A ML-Based Stock Trading Model for Profit Predication

Abstract

This paper uses a new convolutional neural network framework to collect data on leading indicators including historical prices and their futures and options, and use arrays as the input map of the CNN framework for stock prices trend prediction. Experiments are then conducted by the stock markets of the United States and Taiwan using historical data, futures and options as data sets to predict the stock prices. After that, genetic algorithm is then utilized to find trading signals. Results showed that the designed model achieves good return of the investments.