Order Book Snapshot Analysis for Missed Trade Reconstruction
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Solution Overview
Problem
Analyzing large data sets retrospectively to determine how altering the order of data entries could change outcomes is computationally intensive and challenging, particularly in systems like power grids or electronic exchanges.
Innovation Solution
A computer system processes a data structure with entries containing order book snapshots and actions, selecting failed orders and determining the closest time entry where they could have matched, recording time deltas and quantities for potential matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If retrospective analysis of large data sets is performed to determine how altering the order of data entries could change outcomes, then insights for improvement are gained, but computational resources and time are excessively consumed
Solution Approach 1:
The system performs preliminary actions by maintaining chronological snapshots of the order book state at each timestamp in the data stream. When analyzing a failed order, the system can immediately reference pre-computed snapshot data from earlier timestamps without performing heavy computational analysis of the entire historical data set, thus reducing retrospective analysis time while preserving complete trading opportunity information
Solution Approach 2:
The system extracts only the necessary data elements for retrospective analysis - specifically, chronological snapshots of order book state containing bid/ask prices, quantities, and timestamps - rather than processing entire large data sets. This extraction approach retains all information about missed trading opportunities while eliminating unnecessary computational overhead from unrelated data
2Measurement precision
If complete order book snapshots are stored for every state change to enable accurate retrospective analysis, then analysis accuracy is improved, but data storage requirements increase significantly
Solution Approach 1:
The system applies local quality by storing complete order book snapshots at every chronological timestamp in the data stream, ensuring that each snapshot contains precise local state information (bid/ask prices, quantities, timestamps) needed for accurate retrospective analysis of failed orders at that specific moment, while avoiding storage of redundant global data
3Reliability
If the system analyzes every possible time point to determine when a failed order could have matched, then completeness of analysis is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary action by pre-processing the data stream into chronological snapshots organized by timestamp during the initial data ingestion phase. When a failed order is identified, the system can efficiently query pre-organized snapshots from relevant time periods without scanning entire data sets, ensuring complete identification of all potential match opportunities while maintaining high processing speed
Solution Approach 2:
The system segments the continuous data stream into discrete chronological snapshots at each timestamp, creating manageable time-based segments that can be independently analyzed. This segmentation allows the system to focus computational resources only on relevant time segments containing failed orders and their potential match opportunities, rather than processing the entire data set uniformly
Data Source
AI summary
A computer system is provided for retrospectively processing a data structure that includes a plurality of entries. The computer system determines if certain data transactions requests that have been recorded in the data structure could have been executed differently (e.g., by being processed at an earlier point in time). For a given entry in the data structure, the system determines if data transaction request could have at least partly succeeded against a prior recorded state of at least one of two ordered lists of pending data transaction requests. Another entry is then found that caused the initial entry to fail in execution and a time delta is stored between the timestamp of the another entry and the timestamp of the initial entry.