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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding SNATIF JUITA : Jurnal Informatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika STMIK Antar Bangsa IKRA-ITH Informatika : Jurnal Komputer dan Informatika JURNAL TEKNOLOGI DAN OPEN SOURCE J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Journal of Innovation and Future Technology (IFTECH) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Teknika Jurnal Sistem Informasi IJISTECH Akasia: Artikel Ilmiah Sistem Informasi Akuntansi JUSTIAN - Jurnal Sistem Informasi Akuntansi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Teknologika Jurnal Pengabdian Pada Masyarakat Journal of Accounting Information System Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Adi Supriyatna
Universitas Bina Sarana Informatika
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Journal : Jurnal Mandiri IT

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Reinforcement learning for bitcoin trading: A comparative study of PPO and DQN Prasetyo, Romadhan Edy; Sumanto, Sumanto; Chaidir, Indra; Supriyatna, Adi
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.455

Abstract

Bitcoin’s high volatility demands automated strategies that adapt to changing market regimes while managing risk. This study compares Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) for Bitcoin trading using hourly BTC/USDT data from 2019 to early 2025. The models are trained to generate buy and sell signals from technical indicators including the Relative Strength Index (RSI), MA20, volatility, Moving Average Convergence Divergence (MACD), volume trend, SMA200, and a weekly trend filter. All features are computed on hourly bars. The evaluation shows that PPO tends to trade more aggressively and delivers higher performance during bullish phases, though with greater risk in unstable markets. By contrast, DQN trades more selectively and maintains better stability in sideways or choppy conditions. These findings support the effectiveness of reinforcement learning for adaptive cryptocurrency trading and highlight complementary strengths between PPO and DQN across market regimes.
Co-Authors Ade Christian Ade Christian, Ade Adjat Sudradjat ahmad yani Ali Haidir Aminudin, Aam Anna Anna Anna Anna, Anna Baginda Oloan Lubis Cindya Windari Dede Firmansyah Saefudin Dewi Laraswati Diah Puspitasari Endang Wahyudi Erma Delima Sikumbang Fani Talia Fatmasari Fatmasari Fattya Ariani Fitra Hariman Hakim Hadi Sukmana, Sulaeman Handayani, Tiwi Hariyanto HARIYANTO HARIYANTO Henny Destiana Herlawati Herlawati Indarti Indarti, Wahyu Indra Chaidir, Indra Indra Riyana Rahadjeng Intan Sarwono Putri Irfan Ali, Irfan Irma Purnamasari, Ade Irmawati Carolina Ishak Komarudin Karlena Indriani Kresna Ramanda Lisnawanty Lisnawanty Lita Sari Marita Lydia Salvina Helling Mari Rahmawati Maria, Vivi Mas Aji Sapto Nugroho Mochamad Wahyudi Prasetyo, Romadhan Edy Pujiastuti, Lise Rachman Komarudin Riska Utami Dewi Rizal Amegia Saputra Rosliana, Siti Ruhul Amin, Ruhul Saridawati Saridawati, Saridawati Siti Rosliana Sri Wasiyanti, Sri Suhar Janti Sumanto, Sumanto Suminten, Suminten Talia, Fani Teni Agustina Ummu Radiyah, Ummu Wida Prima Mustika Wina Widiati Windi Irmayani