Singh Tinku, Kalra Riya, Mishra Suryanshi, Kumar Manish
Department of IT, Indian Institute of Information Technology Allahabad, Prayagraj, U.P. India.
Department of Mathematics and Statistics, SHUATS, Prayagraj, U.P. India.
Evol Syst (Berl). 2022 Dec 21:1-19. doi: 10.1007/s12530-022-09481-x.
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.
日内交易在交易者中很受欢迎，因为它能够在短时间内利用价格波动。对于交易者来说，对未来几分钟的实时价格预测有助于制定策略。由于股票市场具有非平稳、复杂、有噪声、混沌、动态、易变和非参数的性质，实时预测具有挑战性。机器学习模型被认为对股票预测有效，然而，它们的超参数需要根据最新的市场数据进行调整，以纳入市场的复杂性。通常，模型是分批进行训练和测试的，这使校正过程更加平滑，并加快了学习速度。在进行日内股票预测时，模型应该针对每个实例进行预测，而不是针对整个批次，并同时学习以确保高精度。在本文中，我们提出了一种基于两种不同学习方法的策略：增量学习和离线-在线学习，以利用实时市场的实时数据流预测股票价格。在增量学习中，模型在从实时流接收到股票的下一个实例时不断更新，而在离线-在线学习中，模型在每个交易日之后重新训练，以确保它纳入了最新的数据复杂性。这些方法被应用于单变量时间序列（根据历史股票价格建立）和多变量时间序列（考虑历史股票价格以及技术指标）。分别对在美国纳斯达克和印度国家证券交易所上市的八只流动性最强的股票进行了广泛的实验。离线-在线模型在低预测误差方面优于增量模型。
