Predicting Stock Price Trend With Recurrent Neural Networks

This paper propose recurrent neural network can be used in minute-level stock prediction.It generates prediction of future stock tendency solely by past stock price signal.Deep learning has perform dramatically on tons of different problem.In this paper,we combine technical analysis with deep learning,viewing stock prediction a time series prediction problem.We use recurrent neural network to model stock signal and empirical results show recurrent neural network can produce higher accuracy.We also discuss how to utilize prediction to generate better trade actions.This paper proposes limit order is a more intelligent and profitable way to trade stock.When a bad market order is executed,trader will encounter certain loss since the bad decision makes trader stuck in bad price position.A Limit order is superior to market order in such way that it always give the trader a better price position.We use a customized deep continuous Q learning algorithm to pricing limit order and trade stocks in discrete time steps.Experiments on NSC market data show our strategy is better than market order strategy and our algorithm is more suitable for our problem.