Technical indicators for energy market trading
Catherine McHugh, Sonya Coleman, Dermot Kerr
Abstract
Technical indicators have been widely applied to the financial trading market, often combined with machine learning algorithms, to predict future stock market prices. The characteristics of energy market data are comparable to financial trading data; hence this research derives eight price prediction technical indicators for hourly electricity prices from the Irish Integrated Single Electricity Market. The proposed indicators consider the three key types of price indicators: trend, oscillator, and momentum. Building the technical indicators from raw electricity price data helps to capture market behaviours and find information to predict future profitable prices. The electricity price data for the proposed indicators were collected from February 2019 until March 2020. Three machine learning regression algorithms were trained with the technical indicators: Extreme Gradient Boosting, Gradient Boosting, and Random Forest. The results demonstrate that the price prediction models perform much better when trained using the proposed technical indicators when compared with baseline raw price data models.