Trade Talk Blog: TT CampusConnect

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interns at lincoln park zoo
Our summer 2014 interns (L to R): Ian Torres, Purav Shah,
Stephen Herring, Everett Hu, John Lefkovitz, Joshua Alley.
We recently concluded our first official summer internship program. While we’ve had students interning with us in the past, we wanted to make this summer a more formal (and fun!) experience.

Our internship program goals are similar to the TT University Program, which provides free access to our trading platforms, APIs and expertise to more than 50 universities around the world. The main goal of the University Program is to educate and mentor the next generation of financial leaders by donating our expertise and technology to universities worldwide. Whether the students are studying finance, computer science, engineering and/or economics, we want them to understand how to use trading software in a safe, efficient way.

The goal of the paid internship program is to provide top students on summer break the opportunity to contribute enhancements that make our software even better, which in the process helps them become more advanced programmers and developers, and to enhance their understanding of the financial markets.

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Look for the hashtag #TTturns20 on Twitter
for more about our milestone anniversary.

Today, as we celebrate 20 years of building trading software for our customers, I thought it was worth a look back at how the industry, and subsequently TT, has evolved over the past two decades and take a look at what’s ahead for our customers and TT.

On this date 20 years ago, we were founded in Frankfurt, Germany when nearly all trading on futures markets was conducted via open outcry. Access was extremely limited, and the ability for people to realize the benefits of listed futures, namely accurate price discovery and risk management, was limited to a select few.

When TT made Chicago its home a few years later, the floors of the Chicago Board of Trade and Chicago Mercantile Exchange roared. But as the trading community got comfortable with the concept of electronic trading, volume began to gradually migrate to the screen. A product we released in those early days, MD Trader®, had a huge impact because it was a radically different way to interface with the electronic market. It gave traders the ability to see and interact with the market with a level of confidence they hadn’t seen before and, in many ways, went hand-in-hand with the dramatic migration of volume to “the screens.”

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Ben Van Vliet
Dr. Ben Van Vliet

Through the TT University Program, TT partners with universities around the world to help them prepare students for careers in the global derivatives industry. We provide our software, free of charge, to dozens of schools around the world, including the Illinois Institute of Technology (IIT).

IIT’s Dr. Ben Van Vliet has been using TT’s software as an educator for many years. In this guest post, Dr. Van Vliet looks at the impact automation has had on the markets over time and what it means to the next generation of finance professionals.

All of finance is automated. It’s virtually impossible to do anything in finance without turning on a computer and using some form of automation—Excel, databases, charting packages, APIs, execution platforms. The most obvious example of this is automated trading, where the entire life cycle of a trade, from exchange data feed to trading strategy to order management, happens inside the computer. Trading automation is a complex endeavor. It involves programming, mathematics, and strategic thinking about markets and technology. It’s a lot to learn, but this is what markets are about today.

An automated trading system consists of the rules for entry into and exit from a position or positions and the technology, both hardware and software, used to make them happen. These rules are a set of logical or mathematical operations that can be based upon qualitative, technical or quantitative research. If students want to actually build automated trading systems that execute trades on electronic exchanges (and they should), they need to learn how to create these rules and work with both real-time and historical data in code.

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According to the Futures Industry Association, exchange-traded futures and options volume grew 133 percent globally from approximately 9 billion contracts in 2004 to over 21 billion in 2012. Equities trading grew at an even faster rate, up 180 percent from 3.5 billion trades in equity shares in 2004 to 9.8 billion trades in 2012 according to the World Federation of Exchanges.

Considering that most trading volume is electronic—and that electronic volume continues to grow—it’s easy to see that the size and speed of data transmission in today’s financial markets brings challenges to exchanges, money managers and financial service firms. How can the numerous players and platforms streamline the transmission of data to conduct business efficiently and economically?

Enter the Financial Information eXchange (FIX) protocol. Since 1992, FIX has been specifying electronic communications standards for the financial markets. Today, FIX is the de facto benchmark for those communications.

The University of Chicago’s campus in Hyde Park

 

FIX Protocol Limited (FPL), the group that manages the development of FIX and promotes its use, states that “virtually every major stock exchange and investment bank uses FIX for electronic trading, as do the world’s largest mutual funds and money managers and thousands of smaller investment firms. Leading futures exchanges offer FIX connections and major bond dealers either have or are implementing them.” As orders are entered, cancelled or queried around the world, standardized FIX messages are used to manage and report them.

Developers use FIX messaging specifications to create software that electronically communicates trading information to a counterparty’s software, oftentimes replacing messages that were previously communicated between counterparties via telephone, fax or email.

TT’s FIX Adapter allows clients to use the industry-standard FIX protocol to integrate customer and third-party order routing, market data and trade capture systems with the TT platform. Clients use the FIX Adapter to quickly and efficiently integrate existing FIX-enabled systems with TT’s trading platform for compliance, trade tracking and fill reporting, order management and routing, and trade automation.

Using FIX-based messages rather than a proprietary messaging system helps ensure that different software programs can communicate with, understand and respond to each other. When disparate systems use standardized FIX messages, they can efficiently manage and report the status of orders as they are entered, cancelled or queried around the world. You might say FIX is the “international” language of the financial services industry.

In “What are the Benefits of FIX Protocol?”, FPL opines that the benefits of the widespread use of a standard messaging protocol include:

  • Reduced cost and complexity of integrating various internal activities.
  • Increased ability to share infrastructure in terms of software, hardware and internal staff.
  • Lower costs resulting from minimizing re-keying and translating data.
  • Easier monitoring of overall positions.

Integrating FIX with academics at the University of Chicago

It’s easy to see how a working knowledge of FIX would be valuable to students seeking to work in today’s trading and financial services industries. The faculty at the University of Chicago, one of TT’s University Program partners, understand this. The curriculum for the university’s degree in financial math includes a course that requires students to complete projects using the FIX protocol.

TT recently sat down with Chanaka Liyanaarachchi, who teaches the course, to discuss the class and why he feels his students need this experience.

Can you describe the students’ project?

Chanaka: The project was part of a series of Computing for Finance courses taught in the Financial Mathematics Program at the University of Chicago. Working in groups, students wrote an electronic trading program using a trading algorithm of their choice. They used FIX to gather market data and to send out messages related to orders to TT’s Developer Environment.

What was the objective of this project?

Chanaka: My main objective was to provide the students with both an interesting and relevant project that would allow them to practice the programming skills they learned in the Computing for Finance series, which emphasizes project-based learning.

Were there any other goals of the project?

Chanaka: One of my secondary goals was to introduce the FIX protocol to the students. Now that electronic trading has all but eclipsed the other forms of trading, and because FIX is an industry standard in electronic trading, it is a good tool for any financial engineer to be familiar with.

Additionally, it gave the students a chance to use a “real” professional trading platform, along with the opportunity to learn about a variety of products offered by different exchanges. And, finally, the students learned how to successfully complete a software project as a group.

Were these objectives met?

Chanaka: Based on the feedback I’ve received so far, it would seem that the set objectives were successfully met! The students practiced programming skills, and used concepts they learned in other Financial Mathematics courses to design and analyze trading algorithms, price instruments, etc.

Why did you use FIX; why not TT’s proprietary API?

Chanaka: Either would have been a good choice; we could have achieved the main goals of the project by using TT’s proprietary API. Ultimately, FIX was chosen because it is an industry standard.

Why did you select TT as opposed to writing directly to exchange APIs?

Chanaka: Due to a large class size, we had to request a significant amount of resources from TT. We greatly appreciate TT’s efforts to accommodate our requests in a timely manner. From a technical point of view, the TT Developer Environment allowed the students to access different markets of their choice, and to switch between different markets with ease, due to uniform/normalized access. This is the main technical reason why we chose to go with the TT system, as opposed to directly connecting to an exchange.

Quite often our collaboration with universities results in a mutually beneficial relationship: universities benefit from our assistance with “real-world” applications while we benefit from their feedback and the students they educate. The University of Chicago has proven to be a good example of this. The best is yet to come.

Chanaka Liyanaarachchi

About Mr. Liyanaarachchi
Chanaka teaches “Computing for Finance” in the Financial Math Program at the University of Chicago. A graduate of the University of Peradeniya (Sri Lanka) in electrical engineering and Computer Science, he earned his Master’s in Computer Engineering from the University of Kansas. In addition to this, he has an MBA in Finance from DePaul University and a Master’s in Financial Math from the University of Chicago. He has been trying to improve the computing courses to teach students the necessary computing skills needed in today’s finance industry. His current interests include high-performance computing in financial applications and functional programming.

Tulane Algorithmic Trading Club members Willow Zhang (1st
Place) and Yuki Yang (2nd Place) used X_TRADER® to out-
perform the competition in the club’s first official trading contest.




Tulane University is one of Trading Technologies’ most active University Program partners. At Tulane, our X_TRADER® software is used in classroom instruction, and it’s installed at the A.B. Freeman Trading Center laboratory. The lab is accessible to all students, including members of the Tulane Algorithmic Trading Club (TATC).

The club supports research, facilitates discussions and encourages hands-on development of automated trading strategies. As a student organization, membership is open to all interested Tulane students.

TATC members gain real-world skills by writing and developing trading algorithms while learning from collaborations with their peers and faculty advisors. The students are challenged to trade in simulation mode against live market data using industry-standard technologies, including X_TRADER and TT’s application programming interfaces (APIs). The algorithms that they develop are deployed in simulation against real-market data, and the strategies are ranked in terms of profit to identify the students who’ve best met the challenge.

Recently, the club held its first official trading competition. Entry was open to all Tulane students. Participants were encouraged to be creative in their use of information and technology when they developed their automated strategies, which ranged from high-frequency styles that monitored market liquidity and took advantage of arbitrage opportunities to those that were a bit more long-term in nature and driven by technical indicators.

And the Winner Is…

After the contest concluded, TATC President Zachary Poche and Secretary Geoffrey Lewis reported: “…the Tulane Algorithmic Trading Club held its first trading competition. Algorithms were pitted against one another in a battle of mathematical wits and technical savvy. The competitors met in the trading laboratory at 12:00 PM, and, after a quick fine tuning of Trading Technologies by our resident tech wizard Kevin Ammentorp, trading began promptly at 12:15. After 45 minutes of trading, the best algorithm was evident and club member Willow walked away with the top prize, our respect and adoration, with an overall profit of $55,000. The first runner up was Yuki with a profit of $8,000.”

Willow’s winning strategy utilized the Average Directional Index (ADX) and the Commodity Channel Index (CCI), while Yuki’s runner-up strategy was a Moving Average (MA) indicator built entirely in ADL™, TT’s visual programming platform.

The students who participated in the contest said they appreciated the opportunity to apply what they learned in class using real-world tools in a real-world setting—to get real-world results.

Of particular interest to me is how enthusiastically the students employed ADL. It enabled them to design, test and deploy their trading algorithms in C# without writing a single line of code. Geoff and Zack told me that one of the biggest advantages to using ADL was the speed at which the students were able to generate, test and deploy their strategies once they were defined. These students haven’t graduated yet, but they know that in the marketplace, “speed to market” is vital, and they leveraged ADL to obtain a speed advantage.

Clubs like Tulane’s Algorithmic Trading Club highlight the collaboration of university, students and business. As a voluntary endeavor, the students display an entrepreneurial spirit with faculty guiding that spirit. I’d like to think companies like ours, that provide the technology and training, give them the tools to empower that spirit and bring their ideas to life. That’s a winning strategy for everyone.

ACKNOWLEDGEMENT: The Tulane Algorithmic Trading Club is steered by Zachary Poche (President), Joshua Aiken (Vice President) and Geoffrey Lewis (secretary). Professors Joe LeBlanc and Geoffrey Parker are the faculty advisors.