Abstract
This research delves into the potential of ensemble machine learning in algorithmic trading, aiming to strike a balance between performance and risk management. In the increasingly intricate landscape of financial markets, employing ensemble approaches that amalgamate multiple models becomes imperative. The study elucidates how these techniques enhance the accuracy, resilience, and adaptability of algorithmic trading systems. It further emphasizes the significance of model variability, hyperparameter tuning, and the bagging ensemble learning technique for optimizing outcomes. Moreover, the research scrutinizes the benefits of ensemble learning for algorithmic trading portfolios, particularly in terms of tail risk management and risk diversification. By utilizing ensemble approaches, several advantages emerge, including mitigated model bias, heightened generalizability, and an improved Sharpe Ratio. Ensemble methods enable traders to navigate market complexities more effectively, adapting to changing conditions and reducing the impact of outliers. This comprehensive approach not only enhances trading performance but also fortifies risk management strategies, making it indispensable in today's dynamic financial environment. Keywords: Ensemble machine learning, algorithmic trading, risk management, performance, Stock market forecast
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