Bayesian Machine Learning on the Limit Order Book

Access to the historical limit order book for all global liquid securities at L3 resolution along with associated metadata. This AMI contains the interface to this AWS hosted multi-petabyte dataset, along with the software framework required to perform analysis on it. This framework integrates use of big-data tools and applies a world-leading machine learning library designed at Cambridge and MIT universities. The library allows limit order books to be represented as a probabilistic graphical model, enabling pattern recognition at massive scale. Additionally, the library is implemented to p... See more

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Impressive, but still under development

  • By Francois
  • on 11/05/2014

I am a quant in a French investment bank and handle limit order book data on a daily basis, using it to develop algorithms to support our trading efforts.

The authors have built on their academic work to implement an ambitious project. We have been involved as one of their initial customers, providing feedback to the platform. Users need to sign-up with BMLL first and then they log into AWS using an RSA key and access the processed historical data and the suite of algorithms that accompanies it. The available data is growing rapidly, as is the sophistication of the offering. Has the potential to be an excellent resource.

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