Fidessa Trading UK Limited

Fidessa Plc

Daniel Nicholass

Bruce Bland

Oleh Danyliv

Christian Voigt

The present disclosure generally provides techniques for analyzing and displaying the order execution quality of market instruments traded during a relevant period, or lifetime, of the order. The utilization of quote information in calculating a normalization factor allows for comparisons across instruments, orders, and days for instruments with a publicly available price irrespective of executed volume. A calculated Z score illustrates the quality of an order execution for a specific traded time as compared to all possible random executions. Moreover, the techniques disclosed herein allow for the comparison of execution quality across market instruments, orders sizes, and other variables via a graphical user interface and other data visualization tools, and can encapsulate evaluation methods using other adjusted Z score thresholds and/or alternatively take into account desirable volume weighting when calculating the standard deviation.

Order execution quality of financial market transactions utilizing an adjusted Z-score benchmark

Techniques for displaying market depth information for a financial instrument including a plurality of bid and ask order metrics at corresponding prices. A graphical representation of the market depth information, with axes corresponding to order metrics and price, is aligned with an expected profile, including an expected metric corresponding to one or more metrics including, for example, bid and ask order volume, order count, or order consideration, generated based on the market depth information. The graphical representation of the market depth information and the expected profile are displayed using a display device.

Systems and methods for trading with market depth information

The present disclosure generally provides techniques for analyzing and displaying the order execution quality of market instruments traded during a relevant period, or lifetime, of the order. The utilization of quote information in calculating a normalization factor allows for comparisons across instruments, orders, and days for instruments with a publically available price irrespective of executed volume. A calculated Z score illustrates the quality of an order execution for a specific traded time as compared to all possible random executions. Moreover, the techniques disclosed herein allow for the comparison of execution quality across market instruments, orders sizes, and other variables via a graphical user interface and other data visualization tools, and can encapsulate evaluation methods using other adjusted Z score thresholds and/or alternatively take into account desirable volume weighting when calculating the standard deviation.

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Carshalton, United Kingdom

Daniel Nicholass