automated-market-makers

Name: automated-market-makers
Availability: InStock
Author: ghostof0days

Automate AMM market maker workflows for invariant, liquidity, and rebalancing analysis.

Updated Feb 10, 2026

Search Tags:#quantitative finance #liquidity #defi #rebalancing #amm #market making

Ready to run

npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill automated-market-makers

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the complex workflows involved in quantitative research, implementation, and production controls for Automated Market Makers (AMMs).

Core Features & Use Cases

Invariant Behavior Analysis: Understand and model pool invariant dynamics.
Concentrated Liquidity Management: Optimize strategies for concentrated liquidity.
Rebalancing Cost Control: Analyze and manage the costs associated with rebalancing.
Use Case: When researching new AMM strategies, use this Skill to define hypotheses, build features, estimate signal edge, stress test performance across different regimes, and ensure robust net performance after trading costs.

Quick Start

Run the automated market makers diagnostics script with your input data.

Frequently Asked Questions about automated-market-makers

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

FAQPage Schema

How do I analyze AMM pool invariant behavior and concentrated liquidity?▼

To manage concentrated liquidity rebalancing costs, you can automate workflows that analyze pool invariant dynamics and estimate net performance after trading costs. This approach requires diagnostics for cost-adjusted performance and fee-income stability.

How do I measure signal edge for DeFi market making strategies?▼

Measuring signal edge for DeFi market making involves building features and running diagnostics for signal monotonicity, capacity stress, and regime dependency. This validates the robustness of quantitative research hypotheses before production deployment.

Can I use pandas for quantitative research on AMM rebalancing costs?▼

Yes, pandas is required for quantitative research on AMM rebalancing costs. The environment supports pandas to build features, estimate signal edge, and run diagnostics for cost-adjusted performance and invariant drift.

What is the best way to stress test AMM performance across different market regimes?▼

The best way to stress test AMM performance is to automate diagnostics for regime dependency, capacity stress, and invariant drift. This evaluates robust net performance after accounting for trading costs and fee-income stability.

Why do I need diagnostics for invariant drift and fee-income stability in AMM pools?▼

Diagnostics for invariant drift and fee-income stability are needed to ensure production controls function correctly and net performance remains robust after trading costs. These checks validate that pool invariant behavior stays within expected parameters.