## 6 Conclusions and Future Work  
The primary objective of this research study was to construct a stock price prediction model utilising deep supervised learning with high-frequency limit order book (LOB) and order flow (OF) market data. Expanding upon previous investigations in this domain, a novel deep supervised model, namely the TFF-CL-GRU model, was developed. This model \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Metric** & **VTB** & **Sberbank** & **Gazprom** & **Apple** & **Amazon** & **Google** \\ \hline \multirow{4}{*}{TFF-CL-GRU} & Accuracy & 66\% & 48\% & 53\% & 50\% & 60\% & 55\% \\  & F1 score & 65\% & 45\% & 51\% & 49\% & 60\% & 55\% \\  & Precision & 67\% & 51\% & 56\% & 51\% & 61\% & 55\% \\  & Recall & 63\% & 40\% & 47\% & 47\% & 59\% & 54\% \\ \hline \multirow{4}{*}{DeepLOB} & Accuracy & 62\% & 49\% & 52\% & 41\% & 40\% & 42\% \\  & F1 score & 60\% & 41\% & 46\% & 31\% & 36\% & 38\% \\ \cline{1-1}  & Precision & 66\% & 54\% & 57\% & 42\% & 40\% & 42\% \\ \cline{1-1}  & Recall & 55\% & 34\% & 39\% & 25\% & 32\% & 34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the TFF-CL-GRU model and the DeepLOB model for six stocks. Figure 14: Trading simulation results for Gazprom. Figure 13: Trading simulation results for Sberbank. combines the strengths of convolutional layers to handle the high-dimensional LOB and OF features and recurrent GRU layers to accommodate the time-series nature of the data. The collected high-frequency market data, including order flow and limit order book data, underwent several steps of data processing, including data sourcing, feature selection, and normalisation. Feature selection involved considering the 100 most recent events in the order flow and limit order book data to capture the time-series nature of the stock price movement. Limiting the analysis to the first 10 levels of the limit order book and setting a limit on the number of transactions at each timestamp ensured consistency in the input data tensors. Datasets for Sberbank, VTB, Gazprom, Apple, Amazon, and Google stocks were used in the experiment. Based on the analysis of the capabilities of state-of-the-art models in this field, the DeepLOB model [10] was selected as the benchmark for comparative assessment. This decision was influenced by its performance on the benchmark LOB dataset, as demonstrated by [PERSON] et al. [27], and the availability of the code to reproduce the experiment. The TFF-CL-GRU model demonstrated markedly better performance in terms of its F1 score, which was at least 4% higher than that of the DeepLOB model for each of the six stocks considered. This advantage can be attributed to a combination of feature engineering, model architecture, and the fine-tuning of its parameters. The F1 score achieved by the TFF-CL-GRU model was 45% for Sberbank stock; 51% for Gazprom stock; 65% for VTB stock; 49% for Apple stock; 60% for Amazon stock; and 55% for Google stock. This is much higher than random prediction (which would have been around 33% for three classes) and, in theory, should provide a noticeable advantage in trading. In order to confirm if this was indeed the case, further trading simulations were conducted. The annual returns for the strategy based on the TFF-CL-GRU model's predictions were measured for each of the three stocks (VTB, Sberbank, and Gazprom). While the strategy relying on the model's predictions consistently yielded higher median returns for VTB and Gazprom compared to the buy-and-hold strategy, the results differed for Sberbank. The negative returns for Sberbank can be attributed to its predominantly declining trend, compounded by restrictions on short sales. While the proposed TFF-CL-GRU model demonstrated strong predictive performance on the test datasets, its complexity and computational demands need to be addressed to enable practical deployment in real-time trading environments. Future work should explore techniques for optimising model efficiency without significantly compromising accuracy.