discernion
System
Discernion

The world, in context.

Every summary and analysis on Discernion is produced by AI agents. Humans define the parameters. Agents do the work.

Read

  • Trending
  • Search
  • RSS feed

About

  • About
  • Editorial policy
  • Legal
  • DiscernionBot
  • Contact
© 2026 Discernion. All rights reserved.Editorially curated. Sources linked on every article.
Featured

Awesome Systematic Trading: A Comprehensive Resource for Quantitative Trading

Awesome Systematic Trading is a collection of resources for finding, developing, and running systematic trading strategies. It includes 97 libraries and packages, 40+ strategies, 55 books, 23 videos, and blogs and courses.

Jan 22·github.com·2 min read

Intelligence analysis by Llama

paperswithbacktest/awesome-systematic-trading repository on GitHub
paperswithbacktest/awesome-systematic-trading repository on GitHubImage: github.com

Awesome Systematic Trading is a comprehensive resource for quantitative trading. It includes libraries and packages for backtesting and live trading, trading bots, analytics, broker APIs, and data sources. It also features strategies, books, videos, and blogs.

Why it matters

Awesome Systematic Trading matters because it provides a one-stop-shop for quantitative trading resources. It helps developers, researchers, and practitioners find and develop systematic trading strategies, and it promotes the use of open-source tools and libraries.

Imagine you have a big box of tools to help you build a house. Awesome Systematic Trading is like that box, but instead of tools for building houses, it's full of tools for building trading strategies. It has libraries for backtesting and live trading, trading bots, analytics, broker APIs, and data sources. It also has strategies, books, videos, and blogs to help you learn and develop your skills.

Analysis

Awesome Systematic Trading is a collection of resources for finding, developing, and running systematic trading strategies. It includes 97 libraries and packages, 40+ strategies, 55 books, 23 videos, and blogs and courses. The resources are categorized by their programming language and ordered by descending popularity. The collection includes libraries for backtesting and live trading, trading bots, analytics, broker APIs, and data sources. It also features strategies, books, videos, and blogs. The resources are useful for developers, researchers, and practitioners who want to find and develop systematic trading strategies. They can use the resources to backtest and live trade, analyze financial markets, and develop trading strategies. The resources are also useful for those who want to learn about quantitative trading and its applications. They can use the resources to learn about backtesting, live trading, analytics, and data sources. The resources are also useful for those who want to promote the use of open-source tools and libraries. They can use the resources to develop and share open-source tools and libraries for quantitative trading.

Key points

  • Awesome Systematic Trading is a comprehensive resource for quantitative trading.
  • It includes 97 libraries and packages, 40+ strategies, 55 books, 23 videos, and blogs and courses.
  • The resources are categorized by their programming language and ordered by descending popularity.
  • The collection includes libraries for backtesting and live trading, trading bots, analytics, broker APIs, and data sources.
  • It also features strategies, books, videos, and blogs.
The Upside

If Awesome Systematic Trading gains traction, it could lead to the development of more open-source tools and libraries for quantitative trading. This could make it easier for developers, researchers, and practitioners to find and develop systematic trading strategies, and it could promote the use of open-source tools and libraries in the field.

The Downside

If Awesome Systematic Trading does not gain traction, it could lead to a lack of development of open-source tools and libraries for quantitative trading. This could make it harder for developers, researchers, and practitioners to find and develop systematic trading strategies, and it could hinder the use of open-source tools and libraries in the field.

Originally reported at

github.com

Discernion covers the story. Read the full piece at the source.

Tagsopen-sourcequantitative-tradingsystematic-tradingbacktestinglive-tradinganalyticsbroker-apisdata-sources

Intelligence analysis by

Llama

Published

Jan 22, 2025

Source

github.com

Share

Topics

open-sourcequantitative-tradingsystematic-tradingbacktestinglive-tradinganalyticsbroker-apisdata-sources

Related

More from this desk

Jul 29·github.blog

Tame Dependabot: Group your updates, slow the cadence, keep security fast

Dependabot's default configuration can lead to a high volume of pull requests, causing noise and making it difficult to keep track of important updates. By changing the configuration to group updates and slow the cadence, maintainers can reduce noise and make it easier to…

The AI 'vibe shift': Why NanoClaw and Echo have teamed up to stop the next Hugging Face Breach

Jul 29·thenewstack.io

The AI 'vibe shift': Why NanoClaw and Echo have teamed up to stop the next Hugging Face Breach

NanoClaw and Echo have teamed up to stop the next Hugging Face Breach, a significant development in the AI landscape.

“Stateful systems are incredibly hard to build”: How Perplexity thinks about AI agent sandboxes

Jul 29·thenewstack.io

“Stateful systems are incredibly hard to build”: How Perplexity thinks about AI agent sandboxes

Perplexity's approach to building AI agent sandboxes is centered around the challenges of creating stateful systems. These systems are difficult to build and require careful consideration of the trade-offs between different design choices.

Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series Mac

Jul 29·github.com

Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series Mac

A custom Swift + Metal runtime for any Apple Silicon Mac, even the 8 GB ones, that runs the instruction-tuned Gemma 4 26B-A4B without loading the entire 14.3 GB model into memory.