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Te other side of the equation is the development of algorithmic trading. Like equities, FX has been electronic for some time, but in equities there have been more factors to base algos on, making it dominant in terms of their complexity. As algorithms have become more sophisticated, the know-how has flown into the FX market with firms using multiple algorithms with commensurate growth in the measurement of their performance. To increase their FX order flow banks are now adapting these systems for retail clients.
“Some banks are looking at how they can bring their two sides of the FX business together to link retail and institutional flow,” says Grant. “So on the retail side we are starting to see FX provider offer algorithms to their high street customers. Tere are opportunities to reduce costs by improving your ability to trade and the speed at which you can trade based on the strategy you use. For example, who narrows their bid-offer spreads first when there’s an event in the market? Who are the laggards? Tat’s where big data really comes into its own, where organisations can start developing more sophisticated strategies.”
Comply or die
Tis growth in trading has knock-on effects for other areas of the business. Each trade has to be checked by the bank to ensure that it is not exceeding risk positions or is in some way unauthorised. Behnstedt says, “Regulations pose a challenge for banks as they have to manage large volumes of data, for example based on know your customer rules, but they have a silo-oriented approach so there may not be much interaction between the Forex silo, the equities silo and the bond silo. As trading volumes increase so banks are less able to cope. Tey are looking for methods of volume intensive processing; it doesn’t meant that they will move from a position of 10,000 clients to 1,000,000 clients the next day, it is more an issue that transaction volumes might spike.”
Banks already have large structured information stores, data warehouses, but these are really designed
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to be used after the trade has been made, notes Behnstedt. Often used to meet Basel III and reporting requirements, they are not designed to calculate details of the trading operations, such as the average mark- up of trades, or which currencies a client is trading and which he would normally have exposure in. Business intelligence technologies that allow a deeper understanding of this information are in place in firms but not usually attached to data warehouses.
“Business intelligence functionalities which help to analyse data are always held in systems in the middle office, sometimes in the risk management area,” he explains. “By making trading more automated banks have a problem in that they have to cope with the data volume and apply business intelligence. So they need to look for or build something that makes to easier to manage. Te question is whether you can build and support this or outsource it to a provider.”
The wood from the trees
Getting the data processed is well and good but at some point its analysis has to be crystallised into something that FX traders can actually use. Given that the definition of big data is a dataset beyond the capacity of a normal database, what tools can be used for the sake of practicality?
“Tere is understanding data using analysis, which I might call the value of the data, then there is visualisation, which are ways of interacting with the data,” explains Kennedy. “If I have got an enormous quantity of bid-ask trading data how do I, as a human, make sense of that? Visualisation demonstrates that a picture can be worth a thousand words, it allows us to make sense of the numbers in the sense of where the market is going and what is affecting it.”
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