Dynamic Execution Probability Model for Limit Orders
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Solution Overview
Problem
Existing systems for determining the execution probability of a limit order in financial markets are static and non-adaptive, failing to effectively utilize historical trade data and current market conditions to provide accurate predictions.
Innovation Solution
A dynamic and adaptive model that calculates execution probability based on past trade data, including frequency of trades, price movements, and volume distributions, using mathematical models like Brownian motion and Monte Carlo simulations, integrated with current market state data to infer execution probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static econometric modeling or direct limit order book modeling is used, then the system structure is simple, but the execution probability prediction accuracy deteriorates due to inability to adapt to changing market conditions
Solution Approach 1:
The patent transforms the static modeling approach into a dynamic one by continuously updating the execution probability model using real-time limit order book data and historical trade data. The system adapts to changing market conditions by recalibrating parameters and retraining the model, making the prediction system dynamic rather than static.
Solution Approach 2:
The system implements feedback mechanisms by using actual trade execution outcomes to update and refine the execution probability model. The limit order book data and historical trade data provide continuous feedback loops that allow the model to learn from past performance and improve future predictions, resolving the accuracy-precision contradiction.
2Measurement precision
If dynamic and adaptive modeling with historical trade data is used, then the execution probability prediction accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex modeling task into distinct components: limit order book data processing, historical trade data analysis, feature extraction, model training, and prediction generation. This segmentation allows each component to be optimized independently and processed in manageable stages, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing historical trade data and limit order book data in structured formats before actual prediction is needed. This includes pre-calculating features, organizing data structures, and preparing training datasets, which reduces computational burden during real-time prediction operations.
3Adaptability or versatility
If real-time limit order book data and historical trade data are integrated, then the adaptability to market conditions improves, but the data processing time and system resource requirements increase
Solution Approach 1:
The patent implements continuous data processing and model updating mechanisms that operate continuously in the background, maintaining readiness for real-time predictions. The system continuously ingests limit order book data and historical trade data, updating the execution probability model without interrupting its adaptive capability, thus minimizing processing delays while maintaining market condition adaptability.
Data Source
AI summary
A system, method and computer program product are described for providing the execution probability of a limit order within a given time period based on historical and current information and for adaptively and dynamically adjusting to intra-day trade data. For a given financial interest, the frequency of trade execution, the time evolution of the price, the time evolution of the trade volume, and the current state of the market, among other parameters, are captured and/or calculated. A probability function is generated based on the parameters corresponding to various time spans, and the execution probability of a limit order within a given time period is provided. Embodiments of the invention can be employed to estimate the probability of a limit order being executed within a given time period in the future, e.g., the next two minutes, based on the parameter data of a given time period in the past, e.g., the previous five minutes.