market-making

Name: market-making
Availability: InStock
Author: ghostof0days

Automate cash market making with continuous two-sided quoting and inventory control.

Updated Feb 10, 2026

Search Tags:#inventory management #algorithmic trading #quoting #market making #spread capture #cash markets

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexities of continuous two-sided quoting in cash markets, focusing on inventory control, spread capture, and quote quality optimization.

Core Features & Use Cases

Automated Quoting: Dynamically adjust bid and ask quotes based on real-time market conditions, order flow, and volatility.
Inventory Management: Implement strategies to hedge inventory exposure and minimize market impact.
Performance Diagnostics: Utilize detailed metrics to monitor spread capture, adverse selection costs, and latency.
Use Case: Deploy this skill to manage a high-frequency trading strategy that continuously quotes a security, aiming to profit from the bid-ask spread while managing inventory risk.

Quick Start

Run the market making diagnostics script with your input data.

Frequently Asked Questions about market-making

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

FAQPage Schema

How do I automate two-sided quoting for cash market making?▼

Automate cash market making by running scripts that dynamically adjust bid and ask quotes based on real-time conditions, capturing spreads while controlling inventory exposure.

What is spread capture optimization in algorithmic trading?▼

Spread capture optimization in algorithmic trading profits from the bid-ask spread by continuously quoting securities. It requires performance diagnostics to monitor adverse selection costs and latency.

How do I manage inventory risk when continuously quoting securities?▼

Manage inventory risk during continuous quoting by implementing strategies that hedge exposure and minimize market impact. The workflow dynamically adjusts quotes based on real-time volatility and order flow.

Can I use pandas for high-frequency trading diagnostics?▼

Yes, you can use pandas for high-frequency trading diagnostics. The scripts utilize pandas alongside argparse and json to process input data and generate metrics for spread capture and latency monitoring.

Does this market making workflow support options trading venues?▼

No, this workflow does not support options trading venues. It is specifically designed for non-option market-making operations across various cash markets.

What metrics are needed to monitor algorithmic quoting performance?▼

Monitoring algorithmic quoting performance requires detailed metrics for spread capture, adverse selection costs, and latency. These diagnostics ensure safe deployment of risk controls within your strategy.