Someone has compiled all the commonly used tools for quantitative funds into a free GitHub repository. Pricing engines, backtesting frameworks, order books, real-time market data, risk models – almost a complete set. The projects inside are ridiculously numerous:

• Options pricing library for calculating option and derivative values, covering multiple pricing models and risk metrics.
• Complete backtesting frameworks including Zipline, Backtrader, NautilusTrader, and LEAN. From simple strategy validation to event-driven trading and multi-asset portfolio backtesting, you can find corresponding tools.
• Matching engine and high-frequency trading tools including a matching engine that claims to handle over 150 million orders per second, and a high-frequency backtesting framework that simulates queue position, latency, and fill probability.
• Microsoft’s open-source AI quantitative platform that can use AI for data processing, factor research, strategy generation, and experiment management.
• Portfolio and risk tools including portfolio optimization, risk models, factor libraries, and alternative data sources.

Previously, one of the biggest barriers for ordinary people to do quantitative trading was tools. Now, a large number of institutional-grade frameworks have been open-sourced, and the real gap has shifted to data quality, strategy logic, execution cost, and risk control. Code being public does not mean profits are public. But at least you can now build a research environment that previously only professional teams could use, at almost zero software cost. The repository link is below.

wilsonfreitas/awesome-quant

Source: https://github.com/wilsonfreitas/awesome-quant

Awesome Quant

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

Contents

• Numerical Libraries & Data Structures
• Financial Instruments & Pricing
• Technical Indicators
• Trading & Backtesting
• Portfolio Optimization & Risk Analysis
• Factor Analysis
• Sentiment Analysis & Alternative Data
• Time Series Analysis
• Market Data & Data Sources
• Prediction Markets
• Calendars & Market Hours
• Visualization
• Excel & Spreadsheet Integration
• Quant Research Environments
• Cross-Language Frameworks
• Reproducing Works, Training & Books
• Commercial & Proprietary Services
• Related Lists

Numerical Libraries & Data Structures

• numpy - Python - NumPy is the fundamental package for scientific computing with Python. GitHub
• scipy - Python - SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. GitHub
• pandas - Python - pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. GitHub
• polars - Python - Polars is a blazingly fast DataFrame library for manipulating structured data. GitHub
• quantdsl - Python - Domain specific language for quantitative analytics in finance and trading.
• statistics - Python - Builtin Python library for all basic statistical calculations.
• sympy - Python - SymPy is a Python library for symbolic mathematics. GitHub
• pymc3 - Python - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano. GitHub
• modelx - Python - Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas. GitHub
• ArcticDB - Python - High performance datastore for time series and tick data.
• CRNG - Python - Contingency Random Number Generator that produces random numbers with real financial market statistical signatures (fat tails, volatility clustering, kurtosis). Matches 86% of real market metrics vs 14% for NumPy.
• xts - R - eXtensible Time Series: Provide for uniform handling of R’s different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
• data.table - R - Extension of data.frame: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
• sparseEigen - R - Sparse principal component analysis.
• TSdbi - R - Provides a common interface to time series databases.
• tseries - R - Time Series Analysis and Computational Finance.
• zoo - R - S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations).
• tis - R - Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.
• tfplot - R - Utilities for simple manipulation and quick plotting of time series data.
• tframe - R - A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.
• Temporal.jl - Julia - Flexible and efficient time series class & methods.
• DataFrames.jl - Julia - In-memory tabular data in Julia.
• TSFrames.jl - Julia - Handle timeseries data on top of the powerful and mature DataFrames.jl.
• TimeArrays.jl - Julia - Time series handling for Julia.

Financial Instruments & Pricing

• PyQL - Python - QuantLib’s Python port.
• pyfin - Python - Basic options pricing in Python. ARCHIVED.
• vollib - Python - vollib is a python library for calculating option prices, implied volatility and greeks.
• py_vollib - Python - vollib Python implementation.
• QuantPy - Python - A framework for quantitative finance In python.
• Finance-Python - Python - Python tools for Finance.
• ffn - Python - A financial function library for Python.
• pynance - Python - Lightweight Python library for assembling and analyzing financial data.
• tia - Python - Toolkit for integration and analysis.
• pysabr - Python - SABR model Python implementation.
• FinancePy - Python - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
• gs-quant - Python - Python toolkit for quantitative finance.
• willowtree - Python - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.
• financial-engineering - Python - Applications of Monte Carlo methods to financial engineering projects, in Python.
• optlib - Python - A library for financial options pricing written in Python.
• tf-quant-finance - Python - High-performance TensorFlow library for quantitative finance.
• Q-Fin - Python - A Python library for mathematical finance.
• Quantsbin - Python - Tools for pricing and plotting of vanilla option prices, greeks and various other analysis around them.
• finoptions - Python - Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.
• pypme - Python - PME (Public Market Equivalent) calculation.
• AbsBox - Python - A Python based library to model cashflow for structured product like Asset-backed securities (ABS) and Mortgage-backed securities (MBS).
• mortgagemath - Python - Cent-accurate mortgage amortization schedules with Decimal arithmetic and published-source validation across six countries.
• Intrinsic-Value-Calculator - Python - A Python tool for quick calculations of a stock’s fair value using Discounted Cash Flow analysis.
• Kelly-Criterion - Python - Kelly Criterion implemented in Python to size portfolios based on J. L. Kelly Jr’s formula.
• rateslib - Python - A fixed income library for pricing bonds and bond futures, and derivatives such as IRS, cross-currency and FX swaps.
• fypy - Python - Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.
• Pyderivatives - Python - Toolkit for option pricing, implied volatility surfaces, risk-neutral densities, and pricing kernel surfaces with support for advanced models including Heston, Kou, and Bates.
• quantra - Python - High-performance pricing engine built on QuantLib. It exposes QuantLib’s functionality through gRPC and REST APIs, enabling distributed computations with FlatBuffers serialization.
• optionlab - Python - A Python library for evaluating option trading strategies.
• flashalpha - Python - Python client for the FlashAlpha options analytics API.
• QuantOracle - Python - Free quant finance API with 63 deterministic endpoints + 15 free interactive calculators at quantoracle.dev. Options pricing with full Greeks, Monte Carlo, Kelly, VaR, Sharpe, CAGR, crypto liquidation, impermanent loss, plus live crypto volatility/funding data and 24/7 position monitoring with webhook alerts. 1,000 free calls/day, no API key.
• RQuantLib - R - RQuantLib connects GNU R with QuantLib.
• quantmod - R - Quantitative Financial Modelling Framework. GitHub
• Rmetrics - R - The premier open source software solution for teaching and training quantitative finance.
• fAsianOptions - EBM and Asian Option Valuation.
• fAssets - Analysing and Modelling Financial Assets.
• fBasics - Markets and Basic Statistics.
• fBonds - Bonds and Interest Rate Models.
• fExoticOptions - Exotic Option Valuation.
• fOptions - Pricing and Evaluating Basic Options.
• fPortfolio - Portfolio Selection and Optimization.
• sde - R - Simulation and Inference for Stochastic Differential Equations.
• YieldCurve - R - Modelling and estimation of the yield curve.
• SmithWilsonYieldCurve - R - Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.
• ycinterextra - R - Yield curve or zero-coupon prices interpolation and extrapolation.
• AmericanCallOpt - R - This package includes pricing function for selected American call options with underlying assets that generate payouts.
• VarSwapPrice - R - Pricing a variance swap on an equity index.
• RND - R - Risk Neutral Density Extraction Package.
• LSMonteCarlo - R - American options pricing with Least Squares Monte Carlo method.
• OptHedging - R - Estimation of value and hedging strategy of call and put options.
• tvm - R - Time Value of Money Functions.
• OptionPricing - R - Option Pricing with Efficient Simulation Algorithms.
• credule - R - Credit Default Swap Functions.
• derivmkts - R - Functions and R Code to Accompany Derivatives Markets. GitHub
• FinCal - R - Package for time value of money calculation, time series analysis and computational finance.
• r-quant - R - R code for quantitative analysis in finance.
• options.studies - R - options trading studies functions for use with options.data package and shiny.
• fmbasics - R - Financial Market Building Blocks.
• R-fixedincome - R - Fixed income tools for R.
• QuantLib.jl - Julia - Quantlib implementation in pure Julia.
• Ito.jl - Julia - A Julia package for quantitative finance.
• Miletus.jl - Julia - A financial contract definition, modeling language, and valuation framework.
• Strata - Java - Modern open-source analytics and market risk library designed and written in Java. GitHub
• JQuantLib - Java - JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java.
• finmath.net - Java - Java library with algorithms and methodologies related to mathematical finance. GitHub
• quantcomponents - Java - Free Java components for Quantitative Finance and Algorithmic Trading.
• DRIP - Java - Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries.
• finance.js - JavaScript - A JavaScript library for common financial calculations.
• [quantfin](https://github.com/bound