How I learned Quant Trading

About me

I am a Quant with 7+ years of experience in trading. I have traded each and every asset in my career from Forex to stock options and crypto. For the last 4 years, I am into Algo. Trading and achieved great success doing so. Now I am focusing more on helping traders and guiding them on how to trade, what are the market pitfalls and how to manage Risk.

Why I wanted to learn Quant Trading

The reason why I got into Quant Trading is because of my mentor. He is also a Quant trader and trading for the past 20 years. Back then when I was in my college completing my graduation while trading using discretionary methods I got very intrigued by the concept of Algo trading and the approach that Quant use. As human evolves so does the market and the market only favors those who have an edge in the market. Quant trading provides you an edge as you will be trading a highly backtested and risk-averse strategy.

How I approached learning Quant Trading

I am a big fan of python. My journey in coding begins with C++, it is a great language and helps you to learn concepts better than any other language also it is very fast. I have cracked Global competitive hackathons using it. But there is a big issue with the language which is development time. Python is function-rich and the development time to code a strategy is very low compared to C++.
There are also many free Quant libraries available in python. I have coded successful strategies over many exchanges, like Alpaca, Oanda, FXCM, Binance, and Deribit .......

Challenges I faced

Building Alphas are tricky. I have used M.L and D.L in trading too and believe me it is quite easy to overfit your trading strategy and when you deploy it in the live market it loses and loses. Unless you have an understanding of the market and in and out of strategy it is quite normal to fall into these pitfalls.
To overcome this you should always split the data into two parts. The first is for Optimization i.e In sample data other is for Test data i.e sample data. If a strategy performs well in both cases then there is a good probability that it will keep performing well in a live environment.

Key takeaways

I really enjoyed the strategy optimization part and testing it on the live market. Sometimes a good strategy feels apart in a live environment and all you need to do is add logic to it. Markets are always changing and so should your methods.

Tips and advice

I highly recommend learning Python even though Matlab is very useful and there are great resources over the internet. I highly recommend reading Intelligent Investor and other investing books.

Final thoughts and next steps

As I am learning advanced concepts I am more interested in developing models using A.I.