Morgan Stanley Services Group Inc.

Richard Huddleston

Ahmed Jedda

Joseph Edward Pendergast, Jr

Aditya Damodaran

Erhan Senlik

Joseph Pendergast

A system for detecting a communications computer network node malfunction by analysis of network traffic output by the network node. Low latency packet capture nodes copy network traffic and transmit it to an analytics engine, which may use machine learning techniques, including long short-term memory (LSTM) neural networks, to determine a likelihood that the output of one data router in a network is suffering from a software malfunction, hardware malfunction, or network connectivity issue, and preserve overall data quality in the network by causing cessation of traffic by the malfunctioning node of the network.

Faulty distributed system component identification

A system for network message microtiming, comprising a central routing device, a first mesh network, and a second mesh network. The central routing device continuously receives utilization data from one or more devices of the first mesh network; continuously receives timing data from one of more devices of the second mesh network; builds and continuously updates a probabilistic future latency model based at least in part on the utilization data and the timing data; receives a message from a client computing device comprising data to be sent to at least one of a plurality of remote computing devices via the first mesh network; determines that a current expected latency according to the future latency model exceeds a predetermined threshold; and as a result delays transmission of a packet comprising the data to be sent to at the least one of the plurality of remote computing devices.

Latency prediction and network message microtiming

A system that can detect when abnormal trading activity is occurring and take action to halt the detected abnormal trading activity without human intervention using a computer-implemented anomaly detection and action stage computer that performs an exponential weighted averaging of trade order flow, on a per symbol basis within a sliding volume based window and a volume based exponential weighted averaging, on a per symbol basis, on trade update messages received during the sliding volume based window and checks for an inflection in covariance between them.

Trading anomaly kill switch

A system that can detect when abnormal trading activity is occurring and take action to halt the detected abnormal trading activity without human intervention using a computer-implemented anomaly detection and action stage that performs an exponential weighted averaging of trade order flow, on a per symbol basis within a sliding volume based window and a volume based exponential weighted averaging, on a per symbol basis, on trade update messages received during the sliding volume based window and checks for an inflection in covariance between them. A related method is also described.

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Jericho, NY, United States of America

Richard Huddleston