###### Abstract
The goal of this study is to develop a new approach to intraday stock price movement prediction based on machine learning methods, leveraging tick-by-tick LOB and OF data. We hypothesised that LOB and OF data, when processed into an appropriate set of features, would contain enough information to predict intraday stock price movements. We further hypothesised that introducing a new deep learning architecture could improve the predictive power of this approach compared to existing models. Our analysis successfully confirmed both hypotheses. The work carried out in this research has contributed to all the major stages of the experimental pipelines, as designed and evaluated, to arrive at the final results. Overall, this paper makes the following contributions:  
* We introduce a new dataset containing both LOB and OF data, as the current widely used benchmark dataset in the research field contains only LOB data. This new dataset was made available to other researchers to test their models. * We propose a novel deep learning model for stock price prediction using LOB and OF data, which achieves higher predictive power compared to the state-of-the-art models in the research field. * We provide insights into effectively applying deep learning techniques to complex market microstructure data. * We demonstrate the proposed model's real-world viability via trading simulations. Several innovative concepts were introduced in the course of the iterative optimisation of the building blocks of the system, from data processing and feature selection to the supervised deep learning model architecture. In particular, the key innovations are:  
* A tailored input representation that incorporates LOB and OF features across recent timestamps to capture time dependencies. In addition to the LOB price and volume features typically used by researchers in the field, 30 order flow features were added for the 10 latest transactions at each LOB timestamp: price, volume, and direction (buy/sell). * A hierarchical feature-learning architecture that uses convolutional layers to automatically learn spatial features, followed by recurrent GRU layers to learn temporal patterns. * The model architecture, hyperparameters, and experimental design were specifically tailored for the LOB and OF feature set developed, resulting in superior performance compared to the state-of-the-art models in the research field. These innovations address several key challenges in modelling complex limit order book (LOB) and order flow (OF) microstructure data. Firstly, the high dimensionality and complexity of LOB and OF microstructure data make it challenging to identify the features with the highest predictive power. The tailored input representation incorporates features across recent timestamps, capturing the time-series nature of the data in a way that is compatible with deep learning, allowing the model to better navigate and interpret relationships in the vast amount of data. Secondly, previous works often presented a disintegrated representation of LOB and OF data. By developing an integrated input representation of LOB and OF features, this study effectively models these complex microstructure data in a unified manner. Lastly, the noise inherent in high-frequency data makes it difficult to extract meaningful patterns. A carefully tailored model with specific hyperparameters and experiments for the LOB and OF feature set ensures that the approach is optimised to leverage insights within these financial data. The hierarchical feature-learning architecture, utilising convolutional and recurrent layers, directly tackles the challenge of extracting meaningful patterns. Convolutional layers learn spatial features, while Gated Recurrent Unit (GRU) layers learn temporal dependencies to discern trends. Before elaborating further on the methodology employed, the pipelines implemented, and the results achieved, the next section outlines the background for this study, so as to place this study within the broader context of previous works in this area.