The Gateway to Algorithmic and Automated Trading


Published in Automated Trader Magazine Issue 29 Q2 2013

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."

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