Outlook 2017: Louis Lovas, OneMarketData
Louis Lovas is director of solutions at OneMarketData.
What is the most important lesson of 2016?
The increased use of cloud computing by firms both on the private and public cloud has been visible in 2016. Infrastructure and platforms have matured, with more users looking to outsource the next level of services – key business solutions for reduced operational and software costs. Machine learning has surged in quant finance and will continue to advance in 2017 with many of its capabilities not yet explored especially within cloud infrastructures. It will be interesting to see how and if elastic analytics for machine learning can be used for algo trading, market surveillance as a predictive tool and how the role of humans on the trading floor will be impacted.
Increasing competition and thinning margins are pushing the technology envelope in the hunt for alpha. This has manifested itself on many fronts, increasing sophistication in the tools to search for alpha, controlling costs and managing risk to the confluence of the underlying infrastructure. The key enablers are big data and cloud deployments where low latency is the ante to play the game.
Trading firms operate in a fiercely competitive industry where success is measured by profit. They are constantly hunting for talent and technology to achieve that goal. Yet firms are ever threatened by fierce competition and controlling costs. The side effect of this is increasing demands for deep data over longer time periods across a multiplicity of markets. This data dump is the fuel feeding automation technology and centers around two points, managing scale and a focus on solutions.
As firms look to capture and store more and more data from many sources across many asset classes it places enormous demands on IT infrastructure. The improvements in compute power and storage per dollar make the consumption both technically and economically possible. Cloud deployments provide advantages in managing the scale through higher levels of data protection and fault tolerance at a cost saving.
Leveraging this big data dump is the fuel that drives the engine across the entire trade lifecycle. It begins with alpha discovery moves to trading algorithm design, development, and back-test and also includes cost and risk management.
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