FLASHBACK FRIDAY: Comfort in Custom Algos
Do you remember when trading occurred manually?
A long time ago in a galaxy far far away there were no computer programs slicing and dicing orders to either spray or sequentially routing them to the exchanges for execution. There was just the buy- and sell-side trader, the specialist and perhaps a ticket runner. How did traders ever get anything accomplished?
Then around the turn of the millennium after Reg NMS, decimalization and computers took over trading and propelled it into the 21st century, a revolutionary trading software program was created – the algorithm. It was revolutionary in that it took a parent order and according to certain base parameters, broker it down into child orders. And then it would send these child orders to multiple trading venues to either be filled immediately or sent to others in the hopes of being filled there. Traders through computer software could now find liquidity, size and ultimately and best execution through the click of a mouse.
The first algos were simple – they functioned based on either a volume-weighed average or time -weighted average approach to finding liquidity. And these algos functioned only when trading stocks – no options, futures and forex. While limited for a time, these basic algorithms were a boon to the industry and fast became a traders’ best friend.
“Algorithmic strategies have become far more complex over the past 10 years,” began David Margulies, Head of Electronic Products Group at Weeden & Co. “Strategies that used to have very simple routing (i.e. divide the order equally among a list of venues) to a much more data driven approach to routing. Strategies now use a massive amount of historical and real-time data to make decisions on what venues to send orders to, what min fill size, what limit, etc.”
But that’s not all. Margulies added that order placement logic has also become infinitely faster in order to react to order imbalances, spread size, conditional invites, etc.
“In 2007 it was commonplace to measure latency in milliseconds (a thousandth of a second) as compared to microseconds (a millionth of a second) or even nanoseconds (one thousand-millionth of a second) in use today,” Margulies said. “In 2007, we launched the first non-internalized dark pool aggregator called Onepipe. At the time of the launch, Onepipe was considered cutting edge technology in how we accessed over 35 different pools. By today’s standards, Onepipe wouldn’t have been as successful using the logic and technology that was available in 2007. Over the years, we have made literally hundreds of changes to our strategies in order to constantly adapt to client needs, technology advancements, market structure changes and anti-gaming tactics.”
But then technologists said, “wait a sec, why can’t algorithms use other approaches to trading?” And thus more complex and bespoke algos were born.
You say you want a revolution?
And traders, not technologists, began to drive algorithm development. And the algorithms became more complex and required longer development and testing times.
So, is algo development still a long process given the trader’s more complex needs?
“This is an interesting question. The answer is both,” Margulies told Traders Magazine. “Development time is shorter due to deep investments made in rapid development tools and the rewriting algorithms to make them easier to adjust and customize. Many changes can be made overnight but it took a lot of work to get to that point.”
Back in 2007, almost every change to a strategy required a programmer to write new code, regression test, QA test, deploy, etc. Now many changes can be made with a simple parameter change, Margulies explained. This takes a lot of planning before the first line of code is ever written.
“Think about building a house. If you know you may want to add a bathroom in a new house down the road, it’s easy to have the rough-in plumbing installed during construction, as opposed to ripping up walls and floors to add a bathroom later on,” Margulies said. “The same concept applies to designing and building algorithmic strategies. We need to anticipate future changes and make it easy to make these changes when the time arises.”
Algo development never stops. Every day, traders like Margulies and his counterparts around Wall Street make changes to their strategies – some very small and some completely new in terms of features or logic. Just look at the market structure changes over the past 10 years – i.e. tick pilot, conditional venues, new order types – and it makes sense. It also takes a lot of time, effort and expense just to stay current. At Weeden, the firm employs a “Best Ex Committee” where they spend a great deal of time discussing venues, routing logic, performance metrics, etc.”
Peter Maragos, CEO of Dash Financial Technologies agrees, noting that traders too are more complex – just like the algos they now use.
“Buy side traders are extremely sophisticated today,” Maragos began. “Many of them are looking to really drive the routing and posting behavior of their trading strategies, which means they need a degree of control well beyond simple aggressiveness settings. Most of our clients want to work with us to help them customize every aspect of the algo in the same way a tailor would design a bespoke suit.”
And the trading desk, not just the trader began to change. The trading floor, once only the bastion of traders and salespeople, started make room for algorithm developers who would work hand-in-hand with the trader to make his algos better, stronger, faster.
“They (traders) can only do that with a full view into how the algo performs, however, which is why full transparency into the order’s routing behavior is so critical,” Maragos said. “Ultimately, we see all buy side adopting a hands-on approach like this, which helps them drive value for their firm by maximizing alpha capture.”
This article originally appeared in the December 2007 issue of Traders Magazine
Riding in Comfort with Customized Algorithms
By Alexa Jaworski
Industry observers agree that over the past 12 months, algorithmic trading has taken on a new identity, maturing into more adaptive strategies with better routing features. Algorithms are no longer just another innovative trading tool available to the marketplace. Algorithmic trading has become a necessity for savvy traders seeking to gain access to dark liquidity pools. It is no longer just about volume-weighted average price (VWAP).
Looking back on 2007, algorithmic trading has become a vital tool in any trader’s electronic tool chest, says Brad Bailey, senior analyst with the Boston-based research firm Aite Group, explaining that the rapid change in the U.S. equity market structure has driven and added complexity to how the average institutional trader gets a trade done. “You have to think about where all of this liquidity is,” he says. “That drove a lot of people to look at aggregation tools, such as dark algorithms, and algos looking to aggregate this liquidity.” That’s been one of the big themes in 2007–trying to gain access to the dark liquidity and access to this fragmented market structure.
Along with this has come the migration away from simple benchmark algorithms, such as VWAP or arrival price, to algorithms that provide more flexibility with specific tactics used to target those benchmarks, said Carl Carrie, global head of algorithmic products and head of Neovest at JPMorgan. “It’s no longer good enough just to say, ‘Trade according to arrival as a benchmark,'” Carrie explains. “A trader typically wants to have a lot more control over how that’s done. With more fragmentation, there’s more granularity and richer texture to trading then there ever was before.”
Another major trend in 2007 was the push toward using algorithms in different product sets, such as foreign exchange, says Tom Price, senior analyst of securities and capital markets at the Needham, Mass.-based consulting firm TowerGroup. “What I think is the trend or has been the trend is the migration of successful equity strategies into alternate asset classes such as foreign exchange,” he says, noting that the equity model is already fairly mature.
When discussing algorithmic trading trends in 2007, one term jumped out over all the rest, and it isn’t a four-letter acronym–it’s customization. And in 2008, most agree, it will continue to prevail as the industry’s favorite buzzword. The customization of algorithms is far from becoming yesterday’s news anytime soon.
“Customization isn’t even a trend–it’s the driving force now, and fewer and fewer clients want a one-size-fits-all solution,” says Carrie. “Nearly every client we talk to wants variation on what we offered them the previous day. And even within the same client institution, there are traders that trade differently, and they want special parameters exposed. Very often traders want an algorithm to behave differently under different conditions, and when they do that often enough, it becomes a customized algorithm for them.”
Over the last year, the sellside has been developing its strategy around customization, agrees Adam Sussman, senior analyst at the Westborough, Mass.-based TABB Group, noting that there were one or two firms that formally launched marketing and sales efforts around the concept in 2007.
“I think we’re really going to see [customization] come to the forefront in ’08,” he says. “It’s going to be one of the top trends moving into ’08, and there are a couple of different reasons for that. One reason is that it’s difficult for the sellside to get the ear of the buyside these days, as more and more trading becomes electronic. Customization has been a pretty powerful tool for a number of banks to re-engage the client.”
One aspect of that will be the shift to clients self-customizing their own algorithms, says Carrie. “Another emerging trend will be products that tie in algorithms much closer to the alpha-generating process and the emergence of algorithmic management systems to improve workflow for trading,” he explains.
Aite’s Bailey asserts that the industry is still only in the early days of customization. “People are going to demand more and more out of the algorithms,” he says. “They’re going to demand more metrics around the trading and more integration of pre-trade and post-trade analysis.”
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