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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.


“Nobody can catch all the fluctuations.”
– Jesse Livermore

TT partners with universities around the world through our University Program. We provide our software, free of charge, to dozens of schools to help them prepare students for careers in the global derivatives industry. 

Professor Robert Webb

Our collaboration with the University of Virginia has been particularly beneficial. Professor Robert Webb has utilized our software in classes at both Virginia’s McIntire School of Commerce and the Darden Graduate School of Business. Students get experience in electronic trading as well as an understanding of automated trading through TT’s software. 

In this guest post, Professor Webb and Alexander Webb share their thoughts on the recent benefits and challenges that high-frequency trading brings to today’s markets. 

It has been said that today’s high-speed financial markets can change in the blink of an eye. That is wrong. A blink of an eye is too slow. In a market increasingly dominated by high-frequency trading (HFT), prices can change sharply in a millisecond, but it takes between 100 and 200 milliseconds for a human eye to blink.

Simply stated, you are literally missing trading opportunities in the blink of an eye.

With speed like this, can humans even hope to make money in a market dominated by high-frequency traders? Yes—but it entails trading smarter.

The Growth of High-Frequency Trading

HFT is growing because it is immensely profitable. A 2012 study by Baron, Brogaard and Kirilenko reports that HFT firms made $29 million in profits from the e-mini S&P 500 stock index futures market during August 2010 alone.1 And they did so bearing little risk. The average Sharpe ratio was a phenomenal 9.2. To be sure, the HFT firms earned a small amount per contract traded—on average $1.11—but given that they traded thousands of contracts each day, the small profit per contract grew quickly.2

HFT firms are not all the same. They vary from firms that are passive liquidity-providers to firms that are aggressive liquidity-takers. There are HFT firms that target human traders and firms that target other high-frequency traders. The Baron, Brogaard and Kirilenko study reports that the most aggressive HFT firms—which were largely liquidity takers—made the most money.

Milliseconds matter in the fast-paced world of HFT. This has led many HFT firms to co-locate their computer servers near the exchange servers in order to reduce exchange latency, i.e., the time it takes for an exchange to report information to participants.

How much is a millisecond worth? A 2012 study by Frino, Mollica and Webb found the introduction of co-location in the futures markets on the Australian Securities Exchange (ASX) in February 2012 resulted in a two-millisecond advantage to the HFT firms co-locating their servers near the ASX computer.3 Given that HFT firms are paying a minimum of A$10,000 per month for this privilege (and usually more), they must believe it is money well spent.

But the rise of HFT isn’t the only negative news for humans. The study suggests that the introduction of co-location on the ASX futures markets has increased liquidity with no apparent adverse effect on volatility. This increased liquidity would result in an annual savings of A$12 million in the cost of trading the four principal ASX financial futures contracts. These are estimates for the societal savings from the increased liquidity that HFT firms provide.

Trading Obsolescence

The reality is that while HFT may result in increased liquidity, it also presents many obstacles to the human trader. Strategies that were profitable before HFT are now obsolete.

Among those strategies with questionable profitability today are:

  1. Arbitrage: Markets move so quickly that the opportunity to arbitrage between them is difficult if not impossible for those who do not utilize HFT.
  2. Market making: HFT imposes excessive risks on those traders who provide two-sided markets in that the market maker cannot react to a change in the order flow as quickly as the high-frequency trader.
  3. Getting the “edge” in the market (i.e., buying on the bid or selling at the offer): With the exception of illiquid markets where the bid/ask spread is wide, the only situation where a trader can participate on the bid or the offer is when that market is turning.
  4. Event trading: Competing against HFT in terms of speed of response to scheduled economic reports and conventional news is impossible since HFT systems can process the information and react to it quicker.

Human traders need to trade strategically to avoid the dangers HFT presents.

Trading Opportunities

Is there hope for human traders? HFT systems may be fast, but they don’t always get it right. Sometimes, it seems that the market does not always know which direction it ultimately should move. Take, for example, the reaction of EuroFX futures to the monthly employment situation report on February 3, 2012. The euro initially rose in reaction to the January 2012 employment report and then fell. The same was true of gold. Moreover, this was not a one-off event, but similar examples occurred at other times. The key takeaway is that you may have more time than you think to react.

Humans are likely to be best at reacting to freak situations and unexpected market shocks. Not all algorithmic traders are high-frequency, but all high-frequency traders use algorithms. When the winds of change hit the market, humans are still more adaptable, flexible and able to change with the times. While algorithms can be reprogrammed, they can’t be reprogrammed fast enough to take advantage of a contemporaneous shock.

Algorithms are often unable to discern real news from fake news. For example, a tweet from a fake Muddy Waters Twitter account led to a 25 percent selloff in shares of Audience Inc. Reuters quoted Hammerstone Group founder, Jamie Lissette, as saying, “It’s good to some degree, because it makes people realize that we can’t just have a computer doing something like that just based on ‘Muddy’ and a symbol.”4

Trading Smarter

What are the weaknesses of high-frequency traders? Basically, many of them have gone after the easy money—market making—although with an eye toward minimizing losses. They are constrained by their algorithms. You need to think differently.

You need to get your hands dirty by observing the market’s reaction to various news events and spotting oddities. Other things equal, you are probably better off with a reason or hypothesis why prices should behave in a certain fashion than not. Your hypothesis also needs to be tested. It has never been easier for non-programmers to test relationships or develop simple trading systems. For instance, Trading Technologies offers ADL™, a visual programming platform that allows non-programmers to easily create algorithms by dragging building blocks onto a design canvas and connecting them to create executable strategies. The blocks convert to pre-tested code, and the desired features are all pre-programmed for you. Software like this is one way to develop and test potential trading strategies.


TT’s ADL™ visual programming platform allows traders and programmers alike to
develop, test and
deploy automated trading programs without writing a single line of code.

QIM co-founder and “hedge fund wizard” Jaffray Woodriff,  in his interview with Jack Schwager in Hedge Fund Wizards, argued that traders should “look where others don’t.”5 This is excellent advice. Equally important is the need to test potential ideas in a rigorous fashion in order to avoid introducing biases into the analysis.

Some HFT algorithms attempt to identify human orders from other HFT orders. Trading smarter also means not succumbing to some of the decision pitfalls to which humans are prone, like submitting orders for an even number of contracts or trading when volume is lower during the day (i.e., outside the open and close), etc. Easley, Lopez de Prado and O’Hara [2012] suggest some ways that human traders can avoid being victimized by HFT.6

Perhaps the most important thing to remember is that you get to choose when to trade, and you should only trade when you have an advantage. Whether you’re trading intraday or long-term, you should only pull the trigger when you are confident the odds are in your favor.

About the Authors

Robert I. Webb is a Paul Tudor Jones II research professor at the University of Virginia. His industry experience includes positions at the World Bank, the Chicago Mercantile Exchange, the Office of Management and Budget and the CFTC. He earned his PhD in finance from the University of Chicago. He edits The Journal of Futures Markets and has authored numerous books including his most recent book on high-frequency trading he co-authored with his son Alexander, Shock Markets: Trading Lessons for Volatile Times.


Alexander Webb is a writer. His interests include finance, emerging markets, politics, business, technology and international travel. Living in Asia for half a decade, Alex earned a baccalaureate degree in international business and global management from The University of Hong Kong. He has studied at the Chinese University of Hong Kong, Beijing Language and Culture University and at Shanghai Jiao Tong University. He also studied in Japan at Ritsumeikan Asia Pacific University.1 Baron, Brogaard and Kirilenko, “The Trading Profits of High Frequency Traders,” Working Paper, University of Washington, August 2012.
2 Baron, Brogaard and Kirilenko [2012] report that the small profit per contract “equates to $46,039 per day for each HFT in the August 2010 E-mini S&P 500 contract alone.”
3 Frino, Mollica and R.I. Webb, “The Impact of Co-Location of Securities Exchanges’ and Traders’ Computer Servers on Market Liquidity,” Working Paper, University of Sydney, November 2012.
4 Reuters, “A Tweet from Someone Posing as Short Seller Carson Block Sent a Stock Tumbling 25% Today,”, January 30, 2013.
5 Schwager, J., Hedge Fund Wizards, New Jersey: Hoboken, John Wiley & Sons, 2012.
6 Easley, Lopez de Prado and O’Hara, “The Volume Clock: Insights into the High Frequency Paradigm,” March 30, 2012. The Journal of Portfolio Management, Fall 2012, Johnson School Research Paper Series No. 9-2012.