AI-powered backtesting enhances cryptocurrency strategies, suggesting improved performance and risk management.
The swift expansion of cryptocurrency markets necessitates the assessment of clever and flexible trading strategies. In order to optimize cryptocurrency trading strategies, CryptoTradeMate is an AI-powered backtesting framework that combines machine learning, deep learning, and reinforcement learning with historical and on-chain data. The system reduces overfitting and improves robustness by supporting walk-forward optimization, purged cross-validation, and real-time data ingestion. Results from experiments conducted on Bitcoin, Ethereum, and other assets between 2018 and 2024 show that AI-augmented strategies outperform conventional rule-based approaches in terms of stability and risk-adjusted returns, particularly in times of market volatility.
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Nandhini et al. (2025) studied this question.
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