| Abstrakt: |
This study focuses on successful Forex trading by emphasizing the importance of identifying market trends and utilizing trend analysis for informed decision-making. The authors collected low-correlated currency pair datasets to mitigate multicollinearity risk. Authors developed a two-stage predictive model that combines regression and classification tasks, using the predicted closing price to determine entry and exit points. The model incorporates Bi-directional long short-term memory (Bi-LSTM) for improved price forecasting and higher highs and lower lows (HHs-HLs and LHs-LLs) to identify trend changes. They proposed an enhanced DeepSense network (DSN) with all member-based optimization (AMBO-DSN) to optimize decision variables of DSN. The performance of the models was compared to various machine learning, deep learning, and statistical approaches including support vector regressor (SVR), artificial neural network (ANN), auto-regressive integrated moving average (ARIMA), vanilla-LSTM (V-LSTM), and recurrent neural network (RNN). The optimized form of DSN using genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) was compared with AMBO-DSN, yielding satisfactory results that demonstrated comparable quality to the observed trends on the original currency pairs. The effectiveness and reliability of the AMBO-DSN approach in forecasting trends for USD/EUR, AUD/JPY, and CHF/INR currency pairs were validated through statistical analysis while considering computational cost. [ABSTRACT FROM AUTHOR] |
