Business Intelligence in High-Frequency Trading

The aim of this work is design and implementation of Business Intelligence solution in high-frequency trading domain to improve the efficiency and transparency based on strategic needs of businesses. The work includes the definition and analysis of high-frequency trading from multiple perspectives. It examines the structure of the global financial market and particularly their segments by different classifications. Using statistics and regulatory and industrial reports, this thesis highlights markets with optimal conditions for efficient HFT operation. To fulfil the goal of the work, it is required to identify technological constraints and specifics of high-frequency trading. The second part describes common architecture of these systems and examines the internal and external data environment in detail. The output allowed to identify KPIs critical for businesses. The final practical part presents design of BI solution based on identified specifics and requirements. The proposed concept includes metadata description of datasets, dimensions, KPIs, data warehouse model and both technical and business dashboards. This work has both practical and theoretical significance. From theoretical perspective, it systemizes current knowledge about financial markets, high-frequency trading and Business Intelligence. Practically, the proposed solution can be adapted for companies operating in high-frequency trading and used as a tool to improve the transparency, efficiency and manageability of algorithms and strategies.