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In this paper, a neural network-based stock price prediction and tradingsystem using technical analysis indicators is presented. The model developedfirst converts the financial time series data into a series of buy-sell-holdtrigger signals using the most commonly preferred technical analysisindicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN)model is trained in the learning stage on the daily stock prices between 1997and 2007 for all of the Dow30 stocks. Apache Spark big data framework is usedin the training stage. The trained model is then tested with data from 2007 to2017. The results indicate that by choosing the most appropriate technicalindicators, the neural network model can achieve comparable results against theBuy and Hold strategy in most of the cases. Furthermore, fine tuning thetechnical indicators and/or optimization strategy can enhance the overalltrading performance.

An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework