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awesome-quant

New
26.8kGitHubGeneralby wilsonfreitas

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

First seen 5/22/2026

Summary

This skill provides a curated list of libraries, packages, and resources for quantitative finance, helping developers quickly find tools for backtesting, trading, data analysis, and risk management.

  • It saves time by aggregating high-quality quant resources in one place.

Install & Usage

1
Create the skills directory
mkdir -p .claude/skills
2
Download the skill file

Add the configuration to .claude/skills/awesome-quant.md

3
Invoke in Claude Code
/awesome-quant

Use Cases

Find Python libraries for backtesting trading strategies.
Discover data sources for historical stock prices.
Locate risk management frameworks for portfolio optimization.
Identify machine learning tools for financial modeling.
Explore options pricing libraries and derivatives analytics.
Get recommendations for time series analysis packages.

Usage Examples

1

/awesome-quant list Python libraries for backtesting

2

Find resources for options pricing in quantitative finance

3

Show me the best data sources for historical stock data

View source on GitHub

Security Audits

LicenseUnknownSourceWarnRepositoryPass

Frequently Asked Questions

What is awesome-quant?

This skill provides a curated list of libraries, packages, and resources for quantitative finance, helping developers quickly find tools for backtesting, trading, data analysis, and risk management. It saves time by aggregating high-quality quant resources in one place.

How to install awesome-quant?

To install awesome-quant: create the skills directory (mkdir -p .claude/skills), then add the config to .claude/skills/awesome-quant.md. Finally, /awesome-quant in Claude Code.

What is awesome-quant best for?

awesome-quant is a community categorized under General. Created by wilsonfreitas.

What can I use awesome-quant for?

awesome-quant is useful for: Find Python libraries for backtesting trading strategies.; Discover data sources for historical stock prices.; Locate risk management frameworks for portfolio optimization.; Identify machine learning tools for financial modeling.; Explore options pricing libraries and derivatives analytics.; Get recommendations for time series analysis packages..