Hybrid Quantum-Classical Model for Order Book Liquidity Clustering
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Solution Overview
Problem
Existing systems face challenges in predicting which side of an order book market liquidity will cluster, leading to inefficiencies in transaction execution and uncertainty in selecting optimal execution prices.
Innovation Solution
A hybrid approach using a blended or stacked model that combines classical computing and quantum computing to predict the clustering of market liquidity, employing machine learning techniques such as Quantum Bayesian, recurrent neural networks, and extreme gradient boosted trees to determine the probability of directionality of price movement and inform execution strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical machine learning models are used to predict order book liquidity clustering, then the system can process data using conventional computing resources, but the prediction accuracy and speed are insufficient to capture rapid market changes
Solution Approach 1:
The patent combines quantum computing with classical machine learning models to create a hybrid system. Quantum circuits are integrated into the training process of classical models (such as Random Forest, Gradient Boosting, and Neural Networks), allowing the system to leverage quantum parallelism for feature processing while maintaining the interpretability and data handling advantages of classical algorithms. This merging enables both high prediction accuracy and improved processing speed.
Solution Approach 2:
The system dynamically adapts between quantum and classical computing resources based on the specific prediction task and market conditions. The hybrid architecture allows flexible allocation of computational work, using quantum circuits for complex pattern recognition in high-dimensional order book data while using classical models for faster inference when appropriate. This dynamic approach optimizes both accuracy and speed according to real-time requirements.
2Productivity
If quantum computing is used to improve prediction speed and accuracy, then the system can process market data faster, but the device complexity and implementation difficulty increase significantly
Solution Approach 1:
The patent introduces quantum circuits as an intermediary layer between raw market data and classical machine learning models. These quantum circuits process high-dimensional features from order book data through quantum parallelism, then map the results back to classical computational formats. This intermediary approach enables quantum speedup without requiring a complete quantum computing infrastructure, thus reducing overall system complexity while maintaining productivity benefits.
Solution Approach 2:
The computational pipeline is segmented into distinct quantum and classical components. Quantum circuits handle specific feature transformation and pattern recognition tasks where quantum advantage is most pronounced, while classical models handle data preprocessing, post-processing, and inference. This segmentation allows the system to incorporate quantum computing benefits without requiring the entire system to be quantum, thereby managing complexity while improving processing speed.
3Measurement precision
If more sophisticated machine learning models are deployed to predict liquidity clustering, then prediction accuracy improves, but the computational resources and training time required increase
Solution Approach 1:
The hybrid quantum-classical architecture merges the strengths of both computational paradigms to achieve high prediction accuracy with reduced resource consumption. Quantum circuits efficiently process high-dimensional order book features and identify complex patterns that would require significantly more classical computational resources. This combination enables sophisticated modeling without proportionally increasing energy consumption, as the quantum component handles the most computationally intensive pattern recognition tasks.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for predicting or determining the side of an order book that market liquidity will cluster at a future time period to facilitate optimization of spread capture. The method may include receiving market tick data. The method may include receiving an order book. The method may include in response to reception of the market tick data generating a probability or outcome indicating which side of the order book will cluster, determining an execution strategy based on the probability or outcome, and performing the execution strategy in relation to the order book.