The course, entitled, “Trading Systems Development with Complex Event Processing,” attracted 33 students and culminated with a mock algorithmic trading challenge.
Adjunct Professor Ahmad Namini, a full-time Wall Street trader at a well-known asset management firm, was on hand during the semester to lecture the class on microstructure, trending signals, analysis and momentum trading as well as how to identify gaming.
In addition, Jeffrey Fredricks, a graduate of the masters program who is now a solutions architect at StreamBase, was also on hand to help the students design and deploy a complete algorithmic trading platform.
“We’ve been equipped with so much math and technical knowledge but there is a gulf between what is done in the industry and what is being taught,” says Fredricks. “It sparked my interest to go back to academia and set up a trading simulator that also teaches students how to do algorithmic trading.”
He says the class and curriculum covers the gamut of algorithmic trading from setting up the system to programming the various components and then creating strategies in the mock environment to compete for alpha.
With the lectures by Namini, Fredricks says the class has the background and the “theory to understand the markets and how to translate that into algorithms.”
There were eight teams set up within the class and each came up with its own algorithmic trading strategy to compete in the mock environment.
“It takes a trader to understand the dynamics of the market but it also takes programmers to express trading ideas and a lot can get lost in communication so it’s essential to understand both sides of the equation,” Fredricks contends.
The mock trading environment was built using the StreamBase complex event processing (CEP) platform and laptops. More than $1 million of StreamBase software was donated to the class.
The environment included an exchange with a matching engine, an execution management type interface between the students and the exchange that tracked orders and fills, as well as a profit and loss module that interfaced with a profit and loss tracking system.
The students also used small pre-trade risk management and post-trade transaction cost analysis modules during the competition.
“The students get a grasp on how hard it is to build an entire algorithmic trading system,” says Fredricks. “From market data management to alpha-seeking algorithms, the EMS interface and all the different parts.”
After the competition Fredricks says students were able to then dissect their performance in order to learn what worked, what didn’t work so well, what were winning strategies and why.
The course is already included in the Fall 2010 curriculum for the Masters in Mathematical Finance program at BU and StreamBase is looking to support additional courses at universities in the U.S. and the UK.