Indian Stock Market Sell and Buy Indication using Technical Indicators and Enhanced Bidirectional Long Short-Term Memory


  • Bhagyashree Pathak, Snehlata Barade


Stock Data, Random Forest, Gradient Boosting, XGBoost, DenseNet, CNN-BiLSTM, LSTM, BiLSTM, DANN, Technical Indicators.


This study introduces an innovative approach to signal generation for sell and buy decisions in the Indian Stock Market, leveraging Novel Technical Indicators and an Enhanced Bidirectional Long Short-Term Memory (BiLSTM) model. We evaluated various machine learning models, including Random Forest, Gradient Boosting, XGBoost, DenseNet, CNN-BiLSTM, LSTM, BiLSTM, and DANN, on their predictive performance using metrics such as MAPE, MAE, and computation time. Our proposed BiLSTM model, optimized with novel technical indicators, demonstrated superior performance with the lowest MAPE and competitive MAE, while maintaining a rapid computation time. These results highlight the efficacy of BiLSTM models in handling the sequential nature of stock data and the advantage of novel technical indicators in capturing intricate market trends. The proposed system holds the potential to revolutionize decision-making processes for traders and investors by providing highly accurate, real-time market predictions. The comparative analysis across diverse machine learning techniques showed that the proposed method significantly surpasses conventional models like Random Forest, Gradient Boosting, and XGBoost in terms of accuracy and efficiency. It achieved a remarkable reduction in Mean Absolute Percentage Error (MAPE) to nearly 0.03%, drastically lower Mean Absolute Error (MAE) at 10.45, and exhibited the fastest execution speed at 0.984 ms, highlighting its substantial advancement over existing approaches.


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How to Cite

Snehlata Barade, B. P. . (2024). Indian Stock Market Sell and Buy Indication using Technical Indicators and Enhanced Bidirectional Long Short-Term Memory. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 169 –. Retrieved from



Research Article