Foreign exchange transaction method and system based on deep enhanced learning algorithm

Abstract

The invention discloses a foreign exchange transaction method and system based on a deep enhancement learning algorithm. The method comprises the steps of (1) establishing an enhanced learning Double-DQN model for a forex trading scene, (2) constructing a deep neural network model and training the enhanced learning Double-DQN model, and learning a Q function, (3) loading trained model parameters,using training data sets of different features, repeating step (2) to perform superposition training and parameter fine-tuning, adjusting the period of model update according to needs, regularly carrying out superposition training on the data sets of different features, and continuously training the model by using data sets of new features such that the model has better scalability and robustness.According to the method and the system, the model can make effective forex trading actions in different data environments, different training sets are used to perform superposition training on the model, and so that the model can be more robust in the context of complex forex data streams.

Description

technical field [0001] The present invention relates to the technical field of deep learning and enhanced learning in machine learning, in particular to a foreign exchange trading method and system based on a deep enhanced learning algorithm, based on the application of the deep enhanced learning algorithm Double-DQN in the foreign exchange trading platform Metatrader4, and at the same time Asynchronous message queue ZMQ is also needed for cross-platform process communication as the technical support for real-time smart transactions. Background technique [0002] Traditional machine learning methods are used in financial transactions, usually a process in which supervised algorithms learn the characteristics of data. A common method is to construct multi-featured training data. First, mark the training data based on experience or financial indicators, then use supervised classification algorithms for modeling and learning, and finally test on the test set. Supervised machin...

AI Technical Summary

Problems solved by technology

However, there may be various potential factors behind the financial data, including random factors of artificial regulation, national policy factors, economic development factors, etc. The pursuit of a single profit cannot bring long-term profits

At the same time, under the influence of complex latent factors, the ability of traditional machine learning feature extraction is extremely limited, and a higher level of abstraction is needed to better learn data features

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