After completing my bachelor’s degree in mathematics, I served as a credit risk analyst at a commercial bank. During this period, I obtained the CFA designation and also engaged in personal trading. Due to my interest in math, I was very curious about how math works in risk management and trading. I wanted to develop an understanding and apply this knowledge to my work and my own trading, so I enrolled in the CQF program. Studying for a PhD or a master’s degree in financial engineering was another option, but that was not viable for me because of the time required, the economic cost, and the lockdown associated with COVID-19.

I think the greatest approach to learning at a superb school or through an intensive project is not that they tell you what to do, but that they help you discover what not to do. As a private investor, I have read a number of books about trading. However, the methods and strategies in these books are often difficult to reproduce in real trading situations. In the CQF lectures, I found models and methods that can examine and embrace randomness and uncertainty instead of trying to constrain or avoid them; these methods include practical techniques related to pairs trading, options, dynamic hedging, and so forth. 

The lecturers are specialists in industry or academia and sometimes shared anecdotes from their professional or academic careers. They also provided well-developed notes and other supporting materials and answered each question during the live broadcast with patience. As I learned, I began to adjust or suspend some trading ideas I had pursued in the past. The CQF lectures gave me a clear target for the years to come, with goals of improving my mathematical ability and programming skills and not dancing with randomness until I find a skillful way to do so. As I prepared for the exams and final project, I often used the reference books that are recommended for the lectures; I also studied various Python third-party libraries introduced in the lectures to seek the best methods and models for particular tasks.

In Module 4, I worked with support vector machines (SVM) and in Module 5, I worked with neural networks. In my final project, I tried to build a larger and more complex neural network. I found that many of the older algorithms had been surpassed by later versions. This provided pressure and motivation to pay attention to the latest developments in the quantitative finance. Throughout the exams, I worked through the entire process of designing a strategy or model from conception to completion. The process was filled with failures and debugging and improved my skills and tenacity greatly. This hard work paid off and to my good fortune, I received the First in Class Award for highest average mark.

While I was doing the CQF, I also attended an online lecture of the CQF Institute about genetic algorithms. I didn't comprehend it fully at that time, but the Lifelong Learning library contains all of the videos of these lectures, so it is possible to revisit the materials – enabling us to review complicated subjects at our own pace and to follow the latest trends, which is very important in quantitative finance.
 

Now I'm looking for a job as a Risk Quant and I really thank the CQF program for giving me an integrated knowledge system, which is the foundation for starting my quant career. My advice to CQF delegates is to take all of the lectures seriously. The exam is only part of the work and there is much more to learn beyond the classroom. In Module 6, for example, the content of one lecture alone was basically an entire reference book, including topics on xVA, Monte Carlo, Copulas, and more. The more hard work you can undertake, the more rewarded you may be. Find the weak links in your skills (math or programming) and address them effectively. I hope everyone has a happy and fulfilling CQF journey.