hidden-liquidity-execution-probability

Estimate hidden liquidity fill probability and route orders with toxicity-aware controls.

Updated Feb 10, 2026
One-click install
npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill hidden-liquidity-execution-probability
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: hidden-liquidity-execution-probability
Source: https://github.com/GhostOf0days/codex-quant-skills/tree/main/hidden-liquidity-execution-probability
Command: npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill hidden-liquidity-execution-probability

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of efficiently and safely utilizing hidden liquidity in trading by providing tools to estimate execution probability and manage routing risks.

Core Features & Use Cases

  • Execution Probability Estimation: Quantify the likelihood of fills in dark and non-displayed venues.
  • Toxicity-Aware Routing: Route orders with confidence, considering adverse selection risk and fallback options.
  • Use Case: When deciding whether to seek hidden liquidity or route across different venues, this Skill helps by providing data-driven insights into fill likelihood and associated risks.

Quick Start

Run the diagnostics script for hidden liquidity execution probability using the provided input CSV file.

Frequently Asked Questions about hidden-liquidity-execution-probability

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

FAQPage Schema
How do I estimate hidden liquidity execution probability in dark pools?

To estimate hidden liquidity execution probability, you analyze queue positions, prints, and venue states to quantify fill likelihood in dark pools. The Skill calculates this probability while assessing adverse selection costs to compare hidden routes against lit alternatives.

How does toxicity-aware order routing manage adverse selection risk?

Toxicity-aware order routing manages adverse selection risk by analyzing venue states and prints to evaluate toxicity costs. It compares hidden routes against lit alternatives, applying fallback options to route orders with confidence and minimize toxic fills.

What inputs do I need to calculate dark pool fill probability and adverse selection rates?

Calculating dark pool fill probability and adverse selection rates requires specific diagnostics for calibration, latency impact, and signal accuracy. You must provide an input CSV file containing queue, prints, and venue state data to run the diagnostics script.

What is the best way to compare hidden liquidity routes against lit alternatives?

The best way to compare hidden liquidity routes against lit alternatives is to analyze execution probability and adverse selection cost simultaneously. By evaluating queue states and toxicity rates, you can determine whether hidden venues offer better fill outcomes than lit markets.

When should I not use hidden liquidity routing for trading algorithms?

You should not use hidden liquidity routing when adverse selection costs outweigh fill probability benefits. If diagnostics indicate high toxicity rates, significant latency impact, or poor signal accuracy, routing orders to lit alternatives provides safer execution outcomes.