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This paper investigates the application of momentum-based technical indicators to enhance electricity price forecasting and inform strategic decision-making in year-ahead futures trading, particularly amid heightened market volatility. Utilizing a Python-based analytical tool, the study integrates Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) indicators with historical electricity price data sourced from the ENTSO-E Transparency Platform. The methodology encompasses automated data preprocessing, computation of technical indicators, and detection of early warning signals for bearish market reversals. These signals are systematically compared with actual market prices from September 2021 to March 2023. The findings reveal that both MACD and RSI effectively identified bearish signals during Q3–Q4 2022, which corresponded with notable price declines in the HUDEX BL YR-23 futures contract. This alignment underscores the capacity of technical indicators to anticipate shifts in market momentum and optimize the timing of contracting decisions. The practical implications are significant: integrating technical analysis into electricity trading facilitates more effective risk-adjusted decision-making and portfolio optimization. Specifically, the use of momentum indicators enables electricity producers to recognize periods of elevated forward prices and secure sales at advantageous market levels, thereby maximizing revenue potential in volatile environments. This research contributes to the intersection of financial market tools and energy trading by demonstrating the practical value of technical indicators in year-ahead electricity futures. It applies MACD and RSI within a systematic, Python-based framework to support year-ahead electricity futures trading in the EU. In contrast to the majority of existing research, which primarily focuses on short-term forecasting, this paper investigates the use of technical signals for long-term hedging optimization, an area that in electricity portfolio management. Furthermore, it offers a replicable, data-driven methodology for signal detection and establishes a foundation for future integration with machine learning and AI-based forecasting models in energy and other commodity markets.

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Applying Technical Indicators to Year-Ahead Electricity Futures in Volatile Markets

  • 会议论文

  • 发表日期：

    2026年

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  • Jelena Popara,
  • Nenad Glavcic,
  • Milena Savkovic,
  • Danijela Ciric Lalic,
  • Bojan Lalic

摘要

This paper investigates the application of momentum-based technical indicators to enhance electricity price forecasting and inform strategic decision-making in year-ahead futures trading, particularly amid heightened market volatility. Utilizing a Python-based analytical tool, the study integrates Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) indicators with historical electricity price data sourced from the ENTSO-E Transparency Platform. The methodology encompasses automated data preprocessing, computation of technical indicators, and detection of early warning signals for bearish market reversals. These signals are systematically compared with actual market prices from September 2021 to March 2023. The findings reveal that both MACD and RSI effectively identified bearish signals during Q3–Q4 2022, which corresponded with notable price declines in the HUDEX BL YR-23 futures contract. This alignment underscores the capacity of technical indicators to anticipate shifts in market momentum and optimize the timing of contracting decisions. The practical implications are significant: integrating technical analysis into electricity trading facilitates more effective risk-adjusted decision-making and portfolio optimization. Specifically, the use of momentum indicators enables electricity producers to recognize periods of elevated forward prices and secure sales at advantageous market levels, thereby maximizing revenue potential in volatile environments. This research contributes to the intersection of financial market tools and energy trading by demonstrating the practical value of technical indicators in year-ahead electricity futures. It applies MACD and RSI within a systematic, Python-based framework to support year-ahead electricity futures trading in the EU. In contrast to the majority of existing research, which primarily focuses on short-term forecasting, this paper investigates the use of technical signals for long-term hedging optimization, an area that in electricity portfolio management. Furthermore, it offers a replicable, data-driven methodology for signal detection and establishes a foundation for future integration with machine learning and AI-based forecasting models in energy and other commodity markets.