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market-transparency-offhours

Analyze off-hours market transparency by comparing lit and off-exchange execution quality.

Updated Feb 10, 2026
Search Tags:#liquidity#market microstructure#execution quality#market transparency#off-hours trading#reporting lag
One-click install
npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill market-transparency-offhours
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: market-transparency-offhours
Source: https://github.com/GhostOf0days/codex-quant-skills/tree/main/market-transparency-offhours
Command: npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill market-transparency-offhours

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of understanding and managing market transparency during off-hours trading sessions, focusing on the differences between lit and off-exchange venues and diagnosing reporting delays.

Core Features & Use Cases

  • Off-Hours Analysis: Evaluate execution quality and liquidity behavior outside regular trading hours.
  • Transparency Diagnostics: Measure and control key transparency metrics like spread, depth, and slippage.
  • Reporting Lag Monitoring: Identify and diagnose delays in trade reporting.
  • Use Case: Use this Skill to assess if your trading strategies maintain execution quality during pre-market and post-market hours, ensuring compliance with market structure governance.

Quick Start

Run the market transparency offhours diagnostics script on the input data file.

Frequently Asked Questions about market-transparency-offhours

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

FAQPage Schema
How do I analyze off-hours trading execution quality and liquidity?▼

To analyze off-hours trading execution quality, you must ingest trade and quote data with accurate timestamps to measure spreads, depth, and slippage across lit and off-exchange venues. This process evaluates liquidity behavior outside regular trading hours.

What causes trade reporting lag during low-liquidity windows?▼

Trade reporting lag during low-liquidity windows is diagnosed by stressing conditions like volatility spikes and comparing execution quality. Accurate session information and venue details are required to identify and measure these reporting delays.

How do I measure market transparency for pre-market and post-market sessions?▼

To measure market transparency for pre-market and post-market sessions, the Skill compares lit versus off-exchange execution quality using benchmark prices. It provides actionable insights for market structure governance during these specific periods.

Can I use pandas to assess off-hours market microstructure and slippage?▼

Yes, you can use pandas to assess off-hours market microstructure and slippage because pandas is the underlying dependency. The Skill processes input trade and quote data in pandas to evaluate spreads and depth during volatility spikes.

What data is needed to diagnose reporting lag and off-exchange execution quality?▼

Diagnosing reporting lag and off-exchange execution quality requires accurate timestamps, session information, venue details, and benchmark prices. This data allows the Skill to stress low-liquidity windows and measure transparency metrics effectively.

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