A small discretionary trading hedge fund was operating with fragmented data systems. Trades executed across multiple brokers and insights from third-party data sources were siloed, making it impossible to generate a unified view of portfolio risk. The fund's risk manager lacked the tools to perform comprehensive, cross-platform analysis, hindering decision-making and increasing operational strain.
- Designed an end-to-end data architecture tailored to trading operations
- Deployed a scalable data warehouse on AWS
- Built custom ingestion pipelines to unify broker and third-party data sources
- Implemented a normalization layer for schema consistency
- Automated the data workflow using AWS ECS for seamless execution
- Developed real-time dashboards for monitoring key risk metrics
- Achieved a holistic view of portfolio risk across all brokers and data providers
- Enabled quantitative risk analysis techniques that were previously infeasible
- Reduced the risk manager's daily workload by several hours
- Improved trading decisions through faster, data-backed insights
- Established a foundation to integrate GenAI for higher decision speed and broader data integration
The fund can now monitor its positions and risk exposure in real-time, unlocking quantitative insights that drive performance. With automation in place and a clean data foundation, they are ready to scale analytics and explore AI-driven strategies.
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