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wilsonfreitas/awesome-quant

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26,818 stars·3,578 forks·HTML·55 viewswilsonfreitas.github.io/awesome-quant↗

Awesome Quant

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research.

The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction of statistical model pipelines, allowing for the integration of data preprocessing, mathematical transformation, and predictive modeling.

Beyond basic analysis, the collection covers advanced capabilities including event-driven backtesting for strategy validation, portfolio optimization techniques, and market sentiment analysis. It also includes resources for automating spreadsheet workflows and establishing modular data connections to external financial exchanges.

Features

  • Quantitative Finance & Trading - Acts as a curated directory of open-source software for financial modeling, backtesting, and quantitative analysis.
  • Trading Strategy Backtesters - Simulates historical market performance of investment strategies to validate logic and assess potential risks.
  • Domain-Specific Library Aggregations - Provides curated collections of specialized software libraries and tools for quantitative finance and modeling.
  • Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
  • Algorithmic Trading - Serves as a comprehensive hub for frameworks and models used in algorithmic trading and strategy development.
  • Backtesting Engines - Simulates historical market performance of investment strategies to validate logic and assess potential risks.
  • Financial Analysis Tools - Provides curated collections of software libraries and resources for financial modeling and analysis.
  • Portfolio Optimization Algorithms - Constructs and rebalances investment allocations using risk parity or mean-variance techniques.
  • Derivative Pricing Models - Computes option values, implied volatility, and risk metrics using established mathematical models.
  • Financial Data Connectors - Standardizes communication with external financial APIs to ingest real-time and historical market data.
  • Financial Data Processing - Provides a directory of tools for processing market data and performing statistical financial research.
  • Market Data Providers - Fetches real-time or historical financial information from external exchanges to ensure current market values.
  • Financial Forecasting Models - Predicts future trends in financial datasets by applying autoregressive or machine learning models to historical data.
  • Market Sentiment Analyzers - Extracts insights from news and social media to gauge market mood and identify potential trading signals.
  • Statistical Pipelines - Chains data preprocessing, mathematical transformation, and predictive modeling steps to generate actionable insights.
  • Technical Analysis - Generates financial market signals and trading rules by applying mathematical algorithms to price and volume data.
  • Trading Platforms - Curated collection of quantitative finance and trading resources.
  • Learning and Reference - Curated list of libraries and resources for quants.
  • Awesome Lists - Quantitative finance.
  • More to explore - English-language version of the quantitative resource list.
  • High-Performance Scientific Computing - Applies high-performance data structures and statistical functions to solve complex scientific computing problems.
  • Vectorized Array Operations - Processes large financial datasets using high-performance array operations and optimized linear algebra libraries.
  • Statistical Analysis Libraries - Offers statistical functions and mathematical algorithms for generating trading signals and computing risk metrics.
  • Spreadsheet Automation - Embeds custom logic directly into spreadsheet environments to automate data extraction and reporting tasks.
  • Spreadsheet Automation Tools - Executes custom code within spreadsheets to extract data and generate reports automatically.

Star history

Star history chart for wilsonfreitas/awesome-quantStar history chart for wilsonfreitas/awesome-quant

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does wilsonfreitas/awesome-quant do?

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research.

What are the main features of wilsonfreitas/awesome-quant?

The main features of wilsonfreitas/awesome-quant are: Quantitative Finance & Trading, Trading Strategy Backtesters, Domain-Specific Library Aggregations, Awesome List, Algorithmic Trading, Backtesting Engines, Financial Analysis Tools, Portfolio Optimization Algorithms.

What are some open-source alternatives to wilsonfreitas/awesome-quant?

Open-source alternatives to wilsonfreitas/awesome-quant include: llmquant/quant-wiki — quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering,… wshobson/agents — This project is an automated trading and agentic workflow platform designed to orchestrate complex financial tasks… bbfamily/abu — Abu is an algorithmic trading framework designed for the development, backtesting, and optimization of automated… ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… mementum/backtrader — Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading… akfamily/akshare — This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets.…