  In this paper, a neural network-based stock price prediction and trading
system using technical analysis indicators is presented. The model developed
first converts the financial time series data into a series of buy-sell-hold
trigger signals using the most commonly preferred technical analysis
indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN)
model is trained in the learning stage on the daily stock prices between 1997
and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used
in the training stage. The trained model is then tested with data from 2007 to
2017. The results indicate that by choosing the most appropriate technical
indicators, the neural network model can achieve comparable results against the
Buy and Hold strategy in most of the cases. Furthermore, fine tuning the
technical indicators and/or optimization strategy can enhance the overall
trading performance.
