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

TL;DR

This paper presents a neural network-based stock trading system that uses technical analysis and big data frameworks to predict stock movements and evaluate trading strategies over a 20-year period.

Contribution

It introduces a novel combination of technical indicators, neural networks, and big data processing for stock trading prediction and strategy optimization.

Findings

Neural network model achieves comparable results to Buy and Hold strategy.

Proper technical indicator selection improves trading performance.

Big data framework accelerates training on large financial datasets.

Abstract

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.