Optimization of Automated Trading Systems with Deep Learning Strategies
DOI:
https://doi.org/10.5281/zenodo.12780180ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a02Keywords:
Automated Trading Systems, Deep Learning, LSTM Networks, Reinforcement Learning, Convolutional Neural Networks, Trading Performance, Financial Markets, Machine Learning, Trading Strategies, Data Preprocessing, Neural Networks, Algorithmic Trading, Model Optimization, Prediction Accuracy, Risk ManagementAbstract
Automated trading systems have revolutionized the financial markets by executing trades at speeds and frequencies far beyond human capabilities. The integration of deep learning strategies into these systems promises to enhance their performance by better predicting market movements and making more informed trading decisions. This paper explores various deep learning techniques applied to automated trading systems, examining their effectiveness, implementation challenges, and potential benefits. Specifically, we investigate the use of Convolutional Neural Networks (CNNs) for pattern recognition in price charts, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for time-series prediction, and Deep Reinforcement Learning (DRL) for strategy optimization. We present a comprehensive analysis of these methods, highlighting their strengths and weaknesses in different market conditions. Our experiments demonstrate significant improvements in trading performance, including higher profitability and reduced risk, thus underscoring the transformative potential of deep learning in automated trading.
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