A design framework for prediction models based on the order bookGantar, Klemen (Avtor) Kononenko, Igor (Mentor) machine learningprediction modeldeep learningneural networkslimit order bookfinancial marketsThis master's thesis deals with the implementation and evaluation of the framework for order book based prediction model design. First, the field of financial markets is described along with the processes, that govern it. Here the focus is on the order book dynamics as it is the main topic of the thesis. Next, the field of machine learning and its relationship with the financial markets are described, followed by the findings of the related work which serve as a motivation for the implementation of the universal framework for order book data collection and analysis. The developed framework and its functionalities are then described. First, the data acquisition and data manipulation modules take care of the data which is then used to design and train prediction models, which are in turn applied to the market along with the trading strategy of choice. Finally, the framework and its functionalities are experimentally demonstrated. Real order book data is collected and used to produce a prediction model which is then evaluated and backtested in connection with a corresponding trading strategy. The thesis is concluded by the overall evaluation of the framework and its results.20192019-10-17 15:05:02Magistrsko delo/naloga111964VisID: 22965COBISS_ID: 1538417347sl