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.

Learn how to compute traditional end of day computations in real time through the use of a hybrid GPU/CPU computing environment. We will detail how computing intensive tasks are delegated to the GPU while interface issues are dealt with by the CPU. We will discuss our methodology consisting of the following three components: (1) valuations; (2) by tenor risk measures; and (3) full distributions allowing for more complex analytics such as exotic options products valuation and counterparty value adjustments calculation.