Order Depth View Visualization for Spoofing Detection
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Solution Overview
Problem
Electronic exchange systems face challenges in reliably and efficiently detecting market manipulation techniques like spoofing due to the complexity of analyzing large volumes of order data and trade data, leading to time-consuming and unreliable methods for identifying abnormal patterns.
Innovation Solution
A computer system and method that correlates broker order data and executed trade information to generate a visual representation of order spread, trade price, and order book depth over time, allowing for simultaneous display and detection of predetermined patterns, such as spoofing attempts, through graphs aligned on a shared time axis with varying color or intensity representing order book depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts manually review rows of order data and trade data to spot unusual patterns, then measurement precision can be maintained, but loss of time increases significantly
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated computer-based system that uses algorithms to detect patterns in order and trade data. The system automatically analyzes large volumes of data, identifies unusual patterns, and generates alerts, eliminating the time-consuming manual inspection while maintaining detection accuracy through systematic computational analysis.
Solution Approach 2:
The patent creates visual representations (graphs and charts) that copy and simplify the complex data patterns into easily interpretable formats. These visual copies allow analysts to quickly grasp market manipulation patterns without manually examining raw data rows, significantly reducing analysis time while preserving the ability to detect abnormalities.
2Reliability
If conventional techniques are used to analyze large volumes of order data and trade data, then reliability of detection can be maintained, but productivity decreases due to time-consuming analysis
Solution Approach 1:
The patent replaces manual analytical processes with automated computational systems that continuously monitor order and trade data. The system applies predefined detection algorithms and patterns to identify market manipulation, maintaining reliability through systematic analysis while dramatically improving productivity by processing large data volumes instantly rather than through time-consuming manual review.
Solution Approach 2:
The system performs self-service by automatically detecting patterns, generating alerts, and providing analysis results without requiring manual intervention for each data set. The automated system serves its own analytical needs, continuously monitoring market data and identifying manipulation patterns independently, thereby maintaining reliability while enhancing productivity.
3Measurement precision
If detailed manual analysis of order data is performed to ensure accurate detection, then measurement precision is improved, but device complexity increases due to the complexity of analyzing large data volumes
Solution Approach 1:
The patent extracts and isolates specific detection patterns and anomalies from the complex data set, focusing analysis on relevant indicators of market manipulation rather than examining every detail of the raw data. By extracting key patterns and presenting them in simplified visual formats, the system maintains measurement precision while reducing the effective complexity of the analysis process.
Solution Approach 2:
The patent introduces visual representations (graphs, charts, and displays) as intermediary elements between the complex raw data and the analyst. These visual intermediaries translate complex data patterns into easily interpretable formats, maintaining detection accuracy by preserving essential information while reducing the perceived and processing complexity for analysts.
4Ease of operation
If conventional manual methods are used for detecting market manipulation, then ease of operation can be maintained for simple cases, but reliability decreases for complex high-volume trading scenarios
Solution Approach 1:
The patent replaces manual analysis methods with an automated system specifically designed to handle complex high-volume trading scenarios. The system maintains ease of operation by providing automated alerts and visual presentations that are simple to interpret, while simultaneously improving reliability through its ability to process and analyze large volumes of data systematically, detecting patterns that would be difficult or impossible to identify through manual review.
Data Source
AI summary
An example computer system is configured to access broker order data and executed trade information in a memory, and to correlate the executed trade information and broker order data. The computer system is further configured to generate a view arranged to simultaneously display order spread, trade price for executed trades, and order book depth over a time period, based upon the correlated data. The broker order data represent orders transmitted by an external computer to an electronic exchange to trade in a tradeable instrument. The executed trade information includes information on trades executed by the electronic exchange based on the said orders.