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

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 decompo