RFQ Pricing Control Using PID and Bayesian Optimization
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Solution Overview
Problem
Existing RFQ pricing systems struggle to dynamically adjust pricing strategies to minimize tracking error with respect to target metrics such as hit ratio, profit, market share, and risk levels, particularly due to challenges in robustness, parameter tuning, and constraint incorporation.
Innovation Solution
Implementing proportional-integral-derivative (PID) control with Bayesian optimization to determine an optimal linear state-feedback controller, utilizing a Monte-Carlo simulation to tune PID controller gains for robustness and constraint adherence, minimizing tracking error through Gaussian Process regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional RFQ pricing methods are used, then the system is simple to operate, but the tracking error with respect to target metrics cannot be minimized effectively
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors actual metric performance (hit ratio, profit, market share, risk) and adjusts RFQ pricing in real-time based on deviations from target values. The controller receives feedback on tracking error and modifies pricing strategies accordingly, enabling dynamic optimization of metric performance while maintaining operational clarity through automated closed-loop control.
Solution Approach 2:
The system dynamically adjusts multiple pricing parameters including bid-ask spread, quotation frequency, and price levels based on optimized control algorithms. By changing these parameters in response to market conditions and metric performance, the system achieves precise tracking of target metrics while managing the complexity through systematic parameter optimization rather than ad-hoc adjustments.
2Reliability
If dynamic pricing adjustment is implemented to minimize tracking error, then the responsiveness and accuracy improve, but the computational complexity and parameter tuning difficulty increase
Solution Approach 1:
The system employs self-service through automated parameter optimization where the controller independently tunes its own parameters based on observed metric performance. The optimization algorithms automatically adjust control parameters without requiring manual intervention, enabling the system to self-optimize pricing strategies and maintain high reliability while reducing the operational burden of parameter tuning.
Solution Approach 2:
The patent implements dynamic pricing adjustment where the controller continuously adapts its parameters in real-time based on market conditions and metric performance. This dynamic approach allows the system to respond flexibly to changing conditions, optimizing pricing accuracy while managing complexity through automated adaptive control rather than static predetermined rules.
3Reliability
If robustness and constraint adherence are prioritized through Monte-Carlo simulation, then the system reliability improves, but the computational time and simulation complexity increase
Solution Approach 1:
The system performs preliminary action by conducting Monte-Carlo simulations during the optimization phase to pre-evaluate robustness and constraint adherence before implementing pricing strategies. By performing these simulations in advance to determine optimal controller parameters, the system ensures reliability and robustness while minimizing the need for continuous computational intensive simulations during actual operation, thus managing computational time effectively.
Data Source
AI summary
A method and a system for dynamic request for quotation (RFQ) pricing using proportional-integral-derivative (PID) control with Bayesian optimization in order to determine an optimal linear state-feedback controller for tracking a metric 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; estimating an output of a PID controller based on a predetermined function of the metric; adjusting the estimate by using a Bayesian optimization process; and using the PID controller, the adjusted estimate, the bid price information, and the ask price information to determine the RFQ price.