market-data-engineering

Name: market-data-engineering
Availability: InStock
Author: ghostof0days

Engineer market data workflows with deterministic normalization and point-in-time join integrity.

Updated Feb 10, 2026

Search Tags:#data quality #data engineering #quantitative research #market data #order book #spread quality

Ready to run

npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill market-data-engineering

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the critical need for robust and reliable market data engineering, ensuring data quality, consistency, and control for quantitative research and production systems.

Core Features & Use Cases

Data Ingestion & Normalization: Handles data ingestion with replay support and deterministic normalization.
Data Validation & Monitoring: Validates keys, timestamps, and point-in-time join behavior, continuously monitoring quality metrics.
Risk Control: Enforces thresholds, quarantines degraded feeds, and ensures full lineage before release.
Use Case: When diagnosing issues with order-book depth behavior or spread-quality, this Skill provides the tools to ingest, validate, and monitor the relevant market data.

Quick Start

Run python scripts/market_data_engineering_diagnostics.py input.csv --output diagnostics.json to generate diagnostics for your market data.

Frequently Asked Questions about market-data-engineering

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

FAQPage Schema

How do I normalize market data and validate point-in-time join integrity for quantitative research?▼

Normalize market data deterministically with replay support and validate point-in-time join integrity by continuously monitoring timestamps and keys to ensure dataset consistency for quantitative research.

What is the best way to monitor order book depth behavior and run spread-quality diagnostics?▼

Monitor order book depth behavior and run spread-quality diagnostics by ingesting market data, validating timestamps, and generating diagnostic outputs to detect anomalies in spread metrics.

How do you quarantine degraded market data feeds and enforce risk control thresholds?▼

Quarantine degraded market data feeds and enforce risk control by applying threshold checks, tracking full lineage, and triggering fallback paths before releasing data to production systems.

Does this market data engineering workflow require pandas for processing input CSV files?▼

Yes, this market data engineering workflow requires pandas to process input CSV files, generating diagnostics JSON outputs to validate data quality and enforce production controls.

When should I use a fallback path for market data ingestion during continuous quality monitoring?▼

Use a fallback path for market data ingestion during continuous quality monitoring when data validation detects degraded feeds, ensuring downstream quantitative systems maintain consistent integrity.

Why does market data normalization require deterministic replay support in production systems?▼

Market data normalization requires deterministic replay support in production systems to guarantee consistent data processing, validate ingestion behavior, and maintain point-in-time join integrity across replays.