Eric Horesnyi on High Frequency Trading and how Hedge Funds are Applying Deep Learning to Markets
Published March 24, 2017
30 min
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    Eric Horesnyi, CEO, talks to Charles Humble about how hedge funds are applying deep learning as an alternative to the raw speed favoured by HFT to try and curve the market. Why listen to this podcast: - was originally built for banks and brokers, but more recently hedge funds have begun using the service. - Whilst Hedge Funds like Renaissance Technologies have been using mathematical approaches for some time deep learning is now being applied to markets. Common techniques such as gradient descent and back propagation apply equally well to market analysis. - The data sources used are very broad. As well as market data the network might be using, sentiment analysis from social networks, social trading data, as well as more unusual data such as retail data, and IoT sensors from farms and factories. - By way of contrast High Frequency Trading focusses on latency. From an infrastructure stand-point you can play with propagation time, Serilization (the thickness of the pipe), and Processing time for any active component in chain. - One current battleground in HFT is around using FPGA to build circuits dedicated to feed handlers. Companies such as Novasparks are specialists in this area. More on this: Quick scan our curated show notes on InfoQ Subscribe: Like InfoQ on Facebook: Follow on Twitter: Follow on LinkedIn: Want to see extented shownotes? Check the landing page on InfoQ: You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development.
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