feed-handler-normalization

Name: feed-handler-normalization
Availability: InStock
Author: ghostof0days

Normalize trading data feeds with replay support and quality validation.

Updated Feb 10, 2026

Search Tags:#data quality #data validation #normalization #quantitative research #trading systems #feed handler

Ready to run

npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill feed-handler-normalization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: feed-handler-normalization
Source: https://github.com/GhostOf0days/codex-quant-skills/tree/main/feed-handler-normalization
Command: npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill feed-handler-normalization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of ensuring data quality, consistency, and reliability in quantitative trading systems by normalizing data feeds and handlers.

Core Features & Use Cases

Data Ingestion & Normalization: Ingests data with replay support and deterministic normalization.
Data Validation: Validates keys, timestamps, and point-in-time join behavior.
Quality Monitoring: Continuously monitors quality metrics and quarantines degraded feeds.
Use Case: When dealing with multiple real-time data feeds for financial instruments, this Skill ensures that all data is processed into a consistent format, validated for accuracy, and monitored for quality before being used in trading algorithms.

Quick Start

Run the feed handler normalization diagnostics script on input.csv and save the output to diagnostics.json.

Frequently Asked Questions about feed-handler-normalization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I normalize real-time trading data feeds for quantitative research?▼

To normalize trading data feeds, this Skill ingests data with replay support and performs deterministic normalization. It ensures all real-time financial instrument feeds are processed into a consistent format before being used in trading algorithms.

What is feed handler normalization in trading systems?▼

Feed handler normalization standardizes data feeds for quantitative research and production controls. The mechanism validates data integrity, monitors quality metrics, and quarantines degraded feeds to ensure reliability in trading systems.

How do I run diagnostics for feed handler normalization on input data?▼

You run the feed handler normalization diagnostics script on your input.csv file and save the output to diagnostics.json. This process evaluates data quality, lineage, and ownership before publishing any normalized output.

When should I quarantine degraded data feeds in quantitative trading systems?▼

You should quarantine degraded data feeds when continuous quality monitoring detects anomalies that fail established thresholds. This Skill prevents compromised data from being published until lineage, ownership, and quality requirements are met.

Does feed handler normalization support data replay for production trading controls?▼

Yes, feed handler normalization supports data ingestion with replay capabilities for production controls. This allows quantitative research and trading systems to deterministically reprocess historical data feeds for validation and testing.