A risk management system based on offline reinforcement learning

Through the offline reinforcement learning risk management system, offline data sets and supervised learning models are used to optimize risk management strategies, which solves the problems of insufficient risk consideration over a long period of time and high trial and error costs of strategies, and achieves efficient risk management decisions.

CN119941408BActive Publication Date: 2025-10-10XQUANT TECH CO LTD
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Patent Information

Application Number
CN202411693768.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Priority Date
2023-11-28
Filing Date
2024-11-25
Publication Date
2025-10-10
Estimated Expiration
2044-11-25

AI Technical Summary

Technical Problem

Existing intelligent risk management methods do not adequately consider risks over long periods of time, and risk management based on reinforcement learning has high trial-and-error costs and low interaction efficiency in real environments.

Method used

A risk management system based on offline reinforcement learning is adopted. Through offline dataset generation, sequence data generation, risk reconstruction and minimum risk strategy generation modules, supervised learning models and deep neural networks are used to optimize risk management strategies, reduce interactions with the real environment, and improve strategy optimization efficiency.

Benefits of technology

Without interacting with the real environment, an efficient and feasible intelligent risk management strategy is optimized, which improves the function convergence and decision accuracy under dynamic uncertainty.

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Abstract

The application discloses a risk management system based on offline reinforcement learning, comprising: an offline data set generation module: generating an offline data set according to a screening strategy and a four-tuple data format; a sequence data generation module: training a supervised learning model according to the offline data set, and generating sequence data according to the supervised learning model; a risk reconstruction module: calculating a risk adjustment value according to the change value of the sequence data and training data, and reconstructing a risk function according to the risk adjustment value; the training data comprises any one or a combination of multiple items of training times, training time, training completion degree; a minimum risk strategy generation module: calculating a minimum risk value according to the reconstructed risk function, inputting the minimum risk value into the supervised learning model, and obtaining a minimum risk strategy function. The application solves the problems of high policy trial and error cost, low interaction efficiency with the real environment and low policy optimization efficiency.
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