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GitHub data synced: Jan 22, 2025 • Sentiment updated: Mar 17, 2026
Community Buzz: The community is actively engaged with the project, with many users contributing to the list and providing feedback. The project's popularity is evident from its high number of stars and forks on GitHub.
This repository is valuable because it provides a comprehensive and curated list of resources for quantitative trading, including libraries, frameworks, and tools. It takes a technical approach by providing a list of specific libraries and frameworks that can be used to build and execute trading strategies. The repository solves the problem of finding and evaluating the numerous resources available for quantitative trading, making it easier for users to get started and build their own trading systems. The repository's focus on backtesting and live trading frameworks, as well as its inclusion of data science and machine learning libraries, sets it apart from other resources.
Build a quantitative trading strategy that backtests on historical data — This repository provides a curated list of 97 libraries and packages for research and live trading, including backtesting and live trading frameworks like vnpy and zipline., Build a trading bot that executes trades based on technical indicators — The repository includes a list of trading bots and analytics libraries like backtrader and QUANTAXIS that can be used to build and execute trading strategies., Build a data pipeline that fetches and processes financial data — The repository provides a list of data sources and data science libraries like finmarketpy that can be used to build a data pipeline., Build a risk management system that monitors and optimizes trading performance — The repository includes a list of risk management libraries and optimization tools like Rqalpha that can be used to build a risk management system., Build a machine learning model that predicts stock prices — The repository provides a list of machine learning libraries and tutorials that can be used to build and train a machine learning model.
A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
Official site: https://paperswithbacktest.com
Category: trading
Tags: algorithmic-trading, algotrading, alpha, arbitrage-bot, awesome, awesome-list, book, finance, futures, futures-historical-data, futures-market, futuresmarkets, paper, quant, quantitative-finance, quantitative-trading, trading-algorithms, trading-bot, trading-strategies
This project is relevant to the finance and trading industries, providing a curated list of resources for systematic trading.