An efficient real-time stock prediction exploiting incremental learning and deep learning

Intraday trading is popular among traders due to its ability to leverage price fluctuations in a short timeframe. For traders, real-time price predictions for the next few minutes can be beneficial for making strategies. Real-time prediction is challenging due to the stock market's non-stationary, complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. Machine learning models are considered effective for stock forecasting, yet, their hyperparameters need tuning with the latest market data to incorporate the market's complexities. Usually, models are trained and tested in batches, which smooths the correction process and speeds up the learning. When making intraday stock predictions, the models should forecast for each instance in contrast to the whole batch and learn simultaneously to ensure high accuracy. In this paper, we propose a strategy based on two different learning approaches: incremental learning and Offline-Online learning, to forecast the stock price using the real-time stream of the live market. In incremental learning, the model is updated continuously upon receiving the stock's next instance from the live-stream, while in Offline-Online learning, the model is retrained after each trading session to make sure it incorporates the latest data complexities. These methods were applied to univariate time-series (established from historical stock price) and multivariate time-series (considering historical stock price as well as technical indicators). Extensive experiments were performed on the eight most liquid stocks listed on the American NASDAQ and Indian NSE stock exchanges, respectively. The Offline-Online models outperformed incremental models in terms of low forecasting error.