Developing Trading Strategies for Doge Coin with Reinforcement Learning

Abstract

Cryptocurrency trading, particularly with highly volatile assets like Dogecoin, presents significant challenges due to rapid price fluctuations and external factors such as social media sentiment and speculative trading behaviors. This study proposes reinforcement learning (RL)-based trading strategies to address these complexities. RL, an advanced machine learning approach, enables dynamic adaptation to market conditions by optimizing sequential decisions for maximum cumulative rewards. Using historical market data and technical indicators, RL agents were trained and evaluated in simulated trading environments. Performance metrics, including profitability, risk-adjusted returns, and robustness under varying market conditions, demonstrate that RL-based strategies outperform traditional methods by capturing non-linear dependencies and responding effectively to delayed rewards. The results highlight the ability of RL to adapt to market volatility and optimize trading outcomes. However, the study acknowledges limitations, including the exclusion of external sentiment data and restricted testing across diverse market scenarios. Future research should integrate external data sources, such as sentiment and macroeconomic indicators, conduct real-time market testing, and explore applications to multi-asset portfolios to improve generalizability and robustness. This research contributes to the intersection of machine learning and financial markets, showcasing RL’s potential to address cryptocurrency trading challenges and offering pathways for more adaptive and robust trading strategies.