Abstract: The dollar treasury and futures market is arguably the most liquid and actively traded market in the world. On a daily basis, this market generates hundreds of millions of records of data, and the set of securities involved amounts to thousands. The environment is dynamic, interrelated, and fast-paced, and liquidity conditions are constantly changing. As participants engage in strategies, enter and exit this marketplace, certain relationships capture the attention of various participants, who apply capital to their (sometimes machine-learned) convictions. Moreover, correlations are non-static and exhibit a term structure. Instead of making a single static model, we will explore how using Multi-GPU setups and this large streaming dataset, you can set up an online machine learning environment where thousands of strategies can be monitored and pockets of available liquidity uncovered.
Bio: Peter works in the Rates Trading group at Citi. He focuses on machine learning for the implementation of pricing and risk analytics. Peter has developed neural net applications for natural language processing, as well as probabilistic graphical models for pricing. Peter joined Citibank’s Fixed Income Algo trading group in 2011. This team has deployed the largest bank systematic trading and execution platform for treasuries and bond futures.