International Journal of Financial Engineering (IJFE)
Combining robust Dynamic Neural Networks with traditional technical indicators for generating mechanic trading signals Pier Giuseppe Giribone (1), Simone Ligato (2), Francesco Penone (3) (1) University of Genoa – Department of Economics CARIGE Bank – Financial Engineering (2) Azzoaglio Bank – Risk Management (3) Auditor
[email protected],
[email protected],
[email protected] Abstract:
Forecasting assets’ prices is the aim of each trader, although the trading approaches employed may vary a lot. The development of machine learning techniques has brought the opportunity to design mechanic trading systems based on Dynamic Artificial Neural Networks. The aim of this paper is to combine traditional technical indicators (such as Exponential Weighted Moving Average – EWMA, Percentage Volume Oscillator – PVO and Stochastic indicator - %K and %D) with the Non Linear Autoregressive Networks (NAR and NARX). The first part of the paper describes how neural networks designed for forecasting time series work, the second one performs a deeper validation of the code and the third one combines the dynamic networks with traditional technical indicators in order to generate reliable mechanic signals. The article ends with a back testing of the trading system performed on Dow Jones Industrial Average and on Nasdaq Composite Indexes.
Keywords:
Machine Learning, Artificial Intelligence, Feedforward Neural Network, Dynamic Artificial Neural Network, NAR, NARX, Time series forecasting, FinTech, Automatic Trading System, Mechanic Trading Signals, Technical Analysis 1
International Journal of Financial Engineering (IJFE) References
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International Journal of Financial Engineering (IJFE)
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