Hybrid RNN-DQN Model for Time Series Forecasting and Trading Strategy Optimization
Keywords:
Hybrid Models, Recurrent Neural Networks, Deep Q-Networks, Time Series Forecasting, Trading Strategy Optimization, Financial Markets.Abstract
Deep learning techniques have attracted a lot of attention in the financial markets lately because of their potential for trading strategy optimization and asset price forecasting. In order to forecast time series and optimize trading strategy, this study presents a novel hybrid model that combines Recurrent Neural Networks (RNN) and Deep Networks (DQN). By combining the best features of DQNs for learning optimal action policies and RNNs for capturing temporal dependencies, the hybrid model improves performance and robustness in financial prediction and decision-making tasks. Our proposed hybrid model is able to anticipate asset values and provide profitable trading strategies, as evidenced by the experimental findings we report on a large dataset spanning multiple years of foreign exchange (forex) market data. Additionally, we perform thorough assessments and contrasts with conventional ways and independent deep learning models to verify the enhanced effectiveness and efficiency of the suggested hybrid model. Our research prepares the way for improved decision support systems and algorithmic trading methods in the field of quantitative finance by enhancing deep learning techniques in financial forecasting and trading.
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