RFQ Pricing Control with Reinforcement Learning and Symbolic Regression
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Solution Overview
Problem
Existing RFQ pricing systems lack the ability to dynamically adjust pricing strategies to minimize tracking error with respect to target metrics in financial markets, particularly in the context of market-making entities, and require a method to optimize pricing using reinforcement learning and symbolic regression for increased interpretability.
Innovation Solution
A method utilizing reinforcement learning and symbolic regression to formulate a Markov Decision Process (MDP) for defining a pseudo-optimal nonlinear state-feedback controller, incorporating a Monte-Carlo simulation to tune the RFQ pricing controller, and applying a predetermined neural network for algebraic approximation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning and symbolic regression are used to create a pseudo-optimal nonlinear state-feedback controller, then tracking error is minimized and pricing strategy adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the pricing controller into distinct functional modules: a reinforcement learning agent for strategic decision-making, a symbolic regression component for mathematical model extraction, and a neural network for algebraic approximation. This modular architecture allows each component to handle specific aspects of pricing optimization independently, reducing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The patent introduces an intermediary algebraic approximation layer between the complex reinforcement learning controller and the pricing output. This intermediary component simplifies the controller's behavior by finding polynomial representations that capture essential pricing dynamics, making the system more interpretable and easier to implement while preserving optimal pricing strategy adaptability.
2Manufacturing precision
If a pseudo-optimal nonlinear state-feedback controller is used to minimize tracking error, then pricing precision is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system dynamically changes parameters based on market conditions by using reinforcement learning to adapt controller behavior in real-time. The symbolic regression component extracts mathematical relationships from observed market data, allowing the controller to adjust pricing parameters optimally without requiring complex manual tuning, thus achieving high pricing precision with manageable complexity.
Solution Approach 2:
The patent creates a simplified algebraic approximation copy of the complex reinforcement learning controller's behavior. This approximation captures the essential pricing logic in a more manageable mathematical form, reducing computational requirements while maintaining pricing precision. The approximation serves as a simplified model that can be executed more efficiently than the full reinforcement learning agent.
3Speed
If reinforcement learning algorithms are applied to tune the RFQ pricing controller, then responsiveness to market dynamics is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training the reinforcement learning agent in simulation environments before deploying to live markets. The symbolic regression component pre-extracts mathematical models from training data, and the algebraic approximation is pre-computed. This preliminary preparation allows the actual pricing controller to respond quickly to market dynamics without requiring intensive real-time computational resources.
Solution Approach 2:
The patent creates a simplified algebraic approximation copy of the reinforcement learning controller's decision-making process. This approximation preserves the responsive behavior learned during training but executes much more efficiently, reducing real-time computational requirements while maintaining speed of response to market changes.
Data Source
AI summary
A method and a system for dynamic request for quotation (RFQ) pricing using reinforcement learning and symbolic regression in order to obtain a pseudo-optimal nonlinear state-feedback controller for tracking a metric with increased interpretability are provided. The method includes: receiving bid price information and ask price information that relates to a financial instrument; selecting a metric to be used in conjunction with a determination of an RFQ price with respect to the financial instrument; formulating a Markov Decision Process (MDP) that relates to the determination of the RFQ price; using the MDP to define an RFQ pricing controller with respect to the metric; applying a reinforcement learning algorithm in order to tune a behavior of the RFQ pricing controller with respect to the metric; and using the RFQ pricing controller, the first information, and the second information to determine the RFQ price.