- Joined
- 3/29/10
- Messages
- 117
- Points
- 38
I’m currently a student at Baruch MFE and had the pleasure of taking Dr. Liew’s 5-course statistical arbitrage lecture series in Baruch’s inaugural Time Series Analysis and Algorithmic Trading course. The purpose of this post is to give readers a student’s perspective of what I believe to be one of the best quant courses on Wall Street.
Dr. Jim Liew offers this intensive statistical arbitrage course to students at Baruch MFE, Columbia MFE, and JHU MSE. He also has made this course available to industry practitioners for free, given that they complete a statistical arbitrage project for him after the course is finished. Dr. Liew received his PhD in Finance from Columbia Business School and has been an industry practitioner of systematic trading strategies ranging from macro to ultra high frequency.
Dr. Liew began the lecture series in earnest, giving background into statistical arbitrage, including its history, current state, and a top 10 list of the delusions of statistical arbitrage novices. In retrospect, I should have plastered this above my computer monitor because I personally experienced nearly every one of them without remembering Dr. Liew’s prophetic advice. Right off the bat, Dr. Liew let us know that his course would change our entire career paths, pending that we worked harder for it than we’ve ever worked in our lives. Students were to research and backtest strategies, incorporating transaction costs and volume constraints. Dr. Liew provided some initial direction, and a pinch of “secret sauce”, but ultimately the strategy and its backtesting framework were up to our own discretion. PCA, machine learning, neural nets, technical analysis, fundamental analysis, s-scores, and pairs trading were all suggested as fair game.
The first project we were given was creating a momentum strategy, trading on daily bars in 40 different markets. Dr. Liew wasted no time getting us up the learning curve, assigning the first of three projects during the first lecture. All of the teams would be ranked by highest sharpe ratio and grades would be diviid out accordingly. We were told that we could work with each partner on only one of the projects, which forced us to work with people who we wouldn’t normally have chosen. We were told we had two weeks to work on it, picked our own teams of two, and were off to the races.
The environment of the class was already highly competitive, which anecdotally led to terse phone conservations beginning in “what’s your sharpe ratio!?!?” breaking out among colleagues. I loved this aspect of the class, but there were some who had complaints about this intensity. Personally, I chose to have no partner for this first project in order to develop my own Matlab backtesting framework. I put in 120 hours of work during these two weeks and ultimately got the top sharpe ratio in the class, a measly 1.86. I believe Dr. Liew intended for the first project to provide us with a backtesting framework for the rest of the course, as well as a lesson in overfitting and optimization. Now I know what having 100+ parameters, no out-of-sample period, and limitless capacity can do to inflate a lackluster strategy.
The second project was using daily bars from SPDRs to create pairs under a mean reversion framework. For this lecture, Dr. Liew provided some “secret sauce”: the Avellaneda and Lee strategy from their 2008 paper. At first the model seemed easy enough to code up, but my team soon realized that keeping track of a universe of 100,000+ pairs was no trivial task. Also, as this time we were to run the strategy out of sample, the parameters needed to be endogenized. This made simulations significantly more computationally intensive. The project was due in the middle of Spring break and I had already booked train tickets home so I battled severe eye strain as I tried to finish the project on my 12 hour train ride the day before it was due. Ultimately, my team failed to endogenize enough parameters and we ended with a 0.50 sharpe ratio. Knowing that the third project would make this one look like cake, I knew I had to step it up.
For the final project we had three weeks to build and backtest a strategy trading anything we wanted on minute bars. There was an imposed volume constraint of 5%, a $1 ticket t-cost, and a $0.003/share t-cost. The final class was to be a presentation in front of a mock investment committee of funders of statistical arbitrage strategies, as well as executives at leading desks. Dr. Liew kept reiterating that this was our “shot” at breaking into statistical arbitrage. Any one of these practitioners could easily bring us on board to their respective desk. My team (David Rappaport, Michael Lwin, Yike Lu, and myself) was united in one goal: cranking the project and obtaining our dream jobs.
Before my team had even truly begun the project, Yike set up a Q/KDB database hosted on an Amazon EC2 cloud. My team spent a couple of days brainstorming strategies to get a sense of direction for the project. Using my improved Matlab skills, I developed a vectorized backtesting framework in which I fast-prototyped technical analysis strategies and extensions during the full course of the project. Ultimately, we all contributed ideas to improve the single-stock counter-trend strategy which I had discovered during this process. David developed a limit order framework which met the rigorous constraints Dr. Liew had assigned and Michael researched our market impact model.
We worked on the project every day for three weeks straight, without fail, for an average of 13 hours per day. After pushing us harder than we had ever worked in our lives, Dr. Liew’s promises held true. We received the highest scores from the mock investment panel, with the accompanying prize of getting to pitch to one of the top high frequency desks in the world. Our presentation can be viewed in the attached document. My team has since been contacted for interviews at other leading desks. Dr. Liew’s class inspired many people to seek positions on the buy-side and now, thanks to his class, we have the avenues opened to do so. I highly recommend this course to anyone looking to break into statistical arbitrage. Prepare to be pushed to your limit.
Dr. Jim Liew offers this intensive statistical arbitrage course to students at Baruch MFE, Columbia MFE, and JHU MSE. He also has made this course available to industry practitioners for free, given that they complete a statistical arbitrage project for him after the course is finished. Dr. Liew received his PhD in Finance from Columbia Business School and has been an industry practitioner of systematic trading strategies ranging from macro to ultra high frequency.
Dr. Liew began the lecture series in earnest, giving background into statistical arbitrage, including its history, current state, and a top 10 list of the delusions of statistical arbitrage novices. In retrospect, I should have plastered this above my computer monitor because I personally experienced nearly every one of them without remembering Dr. Liew’s prophetic advice. Right off the bat, Dr. Liew let us know that his course would change our entire career paths, pending that we worked harder for it than we’ve ever worked in our lives. Students were to research and backtest strategies, incorporating transaction costs and volume constraints. Dr. Liew provided some initial direction, and a pinch of “secret sauce”, but ultimately the strategy and its backtesting framework were up to our own discretion. PCA, machine learning, neural nets, technical analysis, fundamental analysis, s-scores, and pairs trading were all suggested as fair game.
The first project we were given was creating a momentum strategy, trading on daily bars in 40 different markets. Dr. Liew wasted no time getting us up the learning curve, assigning the first of three projects during the first lecture. All of the teams would be ranked by highest sharpe ratio and grades would be diviid out accordingly. We were told that we could work with each partner on only one of the projects, which forced us to work with people who we wouldn’t normally have chosen. We were told we had two weeks to work on it, picked our own teams of two, and were off to the races.
The environment of the class was already highly competitive, which anecdotally led to terse phone conservations beginning in “what’s your sharpe ratio!?!?” breaking out among colleagues. I loved this aspect of the class, but there were some who had complaints about this intensity. Personally, I chose to have no partner for this first project in order to develop my own Matlab backtesting framework. I put in 120 hours of work during these two weeks and ultimately got the top sharpe ratio in the class, a measly 1.86. I believe Dr. Liew intended for the first project to provide us with a backtesting framework for the rest of the course, as well as a lesson in overfitting and optimization. Now I know what having 100+ parameters, no out-of-sample period, and limitless capacity can do to inflate a lackluster strategy.
The second project was using daily bars from SPDRs to create pairs under a mean reversion framework. For this lecture, Dr. Liew provided some “secret sauce”: the Avellaneda and Lee strategy from their 2008 paper. At first the model seemed easy enough to code up, but my team soon realized that keeping track of a universe of 100,000+ pairs was no trivial task. Also, as this time we were to run the strategy out of sample, the parameters needed to be endogenized. This made simulations significantly more computationally intensive. The project was due in the middle of Spring break and I had already booked train tickets home so I battled severe eye strain as I tried to finish the project on my 12 hour train ride the day before it was due. Ultimately, my team failed to endogenize enough parameters and we ended with a 0.50 sharpe ratio. Knowing that the third project would make this one look like cake, I knew I had to step it up.
For the final project we had three weeks to build and backtest a strategy trading anything we wanted on minute bars. There was an imposed volume constraint of 5%, a $1 ticket t-cost, and a $0.003/share t-cost. The final class was to be a presentation in front of a mock investment committee of funders of statistical arbitrage strategies, as well as executives at leading desks. Dr. Liew kept reiterating that this was our “shot” at breaking into statistical arbitrage. Any one of these practitioners could easily bring us on board to their respective desk. My team (David Rappaport, Michael Lwin, Yike Lu, and myself) was united in one goal: cranking the project and obtaining our dream jobs.
Before my team had even truly begun the project, Yike set up a Q/KDB database hosted on an Amazon EC2 cloud. My team spent a couple of days brainstorming strategies to get a sense of direction for the project. Using my improved Matlab skills, I developed a vectorized backtesting framework in which I fast-prototyped technical analysis strategies and extensions during the full course of the project. Ultimately, we all contributed ideas to improve the single-stock counter-trend strategy which I had discovered during this process. David developed a limit order framework which met the rigorous constraints Dr. Liew had assigned and Michael researched our market impact model.
We worked on the project every day for three weeks straight, without fail, for an average of 13 hours per day. After pushing us harder than we had ever worked in our lives, Dr. Liew’s promises held true. We received the highest scores from the mock investment panel, with the accompanying prize of getting to pitch to one of the top high frequency desks in the world. Our presentation can be viewed in the attached document. My team has since been contacted for interviews at other leading desks. Dr. Liew’s class inspired many people to seek positions on the buy-side and now, thanks to his class, we have the avenues opened to do so. I highly recommend this course to anyone looking to break into statistical arbitrage. Prepare to be pushed to your limit.
