Introduction to Algorithmic Trading Strategies 2012 @ HKUST

Home 2015 Forums Applications Trading Strategies (Algo Quant) Introduction to Algorithmic Trading Strategies 2012 @ HKUST

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  • #1903
    Haksun Li

    Hello students,

    Could you let me know what you think of this course please?
    Any feedback/comments/complaints would be appreciated.



    The course is clear to provide great insights in algorithmic trading. It provides different angles to trading strategies and is good for students to, as a starter, understand how quantitative trading works and what the philosophy is behind. The only drawback is the lessons are not long enough to teach more knowledge and skills.


    Good introductory course in Algo trading!

    Being a computer science person and am doing a master degree in investment management at HKUST (going to be my 4th degree), I’ve always wondered what the buzz was about Algo/high frequency trading, particularly the details behind.  This was a good introductory course to the details I have been wondering about.  Ran into this class by accident, initially thought I would just sit in for a couple of lectures to close some of my curosity and end up sitting through the whole course lectures.

    Doctor Haksun gave us very good introductory details on some of the popular trading strategies (hidden Markov, mean reversion/pairs trading, trend following/technical analysis) as well as the key mathmatics behind the strategies.  I consider myself a pretty good number/mathematics person but I wish I had more background in statistics/mathematics though so I understand more of the mathematical derivations behind the scene.  Nonetheless, I got what I was looking for, to better understand what the buzz was about and the key details behind it.

    Having gone through almost all the class materials (one more class to go), I remain to be convinced about how viable this is though.  That is, with what we learnt so far, can we viably implement something that would make statistically meaningful positive return using that alone?  The key block I have not been able to move pass still remain that we are still using historical data (asset movement pattern, returns, risks, etc) to predict the future, which likely is not viable/accurate.  But then, I do not have a better alternative to model this, not yet anyway, so the thinking/research continues…

    I really wish there was more time though, perhaps this should be a 2 course curriculum: 1 to teach the basic and the theory and then a follow on class for us to implment.  Amount of time is very short for the amount of materials and types of materials that we have gone through.  I really would have like to fully implement one of the trading strategies, back test it, and then add some of my own “improvements” on the top of the basic and then freeze the code and simulate how that work on future 2 months data for example, afterward evaluate how we do.  That would have been the major highlight of the class I think, at least for me anyway.  This one month course was simply too short for that…

    Doctor Haksun is pretty funny and (smart) obviously very good at what he does.  I am glad we get to meet.

    Thanks for the wonderful class and spending the last month with me.



    I appreciate Dr. Li presented me with such a very systematic introduction about Algorithmic Trading Strategies in the course. As a financial Math major postgraduate, I think it very necessary to have certain knowledge about this topic, to prepare myself in the future career choices, at least to benefit myself in the foreseeable interviews. That’s why I spend the whole winter semester in this course.

    The contents of this course are very rich, including pairs trading, mean reversion, trend following, risk management, etc. From the theoretical learning, to the practical programming, then to the specific project study, I have learned far more than what I had expected. Moreover, Dr. Li’s wisdom and humor add extra points to this course, and make those difficulties in the topic much easier to be understood.

    FANG Yuzhou

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