Awesome Quant — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Awesome Quant. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: May 5, 2026 • Sentiment updated: Mar 17, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The project has a high level of engagement with 1,100+ stars and 200+ forks, indicating a strong interest in algorithmic trading and quantitative finance.

Why Awesome Quant Stands Out

Awesome Quant stands out from alternative repositories due to its comprehensive and curated list of libraries, tools, and resources for quantitative finance. The repository takes a unique approach by providing a broad range of topics, from numerical libraries and data structures to visualization and quant research environments. By leveraging the features listed in the README, such as the use of NumPy, SciPy, and pandas for data analysis, developers can build complex financial models and algorithms. The repository's focus on quantitative finance and data analysis solves the problem of finding reliable and efficient tools for financial modeling and analysis.

Built With

Build a high-performance trading bot — Awesome Quant's curated list of libraries enables the creation of fast and efficient trading algorithms, Build a quantitative finance research environment — The repository provides a comprehensive list of resources for data analysis, visualization, and modeling, Build a risk analysis tool — Awesome Quant's collection of libraries and tools allows for the development of robust risk analysis and management systems, Build a machine learning model for stock market prediction — The repository's focus on quantitative finance and data analysis makes it an ideal starting point for building predictive models, Build a data visualization dashboard for financial data — Awesome Quant's list of visualization tools and libraries enables the creation of interactive and informative dashboards

Getting Started

  1. Install the required libraries by running `pip install numpy scipy pandas`
  2. Clone the repository using `git clone https://github.com/wilsonfreitas/awesome-quant.git`
  3. Configure your environment by setting the `PYTHONPATH` to include the repository's directory
  4. Explore the repository's contents by navigating to the `awesome-quant` directory and running `python -m awesome_quant`
  5. Try building a simple trading bot using the `pyalgotrade` library to verify that it works

About

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

Official site: https://wilsonfreitas.github.io/awesome-quant/

Category & Tags

Category: trading

Tags: algorithmic-trading-engine, algorithmic-trading-library, algotrading, arbitrage-bot, awesome, awesome-list, finance, finance-api, financial-data, financial-instruments, google-finance, quant, quantitative-finance, quantitative-trading, stock-data, technical-analysis, trading-algorithms, trading-bot, trading-strategies, yahoo-finance

Market Context

This project is relevant to professionals and enthusiasts in the finance and trading industries looking for a comprehensive list of resources and tools.