Brain-Powered, Finance, and AI: An overview


First Published in Automated Trader Magazine Issue 29 Q2 2013 : Artificial Intelligence

Alan Turing once famously posed the question of whether machines could think. Applied to today’s ultra-competitive markets, the question becomes, can machines think well enough to make money? Anna Reitman reports on what some of the players using artificial intelligence-based systems have to say.

The field of artificial intelligence has numerous iterations and though each has its share of cheerleaders, some players say that claims of superior technology could be more marketing than reality.

Marco Fasoli, co-founder of Titian Global Investments, says his firm uses artificial intelligence (AI) techniques but he also believes there are those firms using language associated with AI to differentiate in a highly competitive and secretive market, where offerings are typically very similar to one another. (For an interview with Fasoli, click here.)

“Machine learning and artificial intelligence is a catch-all phrase. Some firms say they do it but are actually dressing up simple systems,” he says.

Titian uses AI to predict intra-day and daily market price movements. It covers 21 of the most liquid global futures markets across bonds, equities, currencies and commodities. Fasoli notes that while overall performance over the three-and-a-half years of live trading since inception has been strong, commodities have performed particularly strongly.

The firm’s AI system differs from other systems in that it aims to make short-term price predictions of within 24 hours. For each prediction, the system self-generates trading rules for each individual market independently of the other markets, Fasoli says.

“This results in very low levels of correlation between the different market-specific systems, even between systems that are highly correlated markets, and hence in little intra-program directionality.”

For example, despite a close to 90% correlation between the underlying markets of heating oil and crude oil, the Titian systems trading these two markets have a daily correlation of -0.15% over three-and-a-half years of trading. This, says Fasoli, is because for each of the 21 markets covered, the firm uses completely independent and autonomous sets of 600 systems.

The machine learning technologies that work best in financial markets, Fasoli adds, are those that are adaptive and can best behave as reliable ‘universal approximators’. In other words, systems that have the ability to draw on historical information to reliably infer likely future price behaviour when presented with unseen data.

The known unknowns

As data sets grow bigger, the application of AI to ‘big data’ is something the industry is taking notice of with a dose of optimism.

Vincent Kilcoyne, capital markets industry lead at analytics firm SAS, says that going forward, much of what was taken for granted as conventional wisdom is being reconsidered. In the 1990s he specialised in the use of AI to control physical systems through the use of feedback and robotics, and has watched the extent to which models have evolved.

“Nobody knows how to wrestle with the way the world is changing from the point of view of the sheer volume of data,” he says. “There is a wealth of data out there that traditional models either ignore or are completely unprepared to consider.”


Jump Trading to set up dark pool trading team in London, source says

Trading to set up dark pool trading team in London, source says

First Published 13th May 2013

Jump Trading wants to take advantage of opportunities for equities dark pool trading, a source tells Automated Trader–source–says

London – Jump Trading, one of the world’s largest proprietary trading firms, is assembling a trading team in London to take advantage of dark pool trading opportunities in the equities space, according to a person familiar with the matter.

The firm is seeking to connect to a number of dark pools in a bid to arbitrage between different venues by measuring latency differences, which allows traders to predict future price moves on comparatively slower venues.

“This is a really interesting move and other shops could follow suit soon,” said this person.

The company did not respond to a request for comment.

A few minutes with… Spencer Greenberg of Rebellion Research on artificial intelligence.

A few minutes with… Spencer Greenberg of Rebellion Research

First Published in Automated Trader Magazine Issue 29 Q2 2013 : Artificial Intelligence

Automated Trader catches up with Spencer Greenberg of Rebellion Research to hear about their work in artificial intelligence.

AT: How is a longer-term horizon different than what other players are doing?

Spencer: It is a different game when you are in a longer term horizon. You are looking for different types of signals. In our case we look for signals that are more stable, specifically seeking out ones that are likely to be maintained over long periods. You want your algorithm to learn about things that are going to continue applying in the future.

AT: How many variables do you take into account, how large are your data sets?

Spencer: There are thousands of variables in our data set. For a given decision on a given asset or stock, generally it will be a small number because [the system] reduces it down to the relevant ones. Often it will take into account 40 or 50 factors while making a single decision. But it really varies a lot.

AT: Some people point to heuristics as advantageous in building models in financial markets because they are simpler. What do you say to that argument?

Spencer: In some ways heuristics, or rule-based predictions, have nice features. You understand exactly what the rules are because you programmed them to do a certain thing. There also are some disadvantages, however. Rules tend to be static. If there is a market change, unless you go and change your rule it is going to stay the same and may no longer be appropriate. Machine learning is more complex in most cases. But it can have significant advantages. It can learn, it can change, it can update itself automatically if done right; so there are trade-offs.

AT: One of those trade-offs is that complex systems tend to be more brittle. How do you work around that?

Spencer: I think this is less an issue of machine learning per se and more an issue of being good at designing quantitative models. First of all, when designing your model, you need to make sure that what it is relying on is something that is likely to be maintained over time. Second of all, you don’t want to be making money in just one way, generally. If that one thing you are taking advantage of suddenly changes it could be disastrous, so you want to rely on lots of different signals that aren’t related to each other or have multiple ways of making money. That is something that applies across all types of quantitative strategies.

AT: What kinds of strategies do your machines use?

Spencer: Our machine learning automatically learns what strategies to apply. We have a large set of factors it analyses. Its goal is to automatically learn which of these are predictive of future performance, and then for a given asset, to take everything it’s learned and make predictions based on that. There are some firms that use machine learning to generate a strategy, and then they will have that fixed strategy run so the machine learning is more a part of the exploration. In that case, the machine learning is not continually being activated, only used to find an initial strategy. For us it is not like that. Every day our system learns more and updates itself based on that new information.

A few minutes with… Marco Fasoli of Titian Global Investments on artificial intelligence

A few minutes with… Marco Fasoli of Titian Global Investments–investments

First Published in Automated Trader Magazine Issue 29 Q2 2013 : Artificial Intelligence

Automated Trader catches up with Marco Fasoli of Titian Global Investments to hear what they’re doing in artificial intelligence.

AT: What do you say to scepticism about the viability of complex AI systems in financial markets?

Marco: The debate between simple and complex can be framed in many different ways. One way of looking at this is that simple systems tend to not have the characteristics of having [an] adaptive and intelligent nature that we believe is critical for predictive systems. Most systems involved in systematic trading tend to identify trends as opposed to predict price behaviour and are based on simple rules that have been pre-determined by the portfolio manager.

Those simple systems tend to work very well in trending markets. The problem is that if the markets are not trending, for example when they are choppy or bound within a tight trading range, the signals are not as clear, and the systems get caught out. This also happens when the markets reverse very quickly when a trend ends.

AT: What is the difference between trend following and your own adaptive systems?

Marco: What we have been working on and been developing and using are systems that are intelligent in the sense that they are able to change their own rules by themselves, as a function of the changes in the market. If, for example, they see a market that is choppy, they behave more like an oscillating system that is more reactive to short-term changes, not stuck in a single (long or short) position as if there was a strong trend. On the other hand, if they detect trends in the market our systems can recognise this and keep their positions longer, so basically our systems are designed to continuously learn and adapt to changing market conditions based on what they see; and the way they do this is by changing their own rules at each prediction point, so every day. Our systems are also short-term focused. They focus on predicting price behaviour over the next 24 hours and have an average holding period less than four days, whilst trend-following systems hold positions for several weeks or months. Our systems are also market-specific, unlike most trend-following systems.

AT: What is the mathematics/science underpinning your technologies?

Marco: It is very well established in industry outside of the world of finance so, for example, it’s similar to the type of technology that is used in weather forecasting or by the online search engines. If you think of Google when you start to type your name in, Google is giving you a prediction of what you are likely to be searching for, taking into account what you searched for most recently and also what the online search community that best matches your profile has been searching. These types of industrial applications use adaptive machine learning technologies similar to the ones we have developed for financial markets.

[The technologies] all have in common this feature of being able to self-generate the predictive rules at each prediction point, as opposed to being based on fixed or pre-determined rules, which is what the simple systems do.

Some of these technologies work better than others in financial markets. So – just one example – neural networks were a real fad several years ago. A lot of people were talking about neural networks as very promising, and they proved to be a bit of a disaster.

Essentially, if you want to know whether you need to go long/short and by how much, [neural networks provide] a very detailed map of the past that can be very accurate in predicting the future, if exactly the same conditions persist in the future. But if you get something that deviates from the past, which is the norm in financial markets, the systems can get confused and what they predict often amounts to wild guesses, with little or no value – could be right, could be wrong – but their predictive accuracy is not reliable.

Our systems use machine learning technologies that are more reliable as universal approximators. Rather than memorising the past, they attempt to model it in more general forms, capturing only its most essential characteristics, and in this sense they act much more like a compass or a smart sat-nav system in predicting likely future behaviour over a broad area, than a very detailed paper-based, static street-map focused on a narrow strip of territory. For this reason they tend to be much more reliable than neural networks at inferring likely future price behaviour based on unseen data, especially in changing market conditions.

ITG launches Smart Trading Monitor

ITG launches Smart Trading Monitor

ITG launches Smart Trading Monitor

Published on   May 14, 2013

ITG, the execution and research broker, has launched Smart Trading Monitor, the latest feature in the ITG Smart Trading Analytics suite. Smart Trading Monitor is a web-based dynamic TCA tool which enables institutional traders and portfolio managers to track equity trading performance in real-time.

“Smart Trading Monitor is a powerful tool for institutional investors, allowing them to view and track execution performance with a high level of transparency and best-in-class monitoring tools, said Ian Domowitz, Head of Analytics at ITG. “Through Smart Trading Monitor, institutional traders and portfolio managers can now monitor trader and broker execution performance across a broad range of benchmarks and regions, taking into account historical and current market conditions, as well as the statistical deviation between them. The tool provides users the ability to rapidly identify outlier or sensitive trades, drill down for specific context at the broker, venue or security level, and take corrective action if necessary.”

Also commenting on the launch, ITG’s Head of Platforms, Will Geyer, said “Smart Trading Monitor provides a crucial information bridge between portfolio managers, institutional traders and brokers, encouraging dynamic feedback and review of order and execution behavior which can ultimately be used to improve the quality of trade execution decisions and reduce market impact.”

Smart Trading Monitor is powered by order and execution data routed over ITG Net, a global FIX network. Smart Trading Monitor is a flexible web and mobile accessible application, integrated with ITG’s Triton® execution management system and ITG OMS and compatible with most third-party OMS or EMS platforms. Smart Trading Monitor is the latest tool in ITG’s suite of pre- and post-trade analytics which empower traders and portfolio managers to predict costs that may otherwise be overlooked and adjust their tactics prior to and during the trade.

Why should a trading firm try to be an IT firm?

Why should a trading firm try to be an IT firm?

Posted by Sébastien Jaouen on  May 08, 2013


It used to be the case that the more complex your trading infrastructure, the larger your IT team needed to be. Systems specialists, networking gurus, database administrators, telephony and IT support were all needed to manage these systems front-to-back, globally.

Arguably, things have become even more complex in recent years – multi-asset, multi-jurisdictional strategies need to be combined with more onerous regulatory oversight and more demanding client expectations. IT budgets (and staff) can no longer cope. Combine this with increased pressure on spending and bottom line and it’s clear that the equation doesn’t balance.

So what’s to be done? How can trading firms reduce IT spend whilst having the capability to meet current and future demands? Trading infrastructure and connectivity is one area which lends itself being managed outside the firm. For a global institution with multiple platforms located around the world, managing proprietary connections is not the best use of resources. In addition, trading centres rise and fall, today’s Brazil could be tomorrow’s Cyprus and nobody wants to pay for redundant infrastructure.

Some advocate a wholly outsourced or managed solution but this sits a little uncomfortably with firms where security, reliability and accuracy are competitive differentiators. After all, if you’re using the same system as your competitors, where’s the technology advantage?

A more sensible solution is to take an approach that allows you to play to your strengths whilst using external partners to take care of non-core functions. Data centres and connectivity can be outsourced to cloud or managed services providers, saving institutions the headaches of managing multiple carriers across different time zones and SLAs. Economies of scale can be achieved by making use of existing connections whilst connections that are no longer needed do not have to be a burden. Relying on a dedicated partner also means that no network improvement work needs to be done or upgrades performed.

This refocusing of the IT department’s efforts removes the issues involved with running a global infrastructure and allows businesses to concentrate on what makes them competitive. All too important in today’s cost- and regulatory-conscious world.

BondDesk combines fixed income platform with Vestmark’s wealth management solution

BondDesk combines fixed income platform with Vestmark’s wealth management solution–management-solution

First Published 15th May 2013

Fixed income technology and wealth management combined solution to simplify building and managing personalized bond portfolios

New York – BondDesk and Vestmark plan to build a service that will enable financial advisors to provide their clients with personalized bond portfolios which they say will combine the advantages of investing in individual securities with the convenience of investing in bond funds or other packaged products.

This new service will unite BondDesk’s retail fixed income platform, BondWorks, with Vestmark’s wealth management solution, VestmarkONE.

The BondWorks-VestmarkONE solution aims to make individual bond portfolio management accessible to a broader range of financial advisors by bringing managed money capabilities to brokerage accounts.

This combined solution will also review account restrictions pre- and post-trade at the portfolio-level, account-level and firm-level, providing important compliance and suitability checks. Supervision managers can input concentration-type restrictions, issuer restrictions and credit rating restrictions that will either warn or stop the execution of an inappropriate trade.

“Constructing and managing a portfolio of individual bonds can be a complicated and manual process that falls outside the comfort zone of many financial advisors,” said John Lunny, Chief Executive Officer at Vestmark. “With VestmarkONE, sophisticated algorithms do the heavy lifting, enabling advisors to spend more time servicing their clients and growing their book of business.”

Craig Pfeiffer, Head of Wealth Management at BondDesk, added, “In addition to helping advisors attract and retain assets, this new solution will help protect advisors and their firms from costly compliance violations, and by automating many portfolio monitoring and rebalancing tasks, free liaisons to focus on portfolio model construction and other value-added services. BondDesk looks forward to working closely with Vestmark to bring this innovative joint solution to market.”

Saxo Bank announces new trading tool – Trade Navigator

Saxo Bank announces new trading tool – Trade Navigator

Saxo Bank provides trading insight through Trade Navigator

Ole Sloth Hansen, vice president, Saxo Bank

Ole Sloth Hansen, vice president, Saxo Bank

“This new tool is another example of our focus at Saxo Bank on developing usefull tools to facilitate precision trading.”

Saxo Bank has launched Trade Navigator on, a new trading tool that provides daily technical insight into around 200 instruments.

By amalgamating several known and widely used technical indicators into one overview Saxo Bank aims to provide the tools that enable traders a quick look up and guide to where to buy and sell assets but also enable informed decisions and manage risk.

Through the use of pivot points traders can use Trade Navigator to set stops, take profits and make entry points for daily trading on over 200 instruments within Forex, Commodities, Bonds and Equities. The new tool is built around daily pivot points to indicate when to trade but it also uses the relative strength index (RSI), break outs and Bollinger bands:

  • The relative strength index is used together with other technical indicators to signal when a security is overbought or oversold – thresholds that can be set as desired
  • Break outs are based on the Donchian channels that indicate a multi-period trading range – if the price breaks through the multi-period high or low this may confirm that a new trend is in place
  • Bollinger bands are used as a tool for both confirming momentum or a reversion towards the mean
  • Using Average True Range (ATR) which is a measure of volatility the Navigator gives the user the ability to determine what would be an appropriate exposure when initiating a trade.

Ole Sloth Hansen, Vice President at Saxo Bank, comments “Trade Navigator is a vital aid to help our clients refine their trading strategy. With this new tool we now not only provide our clients with the best tools to execute trades but complement these with the best tools to inform their trading activities. This new tool is another example of our focus at Saxo Bank on developing usefull tools to facilitate precision trading.”

CQG becomes approved ISV on DGCX

CQG becomes approved ISV on DGCX

Dubai – CQG, the provider of financial market data and trading software, has become an approved independent software vendor (ISV) on the The Dubai Gold & Commodities Exchange (DGCX). CQG has connected its hosted infrastructure to DGCX to deliver low-latency trade execution and market data service to the Exchange’s members.

Gary Anderson, CEO of DGCX, said: “We are very happy to welcome CQG on board as an empanelled independent software vendor. CQG is a leading financial technology player and innovator and their highly rated product offering and global market coverage adds to the pool of world-class technology expertise available in DGCX’s growing community. Our partnership with CQG stems from our commitment to offer our Members access to the world’s leading trading technology solutions.”

US-based CQG partners with more than seventy Futures Commission Merchants and provides Direct Market Access to more than forty exchanges through its global network of co-located Hosted Exchange Gateways.

“We are well-pleased to provide our customers with hosted connectivity to DGCX, a major exchange in the Middle East with a strong history of growth,” said Mike Glista, CQG’s Director of Order Routing. “CQG’s sophisticated tools offer a high-performance solution to traders seeking to access liquidity in this important emerging market.”

ICAP’s EBS Direct now live with 200 customers & 20 providers

ICAP’s EBS Direct now live with 200 customers & 20 providers–20-providers

First Published 13th May 2013

EBS Direct begins initial trades with Commerzbank, Goldman Sachs, J.P. Morgan and Morgan Stanley

London and New York – EBS, ICAP’s electronic FX business, has announced that EBS Direct is now live with its Beta programme and four liquidity providers, Commerzbank, Goldman Sachs, J.P. Morgan and Morgan Stanley, have all conducted initial trades on the platform.

EBS Direct provides relationship-based disclosed liquidity alongside EBS Market, the company’s anonymous matching platform for spot FX. It enables the streaming of tailored prices direct to liquidity consumers.

EBS Direct has already signed up more than 200 customers in 44 countries and has commitment from a further 20 liquidity providers.

In parallel with the launch of the Beta programme, EBS is also continuing to develop a new order routing infrastructure.

Giovanni Pillitteri, Managing Director, Global Head of ecommerce trading at Morgan Stanley, said: “We are excited to be one of the first providers of liquidity on EBS Direct, further cementing our partnership. We look forward to continuing to deliver the benefits of this new platform to our clients.”

Paul Scott, Managing Director, Global Head of eFX, at Commerzbank, said: “EBS Direct provides excellent distribution in a cost-efficient solution through a trusted platform. We are going to continue working closely with EBS to ensure we maximise our reach to serve our broad range of customers.

Christopher Chattaway, Managing Director, FX Trading at Goldman Sachs said: “Our implementation of the EBS Direct solution represents an important step in increasing liquidity distribution to our customers. We are looking forward to working closely with EBS to further develop this innovative platform.”

Svante Hedin, Managing Director, FX ecommerce trading at J.P. Morgan, said: “We are pleased to be working with EBS Direct to provide liquidity to our customers on a disclosed basis. Our initial trades have gone well and we look forward to increasing the volume of trades with EBS.”

Jeff Ward, Global Head of EBS Direct, said: “We have reached an important milestone in the development of this innovative, cost-effective disclosed liquidity solution. EBS’ global presence, robust footprint and trusted relationship with the market have enabled us to attract great interest in the offering, and we are excited to begin moving into live, active trading. Having now established a solid framework for EBS Direct we will work closely with our customers to ensure we continue to deliver a differentiated offering that meets the market’s evolving needs.”

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