Self-adaptive financial time sequence prediction method based on k-line clustering and reinforcement learning

A financial time series and reinforcement learning technology, applied in finance, data processing applications, character and pattern recognition, etc., can solve problems such as increasing the state space, difficult to summarize market trend characteristics, etc., and achieve the effect of increasing representation ability

Inactive Publication Date: 2018-11-02
SHANDONG NORMAL UNIV
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Problems solved by technology

Deng et al. applied fuzzy learning to price denoising. Through the fuzzification of prices, the noise can be removed to a certain extent, bu

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  • Self-adaptive financial time sequence prediction method based on k-line clustering and reinforcement learning
  • Self-adaptive financial time sequence prediction method based on k-line clustering and reinforcement learning
  • Self-adaptive financial time sequence prediction method based on k-line clustering and reinforcement learning

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Embodiment Construction

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0056] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The invention discloses a self-adaptive financial time sequence prediction method based on k-line clustering and reinforcement learning. The method comprises the following steps: firstly acquiring financial data, performing K linearization processing on the financial data, and computing the data after the K linearization processing, thereby obtaining the K line data in the current matching period;clustering various sub-components of the K line by using the Kmeans clustering algorithm, the FCM clustering algorithm or the online clustering method based on the data density; inputting the clustering result in the deep reinforcement learning model to perform the parameter training, and performing the financial transaction by using the trained deep reinforcement learning. The K-linearization isperformed on the financial data, and various sub-parts of the K line are clustered, the clustering result is input into the deep reinforcement learning model to obtain the deep reinforcement learningmodel based on the decomposing k-line clustering, and the online self-adaptive prediction of the real-time financial transaction price is realized.

Description

technical field [0001] The invention relates to the field of financial transactions, in particular to an adaptive financial time series prediction method based on decomposition k-line clustering and deep reinforcement learning. Background technique [0002] Realizing automated trading through computers is a common phenomenon in many developed countries. On the one hand, using computers to replace human transactions is because computers can find laws and phenomena that are difficult or impossible for people to capture from huge historical data. On the other hand, they can Greatly reduce the impact of traders' emotional fluctuations and avoid making irrational decisions in extreme market conditions. With the rapid development of artificial intelligence, it is the dream of every financial transaction and strategy researcher to train an automated trading model through artificial intelligence technology. Although many achievements have been made, it is not an easy task to direct...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06Q40/04
CPCG06Q40/04G06N3/045G06F18/23213G06F18/214
Inventor 骆超丁奉乾
Owner SHANDONG NORMAL UNIV
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