Trading desks are being inundated with tsunamis of streaming data. These ticks and codes and numbers tell a story: they contain critical information that can help firms execute better trades and capture more value – for themselves and their clients. But how do you read that story when its coming at you fast and furious in fragments and waves? How do you know what it means, and when and how to act on it?
Real-time analytics tools can help traders see and read the trading narrative. Streaming data enrichment, performance metric calculations, and, especially, anomaly alerts, now deliver actionable data intelligence faster and better than traditional methods. They are the best tools for helping traders meet their regulatory, profitability, and customer service requirements.
Fund managers must use real-time streaming data effectively. Customers on the trading desk send orders to brokers, who must make critical decisions before and during the trade. This requires continuous feedback about how that trade is performing.
The end-of-day reporting that firms have traditionally relied upon is no longer enough – as a customer once said, you wouldnt decide to cross a busy street based on a picture of that crosswalk taken yesterday.
And its not very helpful to tell a client that you had problems executing a trade after the order has been fulfilled. Firms need trades to be visible in real time. They must be able to identify problems and notify clients about them – and, ideally, to communicate solutions to those problems – while the order is still being executed. Real-time analytical tools make such responsiveness possible.
Real-time analytics are enabling an increased transparency in execution quality across the industry. Historic views of trading data support investigation, back-testing, and optimization of trading processes. Visual displays showing events tick by tick – nanosecond by nanosecond with sequence IDs – were once available only in the surveillance department but today are used on the trading floor as well, where they enable quantitative analysts to investigate market microstructures throughout the trading day.
But theres more that real-time intelligence can deliver. For instance, fund managers routinely evaluate broker performance and use algorithms to determine which brokers are implementing the best trading strategies. Real-time intelligence from streaming data allows managers to monitor this activity, to determine what is driving performance, and to identify opportunities to optimize trade execution quality.
They might, for example, choose to reduce the weighting of certain instruments while increasing the weighting of others. Fund managers might reasonably be willing to take on more risk for an extra 3% of return, but without real-time intelligence, its difficult to adjust modeling parameters to rebalance a portfolio to achieve that kind of peak performance.
Critically, real-time streaming analytics make predictions based on historical data and then continuously compare real-order execution metrics to those predictions. In this way, they reveal the anomalies, the unusual events that can give you an edge – or show you the edge you want to step back from. When everything goes as expected, theres not much to see. But in the real world there are always issues to resolve and opportunities to exploit. For example, imagine that the intraday Value at Risk (Var) exceeds the three-day VaR. The portfolio manager must be able to see that this problem exists, examine the intraday trading data to discover exactly what is driving the VaR spike, and work out the best approach for bringing VaR back into line.
Finally, data visualizations, such as those shown below, are key. The most effective analytical tools will paint a picture by transforming raw lines of data into something clear, comprehensible and actionable.
Peter Simpson is VP of Streaming Analytics at Datawatch, an Altair Company
