financial-data-science

What problem does it solve?

This Skill addresses the challenges of executing financial data science work with reproducible research, explicit controls, and deployable outputs, ensuring data integrity and reliability.

Core Features & Use Cases

• Schema Contracts & Freshness: Defines and enforces data source contracts, schema versions, and freshness objectives.
• Deterministic Ingestion & Validation: Ingests data with replay support and deterministic normalization, validating keys, timestamps, and join behavior.
• Continuous Monitoring & Quarantine: Monitors quality metrics continuously and quarantines degraded feeds.
• Controlled Publishing: Publishes data only when lineage, ownership, and quality thresholds are satisfied.
• Use Case: When dealing with critical financial market data, this Skill ensures that the data used for trading algorithms or risk models is consistently accurate, complete, and up-to-date, preventing costly errors due to data quality issues.

Quick Start

Run the financial data science diagnostics script on the input CSV file named 'market_data.csv' and save the output to 'diagnostics.json'.