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LANCERProject Description
A fintech firm specializing in stock data intelligence sought a unified platform to streamline financial data processing and analysis. With growing complexity in modeling and reporting, the client needed a scalable solution to automate data extraction, strategy configuration, and metric analysis. The platform enabled structured dashboards, editable modules, and custom ratios for deeper insights.
Scope Of Work
The client needed a dynamic system with the help of Oodles to automate stock data imports, manage financial models, and generate custom metrics. Key areas of work included API integration, editable dashboards, dropdown filters, and backend services for data syncing, validation, and export. The project aimed to enhance operational accuracy, reduce manual effort, and simplify financial strategy management.
Our Solution
To address the client’s need for automation and modular control, a Django-based backend was set up with PostgreSQL for structured data handling. The system enabled seamless data import, model configuration, and financial metric generation.
Key features implemented:
- Autocomplete Search: Integrated API to suggest top matching companies as users type.
- Extended Balance Sheet: Updated DB schema and queries to include missing financial columns.
- Modular Dashboards: Built submenus for Strategies, Ranges, Models, and Aggregate Financials.
- Editable Tables: Enabled inline editing, enter-to-save, and pagination (15 rows/page) for large datasets.
- Advanced Metrics Builder: Allowed users to define custom financial ratios using dropdown-based logic.
- Aggregated Views: Created SQL-driven dashboard.