Implied Order Quality Indicator for Electronic Trading
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Solution Overview
Problem
Current electronic trading systems lack the ability to differentiate between the quality of implied orders, exposing traders to risk by treating all implied orders equally, which can lead to missed opportunities and suboptimal pricing.
Innovation Solution
The system determines and provides an indicator of the quality of implied orders based on factors such as generation, origin, number of orders, and price level, allowing traders to filter and rely on orders with higher reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all implied orders are shown and utilized for trading, then the quantity of trading information is increased, but the reliability of individual implied orders deteriorates due to inability to differentiate quality
Solution Approach 1:
The patent segments implied orders by assigning quality indicators that categorize them into different reliability levels. Each implied order is evaluated based on multiple factors (number of source orders, generation depth, price level consistency) and assigned a quality score, allowing traders to segment and filter orders by quality threshold rather than treating all implied orders uniformly.
Solution Approach 2:
The patent applies local quality by assigning different quality characteristics to different implied orders based on their specific attributes. Instead of uniform treatment, each implied order receives a localized quality assessment based on its unique combination of source order count, generation level, and price level metrics, enabling traders to identify high-quality opportunities in specific locations of the market data.
2Quantity of substance
If implied orders from multiple sources are used, then the quantity of implied orders is increased, but the complexity of determining reliability deteriorates
Solution Approach 1:
The patent transforms the complex multi-factor reliability assessment into a simplified parameter system. By defining specific parameters (number of source orders, generation depth, price level variance) and assigning weights to each, the system converts complex qualitative judgment into quantitative parameter changes that can be automatically calculated and compared, reducing the perceived complexity for traders.
Solution Approach 2:
The patent introduces quality indicators as an intermediary layer between raw implied order data and trading decisions. These indicators act as mediators that aggregate multiple complex factors (source order counts, generation levels, price variations) into a single interpretable metric, simplifying the interface between complex data and user decision-making without losing important reliability information.
3Reliability
If traders filter implied orders by quality, then the reliability of selected orders is improved, but the quantity of available trading opportunities deteriorates
Solution Approach 1:
The patent implements dynamic quality filtering that adapts to trader needs and market conditions. Traders can dynamically adjust quality thresholds based on their risk tolerance and trading strategy, and the system dynamically recalculates which orders meet the criteria. This dynamic approach allows traders to optimize between reliability and quantity in real-time rather than being locked into fixed filtering rules.
Data Source
AI summary
Certain embodiments of the present inventions provide implied order quality. The quality may be viewed as an indication of how much an implied order and/or an aggregate quality for implied orders may be relied upon. Certain embodiments utilize various techniques for determining a quality for an implied order. Certain embodiments utilize various techniques for determining an aggregate quality for implied orders. Certain embodiments provide an indicator of the quality for an implied order and/or of the aggregate quality for implied orders. Certain embodiments filter an implied order based on a determined quality value and/or determined aggregate quality.