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The main purpose of these articles is to provide a comprehensive, yet accessible, guide to the ideation and development of a financial Data Warehouse used by hedge funds and asset managers. These writings serve as a resource for professionals in the field, particularly those who are engaged in the technical support of investment management, portfolio analysis, risk management, and related areas. These posts provide an opportunity to understand the 'nuts and bolts' of various operational and strategic elements used in daily practice, helping professionals to make more informed decisions and drive performance.

The discussion on various topics, from data warehousing to backcasting returns, provides insights into how complex data and computational processes can be harnessed to inform investment decisions, risk assessment, and performance evaluation. The posts also shine a light on the nuances of software tools used in the industry, giving readers a clearer picture of how these tools enhance efficiency and accuracy in financial operations.

Furthermore, these posts offer a platform for readers to reflect on their own practices, compare their strategies with industry norms, and potentially identify areas for improvement or further investigation. By presenting practical scenarios and real-world applications, they help professionals gain a more comprehensive understanding of the financial landscape and the mechanisms at play.

Lastly, the discussions around failures and challenges serve as a candid acknowledgment of the complexities inherent in financial management. They underscore the importance of vigilance, documentation, and ongoing analysis to anticipate, understand, and mitigate potential risks or pitfalls. By doing so, these posts contribute to fostering a culture of continuous learning and improvement in the industry.

Overall, these posts aim to enhance knowledge, stimulate thought, and drive excellence in the realm of financial management and investment strategy. They stand as an informative and practical resource for professionals looking to deepen their understanding, sharpen their skills, and stay abreast of industry practices and trends.


The purpose of this reference is to document the concepts and implementation of the DataWarHorse, a long/short equity hedge fund data warehouse and enterprise risk management (ERM) system .

Information is critical to the success of hedge funds and any business for that matter. Data comes in all different shapes and sizes and the synthesis of this data into information is what datawarehouses do.

Hedge fund datawarehouses are used to aggregate data from disparate sources and in turn produce operational reports and risk analytics that help users make better and faster informed investment decisions. More data often leads to better information and that gives you a competitive edge in a competitive industry.

The innovative and intuitive tools were built to empower hedge fund employees to focus on the added-value components of their jobs and let the datawarehouse automate the menial and repetitive daily tasks. The DataWarHorse and this accompanying reference bring these innovative and intuitive tools to you in a digestible and usable way.