.
Since I was really good at mathematics, comfortable with Excel and had a coding background, I was confident of number crunching and learning something challenging. I thus opted for a specialization in Investment Banking which was considered to be the toughest branch. That is where I got interested in the stock market and found it really fascinating.
To improve my knowledge, I decided to pursue the most desired certification in this field – CFA (Chartered Financial Analyst) and successfully completed all 3 levels in 2014.
Once I did my MBA, I worked for different companies in different roles like Product Manager / Business Analyst where we built products for the Fintech industry. In BNP Paribas I was handling the Listed Derivatives product development in the APAC region.
In CRISIL, I was part of the product management team incubated within the organization in an effort to externalize CRISIL’s deep expertise in the Financial Research space. We worked with global research firms and research analysts as customers and launched products to solve their latent needs and pain areas. The product focused on improving the efficiency and productivity of a Research Analyst in building financial models, publishing research reports, news analysis and data analytics.
How has your journey of learning to become a professional trader been like?
In my first job, I worked as a software developer without any exposure to the stock markets. Today, I've been trading for around 7 years in equities, futures and options market.
Around 2012, I was doing a lot of discretionary trading where I buy/sell stocks based on news and what people were talking about. I also got started with options at the same time and lost a lot of money in that. I still remember selling a naked call option of Infosys a day before the earnings day.
Next day I was staring at a loss of more than 30k when the market opened post results. I had a bit of luck initially, made a few big losses later and stopped discretionary trading after some time.
In 2017 I again started trading, but this time I was doing it more systematically with proper position sizing, well-defined risk parameters and only using back-tested systems.
Initially, there were a few instances where I would want to improvise on the backtested systems whenever it made losses, wouldn't follow the rules and make some mistakes. Then once I understood the law of large numbers, I became more systemic and it helped me take the emotional part out of it.
When I came across the EPAT course, I thought algorithmic trading required a really versatile skill set that would match my profile perfectly. I've met some amazing faculties during EPAT. It helped me in gaining a solid understanding of various real-world implementation techniques in quantitative finance and high-frequency trading.
I also developed a deep learning neural network model for my EPAT project work to predict Bank Nifty intraday prices and have also automated a few of my intraday trading strategies. This really saves time and avoids a lot of screen time. Understand more about the applications of neural network in trading equip yourself with advanced skills.
So what do you trade in mostly?
Earlier I used to keep switching between different trading strategies and never had the conviction to stick to a particular style. After a lot of trial and error, I’ve finally found my edge and stuck to a few strategies.
I only look at the F&O stocks in NSE which have good volume.
- In one system I look for the most active stocks for the day and based on additional criteria, I take up some positions.
- On another system, I check for any volatility expansion based on the trading range for the past few days.
- I have a reversal prediction system where I take positions with options which helps in defining risks based on my views.
- I also have a system where I trade only on Thursdays during expiry with Bank Nifty options.
I tried commodities and currencies as well earlier, but it was pretty tough to actually make consistent money and I lost interest eventually. Now I am trading only on equities and options. I've identified my sweet spot and started sticking to that.
I also found it very difficult to scalp trades, where you get in and out in a couple of minutes or so. I get a bit anxious because in scalp you will actually have to increase the position size to make a good profit in less time. But that didn't work out for me. There is a lot of discretion involved in this type of trading and doesn't suit my profile
To overcome the psychological hurdles that we face in life as well as trading, I do meditation regularly to keep a calm mind. That has helped me a lot in trading and it has made me more consistent in following rules.
You have worked through quite many profiles. In the present role what are the things that you do and does it relate to trading?
Not at all actually. The reason that I have worked in so many roles is that I have simply kept on searching and going for what actually suits me.
I felt that somehow there was a gap and I was not feeling fulfilled. So once you get into a Business Analyst kind of profile you don't do any programming and it is more about the business understanding, talking to client stakeholders, ensuring that the requirements are gathered properly, the product is delivered on time and more of a client-facing role. I was not using my mathematical or programming skills.
In my current role, I work in the Financial Control team of Bank of America developing global regulatory products. Being one of the biggest financial institutions in the world, there are a lot of regulatory bodies that we interact with across different regions and should be compliant with.
Our team takes care of building and maintaining global applications for this purpose.
You have said that you like math and programming among other things. How exciting is it when you do it for trading?
Math and programming help a lot in systematic trading.
We can use statistical models to identify mean-reverting and trending stocks which helps in formulating your hypothesis for the trading strategy.
Programming helps in automating most of our manual tasks. Instead of going over the charts of 150 stocks manually which can take hours, we can just run a code snippet which automatically gives you the same result set in a few seconds.
With the advent of machine learning, we can even identify non-linear relationships in our dataset which isn’t very evident to a human eye. This is what I’m exploring currently. All these techniques come in handy for trading and save us a lot of time.
Every programmer has a language that they like. Which one is yours?
Mine is Python. It has been around two years since I have been using Python and it has been a pretty good experience.
- It has helped me a lot in automating many of my mundane tasks
- It is very easy to learn and has excellent support online
- It has a good set of libraries related to machine / deep learning and reduces your programming effort
- Most of the brokers provide Python API to automate your trading
So with the current support that you have I find Python to be pretty helpful.
What are your thoughts on CFA and EPAT?
If you have practical expertise in trading, want to automate your strategies with positive expectancy and have some basic knowledge in Python then it is better to go with EPAT. Another advantage of EPAT is that you can finish the course in 6 months and understand most of the practical nuances in algorithmic trading.
CFA will be a better choice if you want to get into a financial analyst kind of profiles and like to do more research on stocks, create financial models and publish equity research reports. It has three levels and you have to spend almost three years to complete them. The fastest you can complete it is in 18 months, but that will be very hectic.
If someone has done their CFA and they want to get into algorithmic trading, what should be the path for them?
If they have already done CFA and have 1 or 2 years of experience in trading, then it would be ideal to do EPAT, which covers the practical aspects of trading and helps in automating their strategies. Especially the optional project work of EPAT will give a lot of practical insights into the world of algorithmic trading.
What was your learning experience at EPAT like?
The course content was really good and practical. Especially sessions by faculties like Dr. Ernest P Chan, Yves Hilpisch, Dr. Euan Sinclair and Dr. Hui Liu were phenomenal.
Also, the support that I got was really amazing, it was very professional and friendly that I don’t have to hesitate thinking it’ll be too naïve.. I have never seen such support with any other courses that I have done. Whenever I have a query related to the subject or project work, I always got a response within a day.
What are your plans for the future?
I want to spend more time learning about Artificial Intelligence and Machine learning and its application in Finance. I just want to work on part-time projects for a year so I can establish myself more in this domain.
Currently, I’m exploring other deep learning techniques like fastai and reinforcement learning and trying to apply them to financial data.
Do you have any message for people who would like to get into Algorithmic Trading?
Just spend a couple of years trading so that you can truly understand the practical nuances and the psychology aspect related to trading. The aim here should be to focus on risk and minimize losses initially and not to worry too much about profits.
If one is really interested in programming and passionate about trading it would make sense to do EPAT. The main thing is to take the emotional part completely out of trading.
So EPAT actually helps in automating the execution part and you don't end up watching your screen, checking your P&L every now and then. This might lead to some discretionary judgement and take actions emotionally.
Thank you so much for the time, Bala. It has been a pleasurable experience talking to you. You had the passion for Algorithmic Trading, you learnt it, and that is how you followed it. It is great to know that you're applying all of the knowledge gained in EPAT in trading part-time.
EPAT is a comprehensive course covering topics ranging from Statistics & Econometrics to Financial Computing & Technology including Machine Learning and more. Start your quest to upgrade your knowledge of Algorithmic Trading with EPAT.
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.
