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Journals / Uluslararası Ekonomi İşletme ve Politika Dergisi / 2026 / Cilt: 10 Sayı: 2

Deep Reinforcement Learning for Cryptocurrency Portfolio Management: An Application Using the PPO Algorithm

Derin Pekiştirmeli Öğrenme ile Kripto Para Portföy Yönetimi: PPO Algoritması ile Bir Uygulama

Şaban Onur Viga(ISTANBUL ESENYURT UNIVERSITY, FACULTY OF BUSINESS ADMINISTRATION AND MANAGEMENT SCIENCES)0000-0002-1676-7850
Pages
639–655
Publication Date
2026-10-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, the results highlight that improving data quality through robust preprocessing is critical for the stability and generalization of DRL-based trading systems.

📌 3 Key Takeaways

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Objective & Hypothesis:Cryptocurrency markets are a challenging testbed for algorithmic trading due to high volatility and abrupt regime shifts.
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Key Findings & Data:Using daily Bitcoin (BTC/USD) data for 2018–2024, this study applies an iterative scaling–based data-cleaning procedure to mitigate anomalies in raw exchange data and then trains a Deep Reinforcement Learning agent with Proximal Policy Optimization (PPO).
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Conclusion & Impact:Overall, the results highlight that improving data quality through robust preprocessing is critical for the stability and generalization of DRL-based trading systems.
🔬Design:Modeling & Simulation
📍Scope:2018–2024 Verileri
⚙️Variables:Uluslararası Finans · Derin Pekiştirmeli Öğrenme · Algoritmik Ticaret

Abstract

Cryptocurrency markets are a challenging testbed for algorithmic trading due to high volatility and abrupt regime shifts. Using daily Bitcoin (BTC/USD) data for 2018–2024, this study applies an iterative scaling–based data-cleaning procedure to mitigate anomalies in raw exchange data and then trains a Deep Reinforcement Learning agent with Proximal Policy Optimization (PPO). The dataset is split chronologically: the first 80% is used for training and the last 20% for out-of-sample evaluation. In the test period, the PPO agent delivers a total return of 67.35%, while the buy-and-hold benchmark achieves 149.03%. From a risk-adjusted perspective, the agent exhibits positive Sharpe (1.31) and Sortino (1.90) ratios, and its maximum drawdown (-19.88%) is comparable to the benchmark (-20.06%). Overall, the results highlight that improving data quality through robust preprocessing is critical for the stability and generalization of DRL-based trading systems.

Özet

Kripto para piyasaları, yüksek oynaklık ve ani rejim değişimleri nedeniyle algoritmik ticaret için zorlu bir test ortamıdır. Bu çalışmada Bitcoin (BTC/USD) günlük verileri (2018–2024) kullanılarak, ham veride görülen ölçek/anomali sorunlarını gidermek amacıyla yinelemeli ölçek düzeltme temelli bir veri temizleme prosedürü uygulanmış ve Proximal Policy Optimization (PPO) ile bir derin pekiştirmeli öğrenme (DRL) ajanı eğitilmiştir. Veri seti kronolojik olarak ayrılarak ilk %80’lik bölüm eğitim, son %20’lik bölüm ise out-of-sample test için kullanılmıştır. Test döneminde PPO ajanı toplam %67.35 getiri üretirken, buy-and-hold stratejisi %149.03 getiri sağlamıştır. Risk ayarlı performans metrikleri açısından ajan, Sharpe (1.31) ve Sortino (1.90) oranlarında pozitif bir profil sunmuş; maksimum düşüşü -%19.88 ile benchmark’a (-%20.06) yakın gerçekleşmiştir. Ajanın düşüşlerde pozisyon azaltıp nakde geçmesi, sermaye koruması odaklı bir davranışa işaret etmektedir. Bulgular, veri kalitesini iyileştiren ön işleme adımlarının DRL tabanlı ticaret sistemlerinin kararlılığı ve genellenebilirliği açısından kritik olduğunu göstermektedir.

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Cite This Article

Viga, Ş. O. (2026). Deep Reinforcement Learning for Cryptocurrency Portfolio Management: An Application Using the PPO Algorithm. Uluslararası Ekonomi İşletme ve Politika Dergisi, Cilt: 10 Sayı: 2, 639–655. https://doi.org/10.29216/ueip.1860039

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