feature-store-serving
CommunityFeature parity and freshness for quant research.
Data & Analytics#data engineering#feature parity#quantitative research#feature store#data freshness#production controls
AuthorGhostOf0days
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the critical challenge of ensuring feature parity and freshness between offline development environments and online production systems in quantitative research, preventing inconsistencies that can lead to flawed models and analysis.
Core Features & Use Cases
- Offline-Online Parity: Guarantees that features used in research are identical to those served in production.
- Freshness Guarantees: Ensures that data is up-to-date and meets defined latency requirements.
- Reproducible Research: Enables reliable and repeatable quantitative workflows.
- Production Controls: Implements checks and balances for deploying features to live systems.
- Use Case: When developing a trading algorithm, this Skill ensures the features used for backtesting are the exact same features, with the same freshness, that will be available to the live trading system.
Quick Start
Run the feature store serving diagnostics script on the input CSV file named 'input.csv' and save the output to 'diagnostics.json'.
Dependency Matrix
Required Modules
pandasargparse
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: feature-store-serving Download link: https://github.com/GhostOf0days/codex-quant-skills/archive/main.zip#feature-store-serving Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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