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Okay story time.
Why do I teach
We started this course in 2014. At that time, I just left my job at a trading firm as an apprentice to pursue some studies. The idea was to go back to the trading firm after my studies.
However, new traders at the trading firm do not have any base income, we take home a cut of what we make.
Most new traders don’t survive the first year, not to mention take home any money.
You can learn more about trading firms here: “How to get a Job in Trading and How Much will I Make?”
Thus, I decided to start a side project to supplement my income for the first year.
That side project eventually became AlgoTrading101.
Building a trading course that is useful
Before I got into the trading firm, like most trader-wannabes, I joined other courses on trading and read many trading books.
Most of the content impressed me then, but the problem is, I wasn’t competent enough to differentiate good knowledge from crap. Crap looked like gold.
Years later, when viewed with a new lens from my experience at the trading firm, things looked different.
It was frustrating to see so many get-rich-quick schemes and purely theoretical courses that are mistaken as practical knowledge.
The problem with trading knowledge is that it is not hard science, things change fast. To succeed in trading, we have to think 2 steps further than the competition.
How can I teach content that is constantly evolving?
My answer was to teach content that revolves around frameworks and mental models that make students successful.
I was my first customer. The aim was to build a course I would have wanted when starting out.
It doesn’t have to feed me a working strategy, but it has to give me the education that I would have gotten if I had gone into a trading firm.
Different types of trading courses
Let’s digress and talk a bit about types of trading courses. Today, trading courses fall into 3 categories:
- Get-rich-quick shady schemes
- Academic and theory-based courses
- Application-based courses
Academic and theory-based courses
These courses have their merit. Their main goal is to help you get a job in a hedge fund or asset management firm.
University courses are a good example. You won’t become the next retail trading star after taking a quantitative finance degree. But you’ll get into a trading job that pays you $200K/year. And that’s a great outcome!
In your trading job, you will be a part of a system where your quantitative skills can be maximised.
You won’t start off being good at trading, and you don’t have to be. You’ll have top investment managers and traders to guide you.
As you get more experienced, you’ll get more autonomy and learn how to make good trading and investment calls (things that aren’t taught in the academic-based courses!)
That said, if possible, please only go for those degrees and courses that are high in demand, i.e. the top universities (not their free online programme).
The main reason for choosing an academic course is to act as a signalling (and networking) tool, it signals to employers that you are competent enough to enter the course.
If you just want the knowledge, you can just learn it online. Knowledge from these courses can be found online for free (Coursera and EdX etc) or at a cheap price (less than $20 at Udemy).
The knowledge from those courses can be useful when you work as part of a technical team in a hedge fund or bank, but this knowledge does not teach you to trade.
Identifying an academic-based course
There are many such courses. Their content is a mixture of those found in finance, quantitative finance and data science degrees.
It is hard to identify one if you are not trained in financially.
As a guide, if your course looks anything like the following, it is an academic-based course:
- Quantitative Finance & Algorithmic Trading in Python (Udemy)
- Options, Futures, and Other Derivatives (Textbook)
- Financial Engineering and Risk Management Part I and Part II (Coursera)
p.s. I have a degree in Quantitative Finance. The amount of knowledge from it that I use for my personal trading is probably less than 5%.
Application-based courses make you get down and dirty and do practical work in a specific domain, but they don’t necessarily look good on your resume.
I’ve learnt a huge part of my algorithmic trading coding skills from an application-based course and I am grateful for that.
That said, application-based courses and books are not the final answer. They give you a framework for you to find the answer.
There are some that teach the coding and testing part well, but I don’t agree with trading design concepts.
I didn’t find one that gave me a satisfactory framework to think about idea generation, strategy design and testing.
What do we teach
AlgoTrading101 is an application-based course.
We don’t promise you strategies that you can deploy today and make tons of cash. That model is perpetuated by get-rich-quick schemes and that is exactly what AlgoTrading101 stands against.
As mentioned, we wanted to give education that a trading firm provides – these include frameworks and exposure to deeper level strategies.
However, we didn’t start that way.
The first version of AlgoTrading101 had 3 guiding principles. We wanted people to:
- Get started coding and implementing fast (so that they feel good and gain momentum when learning)
- Learn in the most effortless and concise way possible
- Learn how to properly design test their ideas
On hindsight, this initial idea was a little lacking.
We wanted students to build market prudent strategies (i.e. strategies that works because of identifiable and fundamental reasons).
When you find a strategy that is market prudent, it should feel obvious:
- You can see clearly why the strategy works
- You can roughly see the opportunity by eye-balling (without using any statistical analysis)
- No matter how you change your parameters, your tests produce good results
- During times when it fails, you see a clear reason why that happens
- You can tell when the opportunity is fading off even before your strategy starts losing money
Initially I had hoped that students, now equipped with the proper skill sets, will try, fail, try again until they succeed in developing their strategies.
However, it was difficult for students to build successful strategies as they have not experienced one in action before. It was like thinking of a new colour.
Evolution of AlgoTrading101
Thus, AlgoTrading101 has to evolve beyond just giving frameworks. We have 3 changes.
Change 1: We plan to expose students to some modern strategies
With a taste of seeing something that works, they can extrapolate that to further ideas.
Note that seeing something that works is not the same as being successful with it.
Some traders I know were taught the strategies that the successful traders used, but they failed to execute it as well – discipline, emotions and psychology all come into play.
Change 2: Obscure markets
We have placed more emphasis on obscure markets. Trade the markets that are less popular and more inefficient. Don’t fight with the big boys in the crowded pool.
Change 3: Keeping up with the new
Another change we made was to keep up to date with new techniques. New avenues of information and methods of analysis provide opportunities.
Retail traders can scrape the web for unique data, buy several data sets and combine them, and analyse data using machine learning techniques (images and texts).
As trading evolves, we will keep up and add content relevant to retail traders.
Note that if you want to be a data science or machine learning expert, this is not the course for you. We teach the path that has the highest reward per effort for trading. This is not the way to go if you want to be a domain expert in any arena. You will need to learn and build a good foundation for that.
Do I find meaning in trading
The short answer is… not really.
When I was still in university, I did. I thought of it as an exciting field where top minds compete.
When I was asked what’s the value of trading to society, my half-joking reply is “it adds liquidity to the markets”. While this is true, trading really isn’t that useful to the world.
If the bright minds in finance were to solve poverty, cure cancer, build rockets or fix climate change, the world would be a better place.
Anyways, I’m a hypocrite as I still trade because of the $$$ and I do enjoy it. I’m a (calculated) risk-lover.
Running a trading course
AlgoTrading101 wasn’t meant to be a full-fledged business. I would be happy if it were to bring me 1K/month as a side project. But I was lucky. It grew and I went along with it.
I used to be uncomfortable running this algorithmic trading course. I didn’t want to encourage people to go into trading. But I’ve changed my mindset because of this disclaimer:
“Trading is tough and there are many other easier and more meaningful ways to make money. Don’t do it.”
That’s how I really feel. I know of people who have pursued this journey for a long time but get nowhere.
However, if you really want to endeavour into trading because of your passion or some other reasons, then alright, I’ll try to aid your journey.
That’s all for this post.
And nope, I didn’t go back to the trading firm after my studies as AlgoTrading101 was gaining some traction by then. Going back will mean letting AlgoTrading101 be passive (and eventually fade away).
Now, I run AlgoTrading101 and trade my own portfolio at the same time.