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All Journal International Journal of Electrical and Computer Engineering Rekayasa Mesin Sinergi MESIN Turbo : Jurnal Program Studi Teknik Mesin International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Lamahu: Jurnal Pengabdian Masyarakat Terintegrasi JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA Kocenin Jurnal Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Sally Cahyati
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Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control Fat, Joni; Moengin, Parwadi; Astuti, Pudji; Cahyati, Sally
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1531-1542

Abstract

Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Co-Authors Agtirey, Novan Rojabil Ahju, Rosalind Astuti , Pudji Dadang Surjasa, Dadang Diki Muchtar Diki Fat, Joni Febriyanto Febriyanto Himawan, Riswanda Laksana, Dek Ngurah Laba Larasati Rizky Putri Leo, Joseph Andrew Mahendra, Daniel Rizky Maya Indrasti Notoprayitno Medy Rioma, Medy Minarti, Astari Nora Azmi Nurhikmah Budi Hartanti Parwadi Moengin Permatasari , Rosyida PUDJI ASTUTI Puspa, Sofia Debi Putra, Reky Amanda Ridhoarto, Muhammad Alwan Rositayanti Hadisoebroto Rosyida Permatasari, Rosyida Suroso Suroso Susetyarto, Martinus Bambang Triwulandari , Triwulandari