Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals
| Autor: | Francesco Penone, Pier Giuseppe Giribone, Simone Ligato |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
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 Automatic Trading System Machine learning computer.software_genre Dynamic Artificial Neural Network Machine Learning FinTech Artificial Intelligence 0502 economics and business 050207 economics Time series Feedforward Neural Network Nonlinear autoregressive exogenous model Mechanic Trading Signals 050208 finance Artificial neural network business.industry 05 social sciences Technical Analysis NAR NARX Technical analysis Time series forecasting Feedforward neural network Artificial intelligence business computer |
| Zdroj: | International Journal of Financial Engineering. :1850037 |
| ISSN: | 2424-7944 2424-7863 |
| DOI: | 10.1142/s2424786318500378 |
| Popis: | 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 nonlinear 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|