###### Abstract  
This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limit order book (LOB) and order flow (OF) microstructure data and improving prediction accuracy over current state-of-the-art models. The proposed deep learning model, TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units (TFF-CL-GRU), takes LOB and OF features as input and consists of convolutional layers splitting into three channels before rejoining into a Gated Recurrent Unit. Key innovations include a tailored input representation incorporating LOB and OF features across recent timestamps, a hierarchical feature-learning architecture leveraging convolutional and recurrent layers, and a model design specifically optimised for LOB and OF data. Experiments utilise a new dataset (MICEX LOB OF) with over 1.5 million LOB and OF records and the existing LOBSTER dataset. Comparative evaluation against the state-of-the-art models demonstrates significant performance improvements with the TFF-CL-GRU approach. Through simulated trading experiments, the model also demonstrates practical applicability, yielding positive returns when used for trade signals. This work contributes a new dataset, performance improvements for microstructure-based price prediction, and insights into effectively applying deep learning to financial time-series data. The results highlight the viability of data-driven deep learning techniques in algorithmic trading systems. Limit order book; order flow; time-series analysis; stock prediction; deep learning; intraday trading +
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discern subtle patterns and relationships crucial for accurate predictions. The impact of this limitation is substantial, as it hinders the models' ability to provide nuanced insights into the intraday dynamics of stock prices, resulting in suboptimal predictions and potentially missed trading opportunities. The prediction of intraday stock price movements, the central focus of this paper, introduces additional challenges, including the need for high-frequency input data and the capability to process these data by distinguishing noise from signals in order to accurately predict tick-by-tick price fluctuations. Deep learning approaches have emerged as promising solutions to address the limitations of traditional methods. In particular, the use of deep learning models to analyse LOB and order flow (OF) data has shown great potential in predicting stock price movements, as seen in the work by [PERSON] et al. [11]. LOB data contain information about all the buy-and-sell orders for a particular stock at a given time, including the price, volume, and time of each order. These data are updated in real time, making them exceedingly high-dimensional and dynamic. By applying deep learning techniques, such as Convolutional Neural Networks (CNNs) [13; 14; 15] and Recurrent Neural Networks (RNNs) [16; 17; 18] to LOB data, researchers can extract valuable features from these complex data and make accurate predictions about future price movements. Furthermore, order flow data provide additional insights into the market by tracking the actual trading executed by market participants. These data can provide valuable information about the supply-and-demand dynamics of the market and can be used to develop more accurate models for predicting future price movements. Attention-based neural networks [19; 20; 21] have shown great promise in extracting relevant features from order flow data and using these features to make accurate predictions. In summary, the use of deep learning models to analyse LOB and OF data has the potential to greatly improve the accuracy of intraday stock price predictions. By leveraging the power of deep learning models, researchers and investors can overcome the limitations of traditional methods and better understand the complex dynamics of the stock market.