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Repository: wilsonfreitas/awesome-quant
Branch: master
Commit: e374d4a47ef9
Files: 20
Total size: 263.6 KB
Directory structure:
gitextract_kr02khkb/
├── .github/
│ └── workflows/
│ └── build.yml
├── .gitignore
├── .nojekyll
├── README.md
├── cran.csv
├── cranscrape.py
├── legacy.txt
├── parse.py
├── pyproject.toml
├── quants.md
├── recommendation.ipynb
├── site/
│ ├── .gitignore
│ ├── CODE_OF_CONDUCT.qmd
│ ├── _quarto.yml
│ ├── about.qmd
│ ├── index.qmd
│ ├── projects.csv
│ └── projects.qmd
├── styles.css
└── topic.py
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FILE CONTENTS
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FILE: .github/workflows/build.yml
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name: Update site
on:
schedule:
- cron: "0 1 * * *"
push:
branches: [ master ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v4
with:
python-version: '3.x'
- name: Install Poetry
run: |
pip install poetry
- name: Install dependencies
run: |
poetry install --no-root --no-interaction
- name: Run parser
run: |
export GITHUB_ACCESS_TOKEN=${{ secrets.GITHUB_TOKEN }}
poetry run python parse.py
- uses: r-lib/actions/setup-r@v2
with:
r-version: '4.2.0' # The R version to download (if necessary) and use.
- uses: r-lib/actions/setup-r-dependencies@v2
with:
packages:
any::knitr
any::rmarkdown
any::DT
- name: setup Quarto
uses: quarto-dev/quarto-actions/setup@v2
- name: Render site
run: |
quarto render site
- name: Check site
run: |
ls -l site/docs
- name: Deploy pages
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: site/docs
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FILE: .gitignore
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env.ps1
.vscode
/.quarto/
github.ipynb
build.sh
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FILE: .nojekyll
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FILE: README.md
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# Awesome Quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
[](https://awesome.re)
## Languages
- [Python](#python)
- [R](#r)
- [Matlab](#matlab)
- [Julia](#julia)
- [Java](#java)
- [JavaScript](#javascript)
- [Haskell](#haskell)
- [Scala](#scala)
- [Ruby](#ruby)
- [Elixir/Erlang](#elixirerlang)
- [Golang](#golang)
- [CPP](#cpp)
- [CSharp](#csharp)
- [Rust](#rust)
- [Frameworks](#frameworks)
- [Reproducing Works, Training & Books](#reproducing-works-training--books)
## Python
### Numerical Libraries & Data Structures
- [numpy](https://www.numpy.org) - NumPy is the fundamental package for scientific computing with Python.
- [scipy](https://www.scipy.org) - SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
- [pandas](https://pandas.pydata.org) - 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.
- [polars](https://docs.pola.rs/) - Polars is a blazingly fast DataFrame library for manipulating structured data.
- [quantdsl](https://github.com/johnbywater/quantdsl) - Domain specific language for quantitative analytics in finance and trading.
- [statistics](https://docs.python.org/3/library/statistics.html) - Builtin Python library for all basic statistical calculations.
- [sympy](https://www.sympy.org/) - SymPy is a Python library for symbolic mathematics.
- [pymc3](https://docs.pymc.io/) - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano.
- [modelx](https://docs.modelx.io/) - Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas.
- [ArcticDB](https://github.com/man-group/ArcticDB) - High performance datastore for time series and tick data.
- [pmxt](https://github.com/pmxt-dev/pmxt) - The CCXT for prediction markets. A unified API for trading on Polymarket, Kalshi, and more.
### Financial Instruments and Pricing
- [OpenBB Terminal](https://github.com/OpenBB-finance/OpenBBTerminal) - Terminal for investment research for everyone.
- [Fincept Terminal](https://github.com/Fincept-Corporation/FinceptTerminal) - Advance Data Based A.I Terminal for all Types of Financial Asset Research.
- [PyQL](https://github.com/enthought/pyql) - QuantLib's Python port.
- [pyfin](https://github.com/opendoor-labs/pyfin) - Basic options pricing in Python. *ARCHIVED*
- [vollib](https://github.com/vollib/vollib) - vollib is a python library for calculating option prices, implied volatility and greeks.
- [QuantPy](https://github.com/jsmidt/QuantPy) - A framework for quantitative finance In python.
- [Finance-Python](https://github.com/alpha-miner/Finance-Python) - Python tools for Finance.
- [ffn](https://github.com/pmorissette/ffn) - A financial function library for Python.
- [pynance](https://github.com/GriffinAustin/pynance) - Lightweight Python library for assembling and analyzing financial data.
- [tia](https://github.com/bpsmith/tia) - Toolkit for integration and analysis.
- [hasura/base-python-dash](https://platform.hasura.io/hub/projects/hasura/base-python-dash) - Hasura quick start to deploy Dash framework. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python.
- [hasura/base-python-bokeh](https://platform.hasura.io/hub/projects/hasura/base-python-bokeh) - Hasura quick start to visualize data with bokeh library.
- [pysabr](https://github.com/ynouri/pysabr) - SABR model Python implementation.
- [FinancePy](https://github.com/domokane/FinancePy) - 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](https://github.com/goldmansachs/gs-quant) - Python toolkit for quantitative finance
- [willowtree](https://github.com/federicomariamassari/willowtree) - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.
- [financial-engineering](https://github.com/federicomariamassari/financial-engineering) - Applications of Monte Carlo methods to financial engineering projects, in Python.
- [optlib](https://github.com/dbrojas/optlib) - A library for financial options pricing written in Python.
- [tf-quant-finance](https://github.com/google/tf-quant-finance) - High-performance TensorFlow library for quantitative finance.
- [Q-Fin](https://github.com/RomanMichaelPaolucci/Q-Fin) - A Python library for mathematical finance.
- [Quantsbin](https://github.com/quantsbin/Quantsbin) - Tools for pricing and plotting of vanilla option prices, greeks and various other analysis around them.
- [finoptions](https://github.com/bbcho/finoptions-dev) - Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.
- [pypme](https://github.com/ymyke/pypme) - PME (Public Market Equivalent) calculation.
- [AbsBox](https://github.com/yellowbean/AbsBox) - A Python based library to model cashflow for structured product like Asset-backed securities (ABS) and Mortgage-backed securities (MBS).
- [Intrinsic-Value-Calculator](https://github.com/akashaero/Intrinsic-Value-Calculator) - A Python tool for quick calculations of a stock's fair value using Discounted Cash Flow analysis.
- [Kelly-Criterion](https://github.com/deltaray-io/kelly-criterion) - Kelly Criterion implemented in Python to size portfolios based on J. L. Kelly Jr's formula.
- [rateslib](https://github.com/attack68/rateslib) - A fixed income library for pricing bonds and bond futures, and derivatives such as IRS, cross-currency and FX swaps.
- [fypy](https://github.com/jkirkby3/fypy) - 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.
- [quantra](https://github.com/joseprupi/quantraserver) High-performance pricing engine built on QuantLib. It exposes QuantLib's functionality through gRPC and REST APIs, enabling distributed computations with FlatBuffers serialization.
- [optionlab](https://github.com/rgaveiga/optionlab) - A Python library for evaluating option trading strategies.
### Indicators
- [pandas_talib](https://github.com/femtotrader/pandas_talib) - A Python Pandas implementation of technical analysis indicators.
- [finta](https://github.com/peerchemist/finta) - Common financial technical analysis indicators implemented in Pandas.
- [Tulipy](https://github.com/cirla/tulipy) - Financial Technical Analysis Indicator Library (Python bindings for [tulipindicators](https://github.com/TulipCharts/tulipindicators))
- [lppls](https://github.com/Boulder-Investment-Technologies/lppls) - A Python module for fitting the [Log-Periodic Power Law Singularity (LPPLS)](https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.
- [talipp](https://github.com/nardew/talipp) - Incremental technical analysis library for Python.
- [streaming_indicators](https://github.com/mr-easy/streaming_indicators) - A python library for computing technical analysis indicators on streaming data.
### Trading & Backtesting
- [the0](https://github.com/alexanderwanyoike/the0) - Self-hosted execution engine for algorithmic trading bots. Write strategies in Python, TypeScript, Rust, C++, C#, Scala, or Haskell and deploy with one command. Each bot runs in an isolated container with scheduled or streaming execution.
- [skfolio](https://github.com/skfolio/skfolio) - Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models.
- [Investing algorithm framework](https://github.com/coding-kitties/investing-algorithm-framework) - Framework for developing, backtesting, and deploying automated trading algorithms.
- [QSTrader](https://github.com/mhallsmoore/qstrader) - QSTrader backtesting simulation engine.
- [Blankly](https://github.com/Blankly-Finance/Blankly) - Fully integrated backtesting, paper trading, and live deployment.
- [TA-Lib](https://github.com/mrjbq7/ta-lib) - Python wrapper for TA-Lib (<http://ta-lib.org/>).
- [zipline](https://github.com/quantopian/zipline) - Pythonic algorithmic trading library.
- [zipline-reloaded](https://github.com/stefan-jansen/zipline-reloaded) - Zipline, a Pythonic Algorithmic Trading Library.
- [QuantSoftware Toolkit](https://github.com/QuantSoftware/QuantSoftwareToolkit) - Python-based open source software framework designed to support portfolio construction and management.
- [quantitative](https://github.com/jeffrey-liang/quantitative) - Quantitative finance, and backtesting library.
- [analyzer](https://github.com/llazzaro/analyzer) - Python framework for real-time financial and backtesting trading strategies.
- [bt](https://github.com/pmorissette/bt) - Flexible Backtesting for Python.
- [backtrader](https://github.com/backtrader/backtrader) - Python Backtesting library for trading strategies.
- [pythalesians](https://github.com/thalesians/pythalesians) - Python library to backtest trading strategies, plot charts, seamlessly download market data, analyze market patterns etc.
- [pybacktest](https://github.com/ematvey/pybacktest) - Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier.
- [pyalgotrade](https://github.com/gbeced/pyalgotrade) - Python Algorithmic Trading Library.
- [basana](https://github.com/gbeced/basana) - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
- [tradingWithPython](https://pypi.org/project/tradingWithPython/) - A collection of functions and classes for Quantitative trading.
- [ta](https://github.com/bukosabino/ta) - Technical Analysis Library using Pandas (Python)
- [algobroker](https://github.com/joequant/algobroker) - This is an execution engine for algo trading.
- [pysentosa](https://pypi.org/project/pysentosa/) - Python API for sentosa trading system.
- [finmarketpy](https://github.com/cuemacro/finmarketpy) - Python library for backtesting trading strategies and analyzing financial markets.
- [binary-martingale](https://github.com/metaperl/binary-martingale) - Computer program to automatically trade binary options martingale style.
- [fooltrader](https://github.com/foolcage/fooltrader) - the project using big-data technology to provide an uniform way to analyze the whole market.
- [zvt](https://github.com/zvtvz/zvt) - the project using sql, pandas to provide an uniform and extendable way to record data, computing factors, select securities, backtesting, realtime trading and it could show all of them in clearly charts in realtime.
- [pylivetrader](https://github.com/alpacahq/pylivetrader) - zipline-compatible live trading library.
- [pipeline-live](https://github.com/alpacahq/pipeline-live) - zipline's pipeline capability with IEX for live trading.
- [zipline-extensions](https://github.com/quantrocket-llc/zipline-extensions) - Zipline extensions and adapters for QuantRocket.
- [moonshot](https://github.com/quantrocket-llc/moonshot) - Vectorized backtester and trading engine for QuantRocket based on Pandas.
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt) - Financial portfolio optimization in python, including classical efficient frontier and advanced methods.
- [Eiten](https://github.com/tradytics/eiten) - Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios.
- [riskparity.py](https://github.com/dppalomar/riskparity.py) - fast and scalable design of risk parity portfolios with TensorFlow 2.0
- [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) - Implementations regarding "Advances in Financial Machine Learning" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling)
- [pyqstrat](https://github.com/abbass2/pyqstrat) - A fast, extensible, transparent python library for backtesting quantitative strategies.
- [NowTrade](https://github.com/edouardpoitras/NowTrade) - Python library for backtesting technical/mechanical strategies in the stock and currency markets.
- [pinkfish](https://github.com/fja05680/pinkfish) - A backtester and spreadsheet library for security analysis.
- [PRISM-INSIGHT](https://github.com/dragon1086/prism-insight) - AI-powered stock analysis system with 13 specialized agents, automated trading via KIS API, supporting Korean & US markets.
- [aat](https://github.com/timkpaine/aat) - Async Algorithmic Trading Engine
- [Backtesting.py](https://kernc.github.io/backtesting.py/) - Backtest trading strategies in Python
- [catalyst](https://github.com/enigmampc/catalyst) - An Algorithmic Trading Library for Crypto-Assets in Python
- [quantstats](https://github.com/ranaroussi/quantstats) - Portfolio analytics for quants, written in Python
- [qtpylib](https://github.com/ranaroussi/qtpylib) - QTPyLib, Pythonic Algorithmic Trading <http://qtpylib.io>
- [Quantdom](https://github.com/constverum/Quantdom) - Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]
- [freqtrade](https://github.com/freqtrade/freqtrade) - Free, open source crypto trading bot
- [algorithmic-trading-with-python](https://github.com/chrisconlan/algorithmic-trading-with-python) - Free `pandas` and `scikit-learn` resources for trading simulation, backtesting, and machine learning on financial data.
- [DeepDow](https://github.com/jankrepl/deepdow) - Portfolio optimization with deep learning
- [Qlib](https://github.com/microsoft/qlib) - An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.
- [machine-learning-for-trading](https://github.com/stefan-jansen/machine-learning-for-trading) - Code and resources for Machine Learning for Algorithmic Trading
- [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) - Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost
- [jesse](https://github.com/jesse-ai/jesse) - An advanced crypto trading bot written in Python
- [rqalpha](https://github.com/ricequant/rqalpha) - A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities.
- [FinRL-Library](https://github.com/AI4Finance-LLC/FinRL-Library) - A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020.
- [bulbea](https://github.com/achillesrasquinha/bulbea) - Deep Learning based Python Library for Stock Market Prediction and Modelling.
- [ib_nope](https://github.com/ajhpark/ib_nope) - Automated trading system for NOPE strategy over IBKR TWS.
- [OctoBot](https://github.com/Drakkar-Software/OctoBot) - Open source cryptocurrency trading bot for high frequency, arbitrage, TA and social trading with an advanced web interface.
- [bta-lib](https://github.com/mementum/bta-lib) - Technical Analysis library in pandas for backtesting algotrading and quantitative analysis.
- [Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models) - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations.
- [TuneTA](https://github.com/jmrichardson/tuneta) - TuneTA optimizes technical indicators using a distance correlation measure to a user defined target feature such as next day return.
- [AutoTrader](https://github.com/kieran-mackle/AutoTrader) - A Python-based development platform for automated trading systems - from backtesting to optimization to livetrading.
- [fast-trade](https://github.com/jrmeier/fast-trade) - A library built with backtest portability and performance in mind for backtest trading strategies.
- [qf-lib](https://github.com/quarkfin/qf-lib) - QF-Lib is a Python library that provides high quality tools for quantitative finance.
- [tda-api](https://github.com/alexgolec/tda-api) - Gather data and trade equities, options, and ETFs via TDAmeritrade.
- [vectorbt](https://github.com/polakowo/vectorbt) - Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.
- [Lean](https://github.com/QuantConnect/Lean) - Lean Algorithmic Trading Engine by QuantConnect (Python, C#).
- [fast-trade](https://github.com/jrmeier/fast-trade) - Low code backtesting library utilizing pandas and technical analysis indicators.
- [pysystemtrade](https://github.com/robcarver17/pysystemtrade) - pysystemtrade is the open source version of Robert Carver's backtesting and trading engine that implements systems according to the framework outlined in his book "Systematic Trading", which is further developed on his [blog](https://qoppac.blogspot.com/).
- [pytrendseries](https://github.com/rafa-rod/pytrendseries) - Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.
- [PyLOB](https://github.com/DrAshBooth/PyLOB) - Fully functioning fast Limit Order Book written in Python.
- [PyBroker](https://github.com/edtechre/pybroker) - Algorithmic Trading with Machine Learning.
- [OctoBot Script](https://github.com/Drakkar-Software/OctoBot-Script) - A quant framework to create cryptocurrencies strategies - from backtesting to optimization to livetrading.
- [hftbacktest](https://github.com/nkaz001/hftbacktest) - A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
- [vnpy](https://github.com/vnpy/vnpy) - VeighNa is a Python-based open source quantitative trading system development framework.
- [Intelligent Trading Bot](https://github.com/asavinov/intelligent-trading-bot) - Automatically generating signals and trading based on machine learning and feature engineering
- [fastquant](https://github.com/enzoampil/fastquant) - fastquant allows you to easily backtest investment strategies with as few as 3 lines of python code.
- [nautilus_trader](https://github.com/nautechsystems/nautilus_trader) - A high-performance algorithmic trading platform and event-driven backtester.
- [YABTE](https://github.com/bsdz/yabte) - Yet Another (Python) BackTesting Engine.
- [Trading Strategy](https://github.com/tradingstrategy-ai/getting-started) - TradingStrategy.ai is a market data, backtesting, live trading and investor management framework for decentralised finance
- [Hikyuu](https://github.com/fasiondog/hikyuu) - A base on Python/C++ open source high-performance quant framework for faster analysis and backtesting, contains the complete trading system components for reuse and combination.
- [rust_bt](https://github.com/jensnesten/rust_bt) - A high performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust.
- [Gunbot Quant](https://github.com/GuntharDeNiro/gunbot-quant) - Toolkit for quantitative trading analysis. It integrates an advanced market screener, a multi-strategy, multi-asset backtesting engine. Use with built-in GUI or through CLI.
- [StrateQueue](https://github.com/StrateQueue/StrateQueue) - An open‑source, broker‑agnostic Python library that lets you seamlessly deploy strategies from any major backtesting engine to live (or paper) trading with zero code changes and built‑in safety controls.
- [Chartscout](https://chartscout.io) - Real-time cryptocurrency chart pattern detection with automated alerts across multiple exchanges
- [DayTradingBench](https://daytradingbench.com) - Live autonomous benchmark that evaluates LLM trading performance on DAX and Nasdaq indices using identical strategies and real-time market data. API access available.
- [CoinTester](https://cointester.io) - No-code crypto backtesting platform with 100+ indicators, AI sentiment signals, and 5+ years of historical data across 1,000+ trading pairs.
- [PythonTradingFramework](https://github.com/JustinGuese/python_tradingbot_framework)  - Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.
- [QTradeX-AI-Agents](https://github.com/squidKid-deluxe/QTradeX-AI-Agents) - Example strategies for the QTradeX platfrom.
- [QTradeX-Algo-Trading-SDK](https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK) - AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
- [antback](https://github.com/ts-kontakt/antback) - A lightweight, event-loop-style backtest engine that allows a function-driven imperative style using efficient stateful helper functions and data containers.
### Risk Analysis
- [QuantLibRisks](https://github.com/auto-differentiation/QuantLib-Risks-Py) - Fast risks with QuantLib
- [XAD](https://github.com/auto-differentiation/xad-py) - Automatic Differentation (AAD) Library
- [pyfolio](https://github.com/quantopian/pyfolio) - Portfolio and risk analytics in Python.
- [empyrical](https://github.com/quantopian/empyrical) - Common financial risk and performance metrics.
- [fecon235](https://github.com/rsvp/fecon235) - Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios.
- [finance](https://pypi.org/project/finance/) - Financial Risk Calculations. Optimized for ease of use through class construction and operator overload.
- [qfrm](https://pypi.org/project/qfrm/) - Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios.
- [visualize-wealth](https://github.com/benjaminmgross/visualize-wealth) - Portfolio construction and quantitative analysis.
- [VisualPortfolio](https://github.com/wegamekinglc/VisualPortfolio) - This tool is used to visualize the performance of a portfolio.
- [universal-portfolios](https://github.com/Marigold/universal-portfolios) - Collection of algorithms for online portfolio selection.
- [FinQuant](https://github.com/fmilthaler/FinQuant) - A program for financial portfolio management, analysis and optimization.
- [Empyrial](https://github.com/ssantoshp/Empyrial) - Portfolio's risk and performance analytics and returns predictions.
- [risktools](https://github.com/bbcho/risktools-dev) - Risk tools for use within the crude and crude products trading space with partial implementation of R's PerformanceAnalytics.
- [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib) - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python.
- [empyrical-reloaded](https://github.com/stefan-jansen/empyrical-reloaded) - Common financial risk and performance metrics. [empyrical](https://github.com/quantopian/empyrical) fork.
- [pyfolio-reloaded](https://github.com/stefan-jansen/pyfolio-reloaded) - Portfolio and risk analytics in Python. [pyfolio](https://github.com/quantopian/pyfolio) fork.
- [fortitudo.tech](https://github.com/fortitudo-tech/fortitudo.tech) - Conditional Value-at-Risk (CVaR) portfolio optimization and Entropy Pooling views / stress-testing in Python.
- [Quant Lab Alpha](https://github.com/husainm97/quant-lab-alpha) — Portfolio risk decomposition and Monte Carlo simulation toolkit with factor-based modeling.
- [quantitative-finance-tools](https://github.com/omichauhan-lgtm/quantitative-finance-tools) - Library for portfolio optimization (MVO) and rigorous risk metrics (VaR/CVaR).
- [curistat](https://github.com/moxiespirit/MyClone/tree/main/volatility_platform) - Futures volatility forecasting platform for ES/NQ. Proprietary CVN rating (1-10), regime detection (CRC composite), 8 directional signals, economic event impact analytics. Includes MCP server for AI agent integration.
- [Prop Trader Compass](https://otto-ships.github.io/prop-trader-compass/) - Interactive risk and payout calculator for Futures and CFD traders; features one-time fee firm comparisons.
### Factor Analysis
- [alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive alpha factors.
- [alphalens-reloaded](https://github.com/stefan-jansen/alphalens-reloaded) - Performance analysis of predictive (alpha) stock factors.
- [Spectre](https://github.com/Heerozh/spectre) - GPU-accelerated Factors analysis library and Backtester
- [quant-lab-alpha](https://github.com/husainm97/quant-lab-alpha) - Open-source investment analytics platform bridging academic research and retail finance.
### Sentiment Analysis
- [Asset News Sentiment Analyzer](https://github.com/KVignesh122/AssetNewsSentimentAnalyzer) - Sentiment analysis and report generation package for financial assets and securities utilizing GPT models.
- [Social Stock Sentiment API](https://api.adanos.org/docs) - REST API analyzing Reddit and X/Twitter for stock mentions and sentiment, providing buzz scores, trending stocks, and AI-generated trend explanations.
### Quant Research Environment
- [Jupyter Quant](https://github.com/gnzsnz/jupyter-quant) - A dockerized Jupyter quant research environment with preloaded tools for quant analysis, statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, etc.
### Time Series
- [ARCH](https://github.com/bashtage/arch) - ARCH models in Python.
- [statsmodels](http://statsmodels.sourceforge.net) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
- [dynts](https://github.com/quantmind/dynts) - Python package for timeseries analysis and manipulation.
- [PyFlux](https://github.com/RJT1990/pyflux) - Python library for timeseries modelling and inference (frequentist and Bayesian) on models.
- [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.
- [hasura/quandl-metabase](https://platform.hasura.io/hub/projects/anirudhm/quandl-metabase-time-series) - Hasura quickstart to visualize Quandl's timeseries datasets with Metabase.
- [Facebook Prophet](https://github.com/facebook/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- [tsmoothie](https://github.com/cerlymarco/tsmoothie) - A python library for time-series smoothing and outlier detection in a vectorized way.
- [pmdarima](https://github.com/alkaline-ml/pmdarima) - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
- [gluon-ts](https://github.com/awslabs/gluon-ts) - vProbabilistic time series modeling in Python.
- [functime](https://github.com/functime-org/functime) - Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.
### Calendars
- [exchange_calendars](https://github.com/gerrymanoim/exchange_calendars) - Stock Exchange Trading Calendars.
- [bizdays](https://github.com/wilsonfreitas/python-bizdays) - Business days calculations and utilities.
- [pandas_market_calendars](https://github.com/rsheftel/pandas_market_calendars) - Exchange calendars to use with pandas for trading applications.
### Data Sources
- [goMacro.ai](https://gomacro.ai) - AI-powered economic calendar with institutional-grade insights, bull/bear/base case scenario planning for NFP, CPI, PPI and other macro data releases.
- [StockAPI](https://stockapi.com.cn) – Free real-time Chinese stock data (REST & WebSocket).
- [yfinance](https://github.com/ranaroussi/yfinance) - Yahoo! Finance market data downloader (+faster Pandas Datareader)
- [defeatbeta-api](https://github.com/defeat-beta/defeatbeta-api) - An open-source alternative to Yahoo Finance's market data APIs with higher reliability.
- [findatapy](https://github.com/cuemacro/findatapy) - Python library to download market data via Bloomberg, Quandl, Yahoo etc.
- [googlefinance](https://github.com/hongtaocai/googlefinance) - Python module to get real-time stock data from Google Finance API.
- [yahoo-finance](https://github.com/lukaszbanasiak/yahoo-finance) - Python module to get stock data from Yahoo! Finance.
- [pandas-datareader](https://github.com/pydata/pandas-datareader) - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
- [pandas-finance](https://github.com/davidastephens/pandas-finance) - High level API for access to and analysis of financial data.
- [pyhoofinance](https://github.com/innes213/pyhoofinance) - Rapidly queries Yahoo Finance for multiple tickers and returns typed data for analysis.
- [yfinanceapi](https://github.com/Karthik005/yfinanceapi) - Finance API for Python.
- [yql-finance](https://github.com/slawek87/yql-finance) - yql-finance is simple and fast. API returns stock closing prices for current period of time and current stock ticker (i.e. APPL, GOOGL).
- [ystockquote](https://github.com/cgoldberg/ystockquote) - Retrieve stock quote data from Yahoo Finance.
- [wallstreet](https://github.com/mcdallas/wallstreet) - Real time stock and option data.
- [stock_extractor](https://github.com/ZachLiuGIS/stock_extractor) - General Purpose Stock Extractors from Online Resources.
- [Stockex](https://github.com/cttn/Stockex) - Python wrapper for Yahoo! Finance API.
- [StockAInsights](https://stockainsights.com) - AI-extracted financial statements API covering SEC filings including foreign filers (20-F, 6-K, 40-F), normalized quarterly and annual data from 2014+.
- [finsymbols](https://github.com/skillachie/finsymbols) - Obtains stock symbols and relating information for SP500, AMEX, NYSE, and NASDAQ.
- [FRB](https://github.com/avelkoski/FRB) - Python Client for FRED® API.
- [inquisitor](https://github.com/econdb/inquisitor) - Python Interface to Econdb.com API.
- [yfi](https://github.com/nickelkr/yfi) - Yahoo! YQL library.
- [chinesestockapi](https://pypi.org/project/chinesestockapi/) - Python API to get Chinese stock price.
- [exchange](https://github.com/akarat/exchange) - Get current exchange rate.
- [ticks](https://github.com/jamescnowell/ticks) - Simple command line tool to get stock ticker data.
- [pybbg](https://github.com/bpsmith/pybbg) - Python interface to Bloomberg COM APIs.
- [ccy](https://github.com/lsbardel/ccy) - Python module for currencies.
- [tushare](https://pypi.org/project/tushare/) - A utility for crawling historical and Real-time Quotes data of China stocks.
- [jsm](https://pypi.org/project/jsm/) - Get the japanese stock market data.
- [edinet-mcp](https://github.com/ajtgjmdjp/edinet-mcp) - Parse Japanese XBRL financial statements from EDINET with 161 normalized labels, 26 financial metrics, and multi-company screening.
- [estat-mcp](https://github.com/ajtgjmdjp/estat-mcp) - Access Japanese government statistics (e-Stat) covering population, GDP, CPI, labor, and trade data with MCP integration and Polars export.
- [tdnet-disclosure-mcp](https://github.com/ajtgjmdjp/tdnet-disclosure-mcp) - Access Japanese timely disclosures (TDNet) via MCP. Retrieve earnings, dividends, forecasts, buybacks, and other filings for 4,000+ listed companies. No API key required.
- [cn_stock_src](https://github.com/jealous/cn_stock_src) - Utility for retrieving basic China stock data from different sources.
- [coinmarketcap](https://github.com/barnumbirr/coinmarketcap) - Python API for coinmarketcap.
- [coinpulse](https://github.com/soutone/coinpulse-python) - Python SDK for cryptocurrency portfolio tracking with real-time prices, P/L calculations, and price alerts. Free tier available.
- [after-hours](https://github.com/datawrestler/after-hours) - Obtain pre market and after hours stock prices for a given symbol.
- [bronto-python](https://pypi.org/project/bronto-python/) - Bronto API Integration for Python.
- [pytdx](https://github.com/rainx/pytdx) - Python Interface for retrieving chinese stock realtime quote data from TongDaXin Nodes.
- [pdblp](https://github.com/matthewgilbert/pdblp) - A simple interface to integrate pandas and the Bloomberg Open API.
- [tiingo](https://github.com/hydrosquall/tiingo-python) - Python interface for daily composite prices/OHLC/Volume + Real-time News Feeds, powered by the Tiingo Data Platform.
- [iexfinance](https://github.com/addisonlynch/iexfinance) - Python Interface for retrieving real-time and historical prices and equities data from The Investor's Exchange.
- [pyEX](https://github.com/timkpaine/pyEX) - Python interface to IEX with emphasis on pandas, support for streaming data, premium data, points data (economic, rates, commodities), and technical indicators.
- [alpaca-trade-api](https://github.com/alpacahq/alpaca-trade-api-python) - Python interface for retrieving real-time and historical prices from Alpaca API as well as trade execution.
- [metatrader5](https://pypi.org/project/MetaTrader5/) - API Connector to MetaTrader 5 Terminal
- [akshare](https://github.com/jindaxiang/akshare) - AkShare is an elegant and simple financial data interface library for Python, built for human beings! <https://akshare.readthedocs.io>
- [yahooquery](https://github.com/dpguthrie/yahooquery) - Python interface for retrieving data through unofficial Yahoo Finance API.
- [investpy](https://github.com/alvarobartt/investpy) - Financial Data Extraction from Investing.com with Python! <https://investpy.readthedocs.io/>
- [yliveticker](https://github.com/yahoofinancelive/yliveticker) - Live stream of market data from Yahoo Finance websocket.
- [bbgbridge](https://github.com/ran404/bbgbridge) - Easy to use Bloomberg Desktop API wrapper for Python.
- [polygon.io](https://github.com/polygon-io/client-python) - A python library for Polygon.io financial data APIs.
- [alpha_vantage](https://github.com/RomelTorres/alpha_vantage) - A python wrapper for Alpha Vantage API for financial data.
- [oilpriceapi](https://github.com/OilpriceAPI/python-sdk) - Python SDK for real-time oil and commodity prices (WTI, Brent, Urals, natural gas, coal) with OpenBB integration.
- [FinanceDataReader](https://github.com/FinanceData/FinanceDataReader) - Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks
- [pystlouisfed](https://github.com/TomasKoutek/pystlouisfed) - Python client for Federal Reserve Bank of St. Louis API - FRED, ALFRED, GeoFRED and FRASER.
- [python-bcb](https://github.com/wilsonfreitas/python-bcb) - Python interface to Brazilian Central Bank web services.
- [brapi.dev](https://brapi.dev/) - Brazilian stock market data API for B3/Bovespa quotes, historical OHLCV, dividends, and fundamentals.
- [market-prices](https://github.com/maread99/market_prices) - Create meaningful OHLCV datasets from knowledge of [exchange-calendars](https://github.com/gerrymanoim/exchange_calendars) (works out-the-box with data from Yahoo Finance).
- [tardis-python](https://github.com/tardis-dev/tardis-python) - Python interface for Tardis.dev high frequency crypto market data
- [lake-api](https://github.com/crypto-lake/lake-api) - Python interface for Crypto Lake high frequency crypto market data
- [tessa](https://github.com/ymyke/tessa) - simple, hassle-free access to price information of financial assets (currently based on yfinance and pycoingecko), including search and a symbol class.
- [pandaSDMX](https://github.com/dr-leo/pandaSDMX) - Python package that implements SDMX 2.1 (ISO 17369:2013), a format for exchange of statistical data and metadata used by national statistical agencies, central banks, and international organisations.
- [cif](https://github.com/LenkaV/CIF) - Python package that include few composite indicators, which summarize multidimensional relationships between individual economic indicators.
- [finagg](https://github.com/theOGognf/finagg) - finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML.
- [FinanceDatabase](https://github.com/JerBouma/FinanceDatabase) - This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
- [Trading Strategy](https://github.com/tradingstrategy-ai/trading-strategy/) - download price data for decentralised exchanges and lending protocols (DeFi)
- [datamule-python](https://github.com/john-friedman/datamule-python) - A package to work with SEC data. Incorporates datamule endpoints.
- [13F Insight](https://13finsight.com/) - Track institutional investor 13F holdings with AI-powered analysis, position change alerts, and filing summaries.
- [Earnings Feed](https://earningsfeed.com/api) - Real-time SEC filings, insider trades, and institutional holdings API.
- [Financial Data](https://financialdata.net/) - Stock Market and Financial Data API.
- [SaxoOpenAPI](https://www.developer.saxo/) - Saxo Bank financial data API.
- [fsynth](https://github.com/welcra/fsynth) - Python library for high-fidelity unlimited synthetic financial data generation using Heston Stochastic Volatility and Merton Jump Diffusion.
- [fedfred](https://nikhilxsunder.github.io/fedfred/) - FRED & GeoFRED Economic data API with preprocessed dataframe output in pandas/geopandas, polars/polars_st, and dask dataframes/geodataframes.
- [edgar-sec](https://nikhilxsunder.github.io/edgar-sec/) - EDGAR Financial data API with preprocessed dataclass outputs.
- [edgartools](https://github.com/dgunning/edgartools) - AI-native SEC EDGAR library with XBRL financials, clean text extraction, 17+ typed forms, and pandas DataFrames.
- [FXMacroData](https://fxmacrodata.com/) - Real-time forex macroeconomic API for all major currency pairs sourced from central bank announcements.
- [wallstreet](https://github.com/mcdallas/wallstreet) - Real time stock and option data.
### Excel Integration
- [xlwings](https://www.xlwings.org/) - Make Excel fly with Python.
- [openpyxl](https://openpyxl.readthedocs.io/en/latest/) - Read/Write Excel 2007 xlsx/xlsm files.
- [xlrd](https://github.com/python-excel/xlrd) - Library for developers to extract data from Microsoft Excel spreadsheet files.
- [xlsxwriter](https://xlsxwriter.readthedocs.io/) - Write files in the Excel 2007+ XLSX file format.
- [xlwt](https://github.com/python-excel/xlwt) - Library to create spreadsheet files compatible with MS Excel 97/2000/XP/2003 XLS files, on any platform.
- [DataNitro](https://datanitro.com/) - DataNitro also offers full-featured Python-Excel integration, including UDFs. Trial downloads are available, but users must purchase a license.
- [xlloop](http://xlloop.sourceforge.net) - XLLoop is an open source framework for implementing Excel user-defined functions (UDFs) on a centralised server (a function server).
- [expy](http://www.bnikolic.co.uk/expy/expy.html) - The ExPy add-in allows easy use of Python directly from within an Microsoft Excel spreadsheet, both to execute arbitrary code and to define new Excel functions.
- [pyxll](https://www.pyxll.com) - PyXLL is an Excel add-in that enables you to extend Excel using nothing but Python code.
### Visualization
- [D-Tale](https://github.com/man-group/dtale) - Visualizer for pandas dataframes and xarray datasets.
- [mplfinance](https://github.com/matplotlib/mplfinance) - matplotlib utilities for the visualization, and visual analysis, of financial data.
- [finplot](https://github.com/highfestiva/finplot) - Performant and effortless finance plotting for Python.
- [finvizfinance](https://github.com/lit26/finvizfinance) - Finviz analysis python library.
- [market-analy](https://github.com/maread99/market_analy) - Analysis and interactive charting using [market-prices](https://github.com/maread99/market_prices) and bqplot.
- [QuantInvestStrats](https://github.com/ArturSepp/QuantInvestStrats) - Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.
- [rallyplot](https://rallyplot.com) - Fast, GPU-accelerated financial plotting library
## R
### Numerical Libraries & Data Structures
- [xts](https://github.com/joshuaulrich/xts) - 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](https://github.com/Rdatatable/data.table) - 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](https://github.com/dppalomar/sparseEigen) - Sparse principal component analysis.
- [TSdbi](http://tsdbi.r-forge.r-project.org/) - Provides a common interface to time series databases.
- [tseries](https://cran.r-project.org/web/packages/tseries/index.html) - Time Series Analysis and Computational Finance.
- [zoo](https://cran.r-project.org/web/packages/zoo/index.html) - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).
- [tis](https://cran.r-project.org/web/packages/tis/index.html) - Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.
- [tfplot](https://cran.r-project.org/web/packages/tfplot/index.html) - Utilities for simple manipulation and quick plotting of time series data.
- [tframe](https://cran.r-project.org/web/packages/tframe/index.html) - A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.
### Data Sources
- [IBrokers](https://cran.r-project.org/web/packages/IBrokers/index.html) - Provides native R access to Interactive Brokers Trader Workstation API.
- [Rblpapi](https://github.com/Rblp/Rblpapi) - An R Interface to 'Bloomberg' is provided via the 'Blp API'.
- [Quandl](https://www.quandl.com/tools/r) - Get Financial Data Directly Into R.
- [Rbitcoin](https://github.com/jangorecki/Rbitcoin) - Unified markets API interface (bitstamp, kraken, btce, bitmarket).
- [GetTDData](https://github.com/msperlin/GetTDData) - Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto.
- [GetHFData](https://github.com/msperlin/GetHFData) - Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site.
- [Reddit WallstreetBets API](https://dashboard.nbshare.io/apps/reddit/api/) - Provides daily top 50 stocks from reddit (subreddit) Wallstreetbets and their sentiments via the API.
- [td](https://github.com/eddelbuettel/td) - Interfaces the 'twelvedata' API for stocks and (digital and standard) currencies.
- [rbcb](https://github.com/wilsonfreitas/rbcb) - R interface to Brazilian Central Bank web services.
- [rb3](https://github.com/ropensci/rb3) - A bunch of downloaders and parsers for data delivered from B3.
- [simfinapi](https://github.com/matthiasgomolka/simfinapi) - Makes 'SimFin' data (<https://simfin.com/>) easily accessible in R.
- [tidyfinance](https://github.com/tidy-finance/r-tidyfinance) - Tidy Finance helper functions to download financial data and process the raw data into a structured Format (tidy data), including
date conversion, scaling factor values, and filtering by the specified date.
### Financial Instruments and Pricing
- [RQuantLib](https://github.com/eddelbuettel/rquantlib) - RQuantLib connects GNU R with QuantLib.
- [quantmod](https://cran.r-project.org/web/packages/quantmod/index.html) - Quantitative Financial Modelling Framework.
- [Rmetrics](https://www.rmetrics.org) - The premier open source software solution for teaching and training quantitative finance.
- [fAsianOptions](https://cran.r-project.org/web/packages/fAsianOptions/index.html) - EBM and Asian Option Valuation.
- [fAssets](https://cran.r-project.org/web/packages/fAssets/index.html) - Analysing and Modelling Financial Assets.
- [fBasics](https://cran.r-project.org/web/packages/fBasics/index.html) - Markets and Basic Statistics.
- [fBonds](https://cran.r-project.org/web/packages/fBonds/index.html) - Bonds and Interest Rate Models.
- [fExoticOptions](https://cran.r-project.org/web/packages/fExoticOptions/index.html) - Exotic Option Valuation.
- [fOptions](https://cran.r-project.org/web/packages/fOptions/index.html) - Pricing and Evaluating Basic Options.
- [fPortfolio](https://cran.r-project.org/web/packages/fPortfolio/index.html) - Portfolio Selection and Optimization.
- [portfolio](https://github.com/dgerlanc/portfolio) - Analysing equity portfolios.
- [sparseIndexTracking](https://github.com/dppalomar/sparseIndexTracking) - Portfolio design to track an index.
- [covFactorModel](https://github.com/dppalomar/covFactorModel) - Covariance matrix estimation via factor models.
- [riskParityPortfolio](https://github.com/dppalomar/riskParityPortfolio) - Blazingly fast design of risk parity portfolios.
- [sde](https://cran.r-project.org/web/packages/sde/index.html) - Simulation and Inference for Stochastic Differential Equations.
- [YieldCurve](https://cran.r-project.org/web/packages/YieldCurve/index.html) - Modelling and estimation of the yield curve.
- [SmithWilsonYieldCurve](https://cran.r-project.org/web/packages/SmithWilsonYieldCurve/index.html) - Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.
- [ycinterextra](https://cran.r-project.org/web/packages/ycinterextra/index.html) - Yield curve or zero-coupon prices interpolation and extrapolation.
- [AmericanCallOpt](https://cran.r-project.org/web/packages/AmericanCallOpt/index.html) - This package includes pricing function for selected American call options with underlying assets that generate payouts.
- [VarSwapPrice](https://cran.r-project.org/web/packages/VarSwapPrice/index.html) - Pricing a variance swap on an equity index.
- [RND](https://cran.r-project.org/web/packages/RND/index.html) - Risk Neutral Density Extraction Package.
- [LSMonteCarlo](https://cran.r-project.org/web/packages/LSMonteCarlo/index.html) - American options pricing with Least Squares Monte Carlo method.
- [OptHedging](https://cran.r-project.org/web/packages/OptHedging/index.html) - Estimation of value and hedging strategy of call and put options.
- [tvm](https://cran.r-project.org/web/packages/tvm/index.html) - Time Value of Money Functions.
- [OptionPricing](https://cran.r-project.org/web/packages/OptionPricing/index.html) - Option Pricing with Efficient Simulation Algorithms.
- [credule](https://github.com/blenezet/credule) - Credit Default Swap Functions.
- [derivmkts](https://cran.r-project.org/web/packages/derivmkts/index.html) - Functions and R Code to Accompany Derivatives Markets.
- [FinCal](https://github.com/felixfan/FinCal) - Package for time value of money calculation, time series analysis and computational finance.
- [r-quant](https://github.com/artyyouth/r-quant) - R code for quantitative analysis in finance.
- [options.studies](https://github.com/taylorizing/options.studies) - options trading studies functions for use with options.data package and shiny.
- [PortfolioAnalytics](https://github.com/braverock/PortfolioAnalytics) - Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios.
- [fmbasics](https://github.com/imanuelcostigan/fmbasics) - Financial Market Building Blocks.
- [R-fixedincome](https://github.com/wilsonfreitas/R-fixedincome) - Fixed income tools for R.
### Trading
- [backtest](https://cran.r-project.org/web/packages/backtest/index.html) - Exploring Portfolio-Based Conjectures About Financial Instruments.
- [pa](https://cran.r-project.org/web/packages/pa/index.html) - Performance Attribution for Equity Portfolios.
- [TTR](https://github.com/joshuaulrich/TTR) - Technical Trading Rules.
- [QuantTools](https://quanttools.bitbucket.io/_site/index.html) - Enhanced Quantitative Trading Modelling.
- [blotter](https://github.com/braverock/blotter) - Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.
### Backtesting
- [quantstrat](https://github.com/braverock/quantstrat) - Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other financial research.
### Risk Analysis
- [PerformanceAnalytics](https://github.com/braverock/PerformanceAnalytics) - Econometric tools for performance and risk analysis.
### Factor Analysis
- [FactorAnalytics](https://github.com/braverock/FactorAnalytics) - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.
- [Expected Returns](https://github.com/JustinMShea/ExpectedReturns) - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.
### Time Series
- [tseries](https://cran.r-project.org/web/packages/tseries/index.html) - Time Series Analysis and Computational Finance.
- [fGarch](https://cran.r-project.org/web/packages/fGarch/index.html) - Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.
- [timeSeries](https://cran.r-project.org/web/packages/timeSeries/index.html) - Rmetrics - Financial Time Series Objects.
- [rugarch](https://github.com/alexiosg/rugarch) - Univariate GARCH Models.
- [rmgarch](https://github.com/alexiosg/rmgarch) - Multivariate GARCH Models.
- [tidypredict](https://github.com/edgararuiz/tidypredict) - Run predictions inside the database <https://tidypredict.netlify.com/>.
- [tidyquant](https://github.com/business-science/tidyquant) - Bringing financial analysis to the tidyverse.
- [timetk](https://github.com/business-science/timetk) - A toolkit for working with time series in R.
- [tibbletime](https://github.com/business-science/tibbletime) - Built on top of the tidyverse, tibbletime is an extension that allows for the creation of time aware tibbles through the setting of a time index.
- [matrixprofile](https://github.com/matrix-profile-foundation/matrixprofile) - Time series data mining library built on top of the novel Matrix Profile data structure and algorithms.
- [garchmodels](https://github.com/AlbertoAlmuinha/garchmodels) - A parsnip backend for GARCH models.
### Calendars
- [timeDate](https://cran.r-project.org/web/packages/timeDate/index.html) - Chronological and Calendar Objects
- [bizdays](https://github.com/wilsonfreitas/R-bizdays) - Business days calculations and utilities
## Matlab
### Alternatives
- [RunMat](https://runmat.org) - High performance, Open Source, MATLAB syntax runtime.
### FrameWorks
- [QUANTAXIS](https://github.com/yutiansut/quantaxis) - Integrated Quantitative Toolbox with Matlab.
- [PROJ_Option_Pricing_Matlab](https://github.com/jkirkby3/PROJ_Option_Pricing_Matlab) - Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader
## Julia
- [CcyConv.jl](https://github.com/bhftbootcamp/CcyConv.jl) - Currency conversion library for Julia
- [CryptoExchangeAPIs.jl](https://github.com/bhftbootcamp/CryptoExchangeAPIs.jl) - A Julia library for cryptocurrency exchange APIs
- [Fastback.jl](https://github.com/rbeeli/Fastback.jl) - Blazing fast Julia backtester.
- [Lucky.jl](https://github.com/oliviermilla/Lucky.jl) - Modular, asynchronous trading engine in pure Julia.
- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) - Quantlib implementation in pure Julia.
- [Ito.jl](https://github.com/aviks/Ito.jl) - A Julia package for quantitative finance.
- [LightweightCharts.jl](https://github.com/bhftbootcamp/LightweightCharts.jl) - Julia wrapper for Lightweight Charts™ by TradingView.
- [TALib.jl](https://github.com/femtotrader/TALib.jl) - A Julia wrapper for TA-Lib.
- [Miletus.jl](https://github.com/JuliaComputing/Miletus.jl) - A financial contract definition, modeling language, and valuation framework.
- [Temporal.jl](https://github.com/dysonance/Temporal.jl) - Flexible and efficient time series class & methods.
- [Indicators.jl](https://github.com/dysonance/Indicators.jl) - Financial market technical analysis & indicators on top of Temporal.
- [Strategems.jl](https://github.com/dysonance/Strategems.jl) - Quantitative systematic trading strategy development and backtesting.
- [TimeSeries.jl](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia.
- [TechnicalIndicatorCharts.jl](https://github.com/g-gundam/TechnicalIndicatorCharts.jl) - Visualize OnlineTechnicalIndicators.jl using LightweightCharts.jl.
- [MarketTechnicals.jl](https://github.com/JuliaQuant/MarketTechnicals.jl) - Technical analysis of financial time series on top of TimeSeries.
- [MarketData.jl](https://github.com/JuliaQuant/MarketData.jl) - Time series market data.
- [OnlineTechnicalIndicators.jl](https://github.com/femtotrader/OnlineTechnicalIndicators.jl) - Julia Technical Analysis Indicators via online algorithms.
- [OnlinePortfolioAnalytics.jl](https://github.com/femtotrader/OnlinePortfolioAnalytics.jl) - A Julia quantitative portfolio analytics (risk / performance) via online algorithms.
- [OnlineResamplers.jl](https://github.com/femtotrader/OnlineResamplers.jl) - High-performance Julia package for real-time resampling of financial market data.
- [RiskPerf.jl](https://github.com/rbeeli/RiskPerf.jl) - Quantitative risk and performance analysis package for financial time series powered by the Julia language.
- [TimeFrames.jl](https://github.com/femtotrader/TimeFrames.jl) - A Julia library that defines TimeFrame (essentially for resampling TimeSeries).
- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) - In-memory tabular data in Julia
- [TSFrames.jl](https://github.com/xKDR/TSFrames.jl) - Handle timeseries data on top of the powerful and mature DataFrames.jl
- [TimeArrays.jl](https://github.com/bhftbootcamp/TimeArrays.jl) - Time series handling for Julia
## Java
- [Strata](http://strata.opengamma.io/) - Modern open-source analytics and market risk library designed and written in Java.
- [JQuantLib](https://github.com/frgomes/jquantlib) - JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java.
- [finmath.net](http://finmath.net) - Java library with algorithms and methodologies related to mathematical finance.
- [quantcomponents](https://github.com/lsgro/quantcomponents) - Free Java components for Quantitative Finance and Algorithmic Trading.
- [DRIP](https://lakshmidrip.github.io/DRIP) - Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries.
- [ta4j](https://github.com/ta4j/ta4j) - A Java library for technical analysis.
## JavaScript
- [finance.js](https://github.com/ebradyjobory/finance.js) - A JavaScript library for common financial calculations.
- [portfolio-allocation](https://github.com/lequant40/portfolio_allocation_js) - PortfolioAllocation is a JavaScript library designed to help constructing financial portfolios made of several assets: bonds, commodities, cryptocurrencies, currencies, exchange traded funds (ETFs), mutual funds, stocks...
- [Ghostfolio](https://github.com/ghostfolio/ghostfolio) - Wealth management software to keep track of financial assets like stocks, ETFs or cryptocurrencies and make solid, data-driven investment decisions.
- [IndicatorTS](https://github.com/cinar/indicatorts) - Indicator is a TypeScript module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.
- [chart-patterns](https://github.com/focus1691/chart-patterns) - Technical analysis library for Market Profile, Volume Profile, Stacked Imbalances and High Volume Node indicators.
- [orderflow](https://github.com/focus1691/orderflow) - Orderflow trade aggregator for building Footprint Candles from exchange websocket data.
- [ccxt](https://github.com/ccxt/ccxt) - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges.
- [PENDAX](https://github.com/CompendiumFi/PENDAX-SDK) - Javascript SDK for Trading/Data API and Websockets for FTX, FTXUS, OKX, Bybit, & More.
- [pmxt](https://github.com/pmxt-dev/pmxt) - The CCXT for prediction markets. A unified API for trading on Polymarket, Kalshi, and more.
- [pmxt](https://github.com/qoery-com/pmxt) - A unified API for accessing prediction market data across multiple exchanges. CCXT for prediction markets.
### Data Visualization
- [QUANTAXIS_Webkit](https://github.com/yutiansut/QUANTAXIS_Webkit) - An awesome visualization center based on quantaxis.
## Haskell
- [quantfin](https://github.com/boundedvariation/quantfin) - quant finance in pure haskell.
- [Haxcel](https://github.com/MarcusRainbow/Haxcel) - Excel Addin for Haskell.
- [Ffinar](https://github.com/MarcusRainbow/Ffinar) - A financial maths library in Haskell.
## Scala
- [QuantScale](https://github.com/choucrifahed/quantscale) - Scala Quantitative Finance Library.
- [Scala Quant](https://github.com/frankcash/Scala-Quant) - Scala library for working with stock data from IFTTT recipes or Google Finance.
## Ruby
- [Jiji](https://github.com/unageanu/jiji2) - Open Source Forex algorithmic trading framework using OANDA REST API.
## Elixir/Erlang
- [Tai](https://github.com/fremantle-capital/tai) - Open Source composable, real time, market data and trade execution toolkit.
- [Workbench](https://github.com/fremantle-industries/workbench) - From Idea to Execution - Manage your trading operation across a globally distributed cluster
- [Prop](https://github.com/fremantle-industries/prop) - An open and opinionated trading platform using productive & familiar open source libraries and tools for strategy research, execution and operation.
## Golang
- [Kelp](https://github.com/stellar/kelp) - Kelp is an open-source Golang algorithmic cryptocurrency trading bot that runs on centralized exchanges and Stellar DEX (command-line usage and desktop GUI).
- [marketstore](https://github.com/alpacahq/marketstore) - DataFrame Server for Financial Timeseries Data.
- [IndicatorGo](https://github.com/cinar/indicator) - IndicatorGo is a Golang module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.
## CPP
- [QuantLib](https://github.com/lballabio/QuantLib) - The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.
- [QuantLibRisks](https://github.com/auto-differentiation/QuantLib-Risks-Cpp) - Fast risks with QuantLib in C++
- [XAD](https://github.com/auto-differentiation/xad) - Automatic Differentation (AAD) Library
- [TradeFrame](https://github.com/rburkholder/trade-frame) - C++ 17 based framework/library (with sample applications) for testing options based automated trading ideas using DTN IQ real time data feed and Interactive Brokers (TWS API) for trade execution. Comes with built-in [Option Greeks/IV](https://github.com/rburkholder/trade-frame/tree/master/lib/TFOptions) calculation library.
- [Hikyuu](https://github.com/fasiondog/hikyuu) - A base on Python/C++ open source high-performance quant framework for faster analysis and backtesting, contains the complete trading system components for reuse and combination. You can use python or c++ freely.
- [OrderMatchingEngine](https://github.com/PIYUSH-KUMAR1809/order-matching-engine) - A production-grade, lock-free, high-frequency trading matching engine achieving 150M+ orders/sec.
- [rallyplot](https://rallyplot.com) - Fast, GPU-accelerated financial plotting library
- [PandoraTrader](https://github.com/pegasusTrader/PandoraTrader) - A C++ CTP trading framework, with very clear logic
## Frameworks
- [QuantLib](https://github.com/lballabio/QuantLib) - The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.
- QuantLibRisks - Fast risks with QuantLib in [Python](https://pypi.org/project/QuantLib-Risks/) and [C++](https://github.com/auto-differentiation/QuantLib-Risks-Cpp)
- XAD - Automatic Differentiation (AAD) Library in [Python](https://pypi.org/project/xad/) and [C++](https://github.com/auto-differentiation/xad/)
- [JQuantLib](https://github.com/frgomes/jquantlib) - Java port.
- [RQuantLib](https://github.com/eddelbuettel/rquantlib) - R port.
- [QuantLibAddin](https://www.quantlib.org/quantlibaddin/) - Excel support.
- [QuantLibXL](https://www.quantlib.org/quantlibxl/) - Excel support.
- [QLNet](https://github.com/amaggiulli/qlnet) - .Net port.
- [PyQL](https://github.com/enthought/pyql) - Python port.
- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) - Julia port.
- [QuantLib-Python Documentation](https://quantlib-python-docs.readthedocs.io/) - Documentation for the Python bindings for the QuantLib library
- [TA-Lib](https://ta-lib.org) - perform technical analysis of financial market data.
- [ta-lib-python](https://github.com/TA-Lib/ta-lib-python)
- [ta-lib](https://github.com/TA-Lib/ta-lib)
- [Portfolio Optimizer](https://portfoliooptimizer.io/) - Portfolio Optimizer is a Web API for portfolio analysis and optimization.
- XAD: Automatic Differentation (AAD) Library for [Python](https://pypi.org/project/xad/) and [C++](https://github.com/auto-differentiation/xad)
## CSharp
- [QuantConnect](https://github.com/QuantConnect/Lean) - Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage.
- [StockSharp](https://github.com/StockSharp/StockSharp) - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
- [TDAmeritrade.DotNetCore](https://github.com/NVentimiglia/TDAmeritrade.DotNetCore) - Free, open-source .NET Client for the TD Ameritrade Trading Platform. Helps developers integrate TD Ameritrade API into custom trading solutions.
## Rust
- [QuantMath](https://github.com/MarcusRainbow/QuantMath) - Financial maths library for risk-neutral pricing and risk
- [Barter](https://github.com/barter-rs/barter-rs) - Open-source Rust framework for building event-driven live-trading & backtesting systems
- [LFEST](https://github.com/MathisWellmann/lfest-rs) - Simulated perpetual futures exchange to trade your strategy against.
- [TradeAggregation](https://github.com/MathisWellmann/trade_aggregation-rs) - Aggregate trades into user-defined candles using information driven rules.
- [SlidingFeatures](https://github.com/MathisWellmann/sliding_features-rs) - Chainable tree-like sliding windows for signal processing and technical analysis.
- [RustQuant](https://github.com/avhz/RustQuant) - Quantitative finance library written in Rust.
- [finalytics](https://github.com/Nnamdi-sys/finalytics) - A rust library for financial data analysis.
- [RunMat](https://github.com/runmat-org/runmat) - Rust runtime for MATLAB-syntax array math with automatic CPU/GPU execution and fused kernels for quant simulations.
## Reproducing Works, Training & Books
- [Auto-Differentiation Website](https://auto-differentiation.github.io/) - Background and resources on Automatic Differentiation (AD) / Adjoint Algorithmic Differentitation (AAD).
- [Derman Papers](https://github.com/MarcosCarreira/DermanPapers) - Notebooks that replicate original quantitative finance papers from Emanuel Derman.
- [KeepRule](https://keeprule.com/) - Curated library of decision-making principles and investment wisdom from masters like Buffett and Munger, featuring mental models for better investment thinking.
- [ML-Quant](https://www.ml-quant.com/) - Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.
- [volatility-trading](https://github.com/jasonstrimpel/volatility-trading) - A complete set of volatility estimators based on Euan Sinclair's Volatility Trading.
- [quant](https://github.com/paulperry/quant) - Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas.
- [fecon235](https://github.com/rsvp/fecon235) - Open source project for software tools in financial economics. Many jupyter notebook to verify theoretical ideas and practical methods interactively.
- [Quantitative-Notebooks](https://github.com/LongOnly/Quantitative-Notebooks) - Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
- [QuantEcon](https://quantecon.org/) - Lecture series on economics, finance, econometrics and data science; QuantEcon.py, QuantEcon.jl, notebooks
- [FinanceHub](https://github.com/Finance-Hub/FinanceHub) - Resources for Quantitative Finance
- [Python_Option_Pricing](https://github.com/dedwards25/Python_Option_Pricing) - An library to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options.
- [python-training](https://github.com/jpmorganchase/python-training) - J.P. Morgan's Python training for business analysts and traders.
- [Stock_Analysis_For_Quant](https://github.com/LastAncientOne/Stock_Analysis_For_Quant) - Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau.
- [algorithmic-trading-with-python](https://github.com/chrisconlan/algorithmic-trading-with-python) - Source code for Algorithmic Trading with Python (2020) by Chris Conlan.
- [MEDIUM_NoteBook](https://github.com/cerlymarco/MEDIUM_NoteBook) - Repository containing notebooks of [cerlymarco](https://github.com/cerlymarco)'s posts on Medium.
- [QuantFinance](https://github.com/PythonCharmers/QuantFinance) - Training materials in quantitative finance.
- [IPythonScripts](https://github.com/mgroncki/IPythonScripts) - Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.
- [Computational-Finance-Course](https://github.com/LechGrzelak/Computational-Finance-Course) - Materials for the course of Computational Finance.
- [Machine-Learning-for-Asset-Managers](https://github.com/emoen/Machine-Learning-for-Asset-Managers) - Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
- [Python-for-Finance-Cookbook](https://github.com/PacktPublishing/Python-for-Finance-Cookbook) - Python for Finance Cookbook, published by Packt.
- [modelos_vol_derivativos](https://github.com/ysaporito/modelos_vol_derivativos) - "Modelos de Volatilidade para Derivativos" book's Jupyter notebooks
- [NMOF](https://github.com/enricoschumann/NMOF) - Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).
- [py4fi2nd](https://github.com/yhilpisch/py4fi2nd) - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.
- [aiif](https://github.com/yhilpisch/aiif) - Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.
- [py4at](https://github.com/yhilpisch/py4at) - Jupyter Notebooks and code for the book Python for Algorithmic Trading (O'Reilly) by Yves Hilpisch.
- [dawp](https://github.com/yhilpisch/dawp) - Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.
- [dx](https://github.com/yhilpisch/dx) - DX Analytics | Financial and Derivatives Analytics with Python.
- [QuantFinanceBook](https://github.com/LechGrzelak/QuantFinanceBook) - Quantitative Finance book.
- [rough_bergomi](https://github.com/ryanmccrickerd/rough_bergomi) - A Python implementation of the rough Bergomi model.
- [frh-fx](https://github.com/ryanmccrickerd/frh-fx) - A python implementation of the fast-reversion Heston model of Mechkov for FX purposes.
- [Value Investing Studies](https://github.com/euclidjda/value-investing-studies) - A collection of data analysis studies that examine the performance and characteristics of value investing over long periods of time.
- [Machine Learning Asset Management](https://github.com/firmai/machine-learning-asset-management) - Machine Learning in Asset Management (by @firmai).
- [Deep Learning Machine Learning Stock](https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock) - Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.
- [Technical Analysis and Feature Engineering](https://github.com/jo-cho/Technical_Analysis_and_Feature_Engineering) - Feature Engineering and Feature Importance of Machine Learning in Financial Market.
- [Differential Machine Learning and Axes that matter by Brian Huge and Antoine Savine](https://github.com/differential-machine-learning/notebooks) - Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
- [systematictradingexamples](https://github.com/robcarver17/systematictradingexamples) - Examples of code related to book [Systematic Trading](www.systematictrading.org) and [blog](http://qoppac.blogspot.com)
- [pysystemtrade_examples](https://github.com/robcarver17/pysystemtrade_examples) - Examples using pysystemtrade for Robert Carver's [blog](http://qoppac.blogspot.com).
- [ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes) - Machine Learning in Finance: From Theory to Practice Book
- [Hands-On Machine Learning for Algorithmic Trading](https://github.com/packtpublishing/hands-on-machine-learning-for-algorithmic-trading) - Hands-On Machine Learning for Algorithmic Trading, published by Packt
- [financialnoob-misc](https://github.com/financialnoob/misc) - Codes from @financialnoob's posts
- [MesoSim Options Trading Strategy Library](https://github.com/deltaray-io/strategy-library) - Free and public Options Trading strategy library for MesoSim.
- [Quant-Finance-With-Python-Code](https://github.com/lingyixu/Quant-Finance-With-Python-Code) - Repo for code examples in Quantitative Finance with Python by Chris Kelliher
- [QuantFinanceTraining](https://github.com/JoaoJungblut/QuantFinanceTraining) - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative Finance). The codes are organized by class, facilitating navigation and reference.
- [Statistical-Learning-based-Portfolio-Optimization](https://github.com/YannickKae/Statistical-Learning-based-Portfolio-Optimization) - This R Shiny App utilizes the Hierarchical Equal Risk Contribution (HERC) approach, a modern portfolio optimization method developed by Raffinot (2018).
- [book_irds3](https://github.com/attack68/book_irds3) - Code repository for Pricing and Trading Interest Rate Derivatives.
- [Autoencoder-Asset-Pricing-Models](https://github.com/RichardS0268/Autoencoder-Asset-Pricing-Models) - Reimplementation of Autoencoder Asset Pricing Models ([GKX, 2019](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3335536)).
- [Finance](https://github.com/shashankvemuri/Finance) - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.
- [101_formulaic_alphas](https://github.com/ram-ki/101_formulaic_alphas) - Implementation of [101 formulaic alphas](https://arxiv.org/ftp/arxiv/papers/1601/1601.00991.pdf) using qstrader.
- [Tidy Finance](https://www.tidy-finance.org/) - An opinionated approach to empirical research in financial economics - a fully transparent, open-source code base in multiple programming languages (Python and R) to enable the reproducible implementation of financial research projects for students and practitioners.
- [RoughVolatilityWorkshop](https://github.com/jgatheral/RoughVolatilityWorkshop) - 2024 QuantMind's Rough Volatility Workshop lectures.
- [AFML](https://github.com/boyboi86/AFML) - All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.
- [AlgoTradingLib](https://github.com/usdaud/algotradinglib.github.io) - A catalog of algorithmic trading libraries, frameworks, strategies, and educational materials.
Add Prop Trader Compass to Risk Management tools
## Related Lists
- [awesome-sec-filings](https://github.com/vibeyclaw/awesome-sec-filings) - A curated list of tools, data sources, libraries, and resources for working with SEC filings (13F, 10-K, 10-Q, 8-K).
================================================
FILE: cran.csv
================================================
cran,github,repo
https://cran.r-project.org/web/packages/xts/index.html,https://github.com/joshuaulrich/xts,joshuaulrich/xts
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================================================
FILE: cranscrape.py
================================================
import requests
import re
import pandas as pd
reu = re.compile(r'https://github.com/([\w-]+/[\w-]+)')
red = re.compile(r'\d\d\d\d-\d\d-\d\d')
url = 'https://cran.r-project.org/web/packages/xts/index.html'
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'https://cran.r-project.org/web/packages/fAssets/index.html',
'https://cran.r-project.org/web/packages/fBasics/index.html',
'https://cran.r-project.org/web/packages/fBonds/index.html',
'https://cran.r-project.org/web/packages/fExoticOptions/index.html',
'https://cran.r-project.org/web/packages/fOptions/index.html',
'https://cran.r-project.org/web/packages/fPortfolio/index.html',
'https://cran.r-project.org/web/packages/portfolio/index.html',
'https://cran.r-project.org/web/packages/portfolioSim/index.html',
'https://cran.r-project.org/web/packages/sde/index.html',
'https://cran.r-project.org/web/packages/YieldCurve/index.html',
'https://cran.r-project.org/web/packages/SmithWilsonYieldCurve/index.html',
'https://cran.r-project.org/web/packages/ycinterextra/index.html',
'https://cran.r-project.org/web/packages/AmericanCallOpt/index.html',
'https://cran.r-project.org/web/packages/VarSwapPrice/index.html',
'https://cran.r-project.org/web/packages/RND/index.html',
'https://cran.r-project.org/web/packages/LSMonteCarlo/index.html',
'https://cran.r-project.org/web/packages/OptHedging/index.html',
'https://cran.r-project.org/web/packages/tvm/index.html',
'https://cran.r-project.org/web/packages/OptionPricing/index.html',
'https://cran.r-project.org/web/packages/credule/index.html',
'https://cran.r-project.org/web/packages/derivmkts/index.html',
'https://cran.r-project.org/web/packages/PortfolioAnalytics/PortfolioAnalytics.pdf',
'https://cran.r-project.org/web/packages/backtest/index.html',
'https://cran.r-project.org/web/packages/pa/index.html',
'https://cran.r-project.org/web/packages/TTR/index.html',
'https://cran.r-project.org/web/packages/PerformanceAnalytics/index.html',
'https://cran.r-project.org/web/packages/tseries/index.html',
'https://cran.r-project.org/web/packages/zoo/index.html',
'https://cran.r-project.org/web/packages/xts/index.html',
'https://cran.r-project.org/web/packages/fGarch/index.html',
'https://cran.r-project.org/web/packages/timeSeries/index.html',
'https://cran.r-project.org/web/packages/rugarch/index.html',
'https://cran.r-project.org/web/packages/rmgarch/index.html',
'https://cran.r-project.org/web/packages/timeDate/index.html',
'https://cran.r-project.org/web/packages/bizdays/index.html',
]
def get_data(url):
res = requests.get(url)
m = reu.search(res.text)
if m:
return dict(cran=url, github=m.group(0), repo=m.group(1))
else:
return dict(cran=url, github='', repo='')
all_data = [get_data(url) for url in urls]
df = pd.DataFrame(all_data)
df.to_csv('cran.csv', index=False)
================================================
FILE: legacy.txt
================================================
https://cran.r-project.org/web/views/Finance.html
http://www.bloomberglabs.com/api/libraries/
https://github.com/bpsmith/pybbg
https://pypi.python.org/pypi?%3Aaction=search&term=finance&submit=search
https://pypi.python.org/pypi?:action=browse&show=all&c=33
https://www.quantnet.com/threads/free-open-source-java-library-for-quantitative-finance.3238/
http://www.jquantlib.org/
http://www.asymptotix.eu/content/quantitative-libraries-financial-predictive-analytics
http://www.xweld.com
http://quantcomponents.com
https://www.quantnet.com/threads/open-source-financial-software.2605/
http://quant.stackexchange.com/questions/4181/library-of-basic-indicators
http://www.quantcode.com
http://www.cs.utah.edu/~cxiong/
================================================
FILE: parse.py
================================================
import os
import re
import pandas as pd
from threading import Thread
from github import Auth, Github
# using an access token
auth = Auth.Token(os.environ["GITHUB_ACCESS_TOKEN"])
g = Github(auth=auth)
def extract_repo(url):
reu = re.compile(r"^https://github.com/([\w-]+/[-\w\.]+)$")
m = reu.match(url)
if m:
return m.group(1)
else:
return ""
def get_last_commit(repo):
try:
if repo:
r = g.get_repo(repo)
cs = r.get_commits()
return cs[0].commit.author.date.strftime("%Y-%m-%d")
else:
return ""
except:
print("ERROR " + repo)
return "error"
class Project(Thread):
def __init__(self, match, section):
super().__init__()
self._match = match
self.regs = None
self._section = section
def run(self):
m = self._match
is_github = "github.com" in m.group(2)
is_cran = "cran.r-project.org" in m.group(2)
repo = extract_repo(m.group(2))
print(repo)
last_commit = get_last_commit(repo)
self.regs = dict(
project=m.group(1),
section=self._section,
last_commit=last_commit,
url=m.group(2),
description=m.group(3),
github=is_github,
cran=is_cran,
repo=repo,
)
projects = []
with open("README.md", "r", encoding="utf8") as f:
ret = re.compile(r"^(#+) (.*)$")
rex = re.compile(r"^\s*- \[(.*)\]\((.*)\) - (.*)$")
m_titles = []
last_head_level = 0
for line in f:
m = rex.match(line)
if m:
p = Project(m, " > ".join(m_titles[1:]))
p.start()
projects.append(p)
else:
m = ret.match(line)
if m:
hrs = m.group(1)
if len(hrs) > last_head_level:
m_titles.append(m.group(2))
else:
for n in range(last_head_level - len(hrs) + 1):
m_titles.pop()
m_titles.append(m.group(2))
last_head_level = len(hrs)
while True:
checks = [not p.is_alive() for p in projects]
if all(checks):
break
projects = [p.regs for p in projects]
df = pd.DataFrame(projects)
df.to_csv("site/projects.csv", index=False)
# df.to_markdown('projects.md', index=False)
================================================
FILE: pyproject.toml
================================================
[tool.poetry]
name = "awesome-quant"
version = "0.1.0"
description = ""
authors = ["Wilson Freitas <wilson.freitas@gmail.com>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.11"
PyGithub = "^2.2.0"
pandas = "^2.2.0"
mypy = "^1.14.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
================================================
FILE: quants.md
================================================
quants
https://sites.google.com/site/peterreinhardhansen/
http://www.dixiederivatives.com/excelpricingfiles.htm
================================================
FILE: recommendation.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Recommendation System for Apna_Vaidya\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1. Rule-Based System"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"user_profile = None\n",
"user_scenario = None"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"def rule_based_recommendation(user_profile, user_scenario):\n",
" if user_scenario == 'Patient with Symptoms':\n",
" return [\"Talk to a Doctor\", \"Verify with a Doctor\", \"Symptom Checker (AI-based)\"]\n",
" elif user_scenario == 'Chronic Condition Management':\n",
" return [\"Get a Prescription\", \"Order Medication\", \"Talk to a Doctor\", \"Talk to a Pharmacist\", \"Health Coach Consultation\"]\n",
" elif user_scenario == 'New Medication Inquiry':\n",
" return [\"Talk to a Doctor\", \"Talk to a Pharmacist\", \"Medication Information (AI-based)\"]\n",
" elif user_scenario == 'Diagnostic Needs':\n",
" return [\"Connect with a Diagnostic Center\", \"Verify with a Doctor\", \"Home Sample Collection\"]\n",
" elif user_scenario == 'Prescription Refill':\n",
" return [\"Order Medication\", \"Get a Prescription\", \"Talk to a Pharmacist\", \"Auto-Refill Subscription\"]\n",
" elif user_scenario == 'Preventive Care':\n",
" return [\"Schedule a Check-up\", \"Health Coach Consultation\", \"Vaccination Booking\"]\n",
" elif user_scenario == 'Post-Treatment Follow-up':\n",
" return [\"Talk to a Doctor\", \"Schedule a Follow-up\", \"Physical Therapy Consultation\"]\n",
" else:\n",
" return [\"Talk to a Doctor\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Talk to a Doctor', 'Verify with a Doctor', 'Symptom Checker (AI-based)']\n"
]
}
],
"source": [
"# Example usage\n",
"user_profile = 'Chronic Condition Management'\n",
"user_scenario = 'Patient with Symptoms'\n",
"recommendation = rule_based_recommendation(user_profile, user_scenario)\n",
"print(recommendation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2. Collaborative Filtering"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.metrics.pairwise import cosine_similarity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example user-item interaction matrix (rows: users, columns: options)\n",
"interaction_matrix = np.array([\n",
" [1, 1, 0, 0, 0, 0, 0, 0, 0], # User 1\n",
" [0, 1, 1, 0, 0, 0, 0, 0, 0], # User 2\n",
" [0, 0, 1, 1, 0, 0, 0, 0, 0], # User 3\n",
" [0, 0, 0, 1, 1, 1, 0, 0, 0], # User 4\n",
" [0, 0, 0, 0, 1, 1, 1, 0, 0], # User 5\n",
" [0, 0, 0, 0, 0, 1, 1, 1, 0], # User 6\n",
"])\n",
"\n",
"# Calculate similarity between users\n",
"user_similarity = cosine_similarity(interaction_matrix)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Recommend based on the most similar user's interactions\n",
"def collaborative_filtering_recommendation(user_index, interaction_matrix, user_similarity):\n",
" similar_user_index = np.argmax(user_similarity[user_index])\n",
" recommendations = np.where(interaction_matrix[similar_user_index] == 1)[0]\n",
" return recommendations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example usage\n",
"user_index = 0 # User 1\n",
"recommendations = collaborative_filtering_recommendation(user_index, interaction_matrix, user_similarity)\n",
"print(recommendations) # Output the indices of the recommended options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3. Content-Based Filtering"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"# Example option descriptions\n",
"options = [\n",
" \"Talk to a Doctor for immediate consultation\",\n",
" \"Verify with a Doctor for second opinion\",\n",
" \"Use Symptom Checker to self-diagnose\",\n",
" \"Get a Prescription for chronic condition\",\n",
" \"Order Medication online\",\n",
" \"Talk to a Pharmacist for medication advice\",\n",
" \"Health Coach Consultation for chronic condition management\",\n",
" \"Medication Information for new prescriptions\",\n",
" \"Medication Interaction Checker\",\n",
" \"Connect with a Diagnostic Center for tests\",\n",
" \"Home Sample Collection for diagnostics\",\n",
" \"Book Lab Tests Online\",\n",
" \"Schedule a Check-up for preventive care\",\n",
" \"Health Screening Packages for preventive care\",\n",
" \"Vaccination Booking for preventive care\",\n",
" \"Schedule a Follow-up for post-treatment\",\n",
" \"Physical Therapy Consultation for recovery\",\n",
" \"Remote Monitoring Services for follow-up\",\n",
" \"Talk to a Therapist for mental health support\",\n",
" \"Join a Support Group for mental health\",\n",
" \"Mental Health Self-assessment (AI-based)\",\n",
" \"Meditation and Mindfulness Resources\"\n",
"]\n",
"\n",
"# User profile description\n",
"user_profile_description = \"Patient with chronic condition needing regular medication and follow-ups\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Vectorize the descriptions\n",
"vectorizer = TfidfVectorizer()\n",
"option_vectors = vectorizer.fit_transform(options)\n",
"user_vector = vectorizer.transform([user_profile_description])\n",
"\n",
"similarity = cosine_similarity(user_vector, option_vectors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Recommend based on the highest similarity scores\n",
"def content_based_recommendation(similarity, options):\n",
" recommendations = np.argsort(similarity[0])[::-1]\n",
" return [options[i] for i in recommendations[:5]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example usage\n",
"recommendations = content_based_recommendation(similarity, options)\n",
"print(recommendations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 4. Machine Learning Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# using a decision tree classifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example dataset (features: user profile data, labels: recommended options)\n",
"# This is a simplified example; in practice, you would have a more complex dataset\n",
"X = np.array([\n",
" [25, 1, 0, 1], # User 1: age, chronic condition, acute symptoms, preventive care\n",
" [45, 1, 1, 0], # User 2\n",
" [60, 0, 0, 1], # User 3\n",
" [35, 1, 0, 1], # User 4\n",
" [50, 0, 1, 0], # User 5\n",
"])\n",
"y = np.array([\n",
" [0, 1, 1, 0], # Recommended options for User 1\n",
" [1, 1, 0, 1], # Recommended options for User 2\n",
" [0, 0, 1, 1], # Recommended options for User 3\n",
" [1, 1, 0, 0], # Recommended options for User 4\n",
" [0, 0, 1, 1], # Recommended options for User 5\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Train the decision tree classifier\n",
"clf = DecisionTreeClassifier()\n",
"clf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Predict recommendations for a new user\n",
"new_user = np.array([[40, 1, 1, 0]]) # New user profile\n",
"predictions = clf.predict(new_user)\n",
"print(predictions)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: site/.gitignore
================================================
/.quarto/
docs
================================================
FILE: site/CODE_OF_CONDUCT.qmd
================================================
---
title: "Contributor Covenant Code of Conduct"
include-in-header:
- text: |
<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-7994446359957143"
crossorigin="anonymous"></script>
---
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
awesom3quant@gmail.com.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
================================================
FILE: site/_quarto.yml
================================================
project:
type: website
output-dir: docs
render:
- "*.qmd"
- "*.ipynb"
- "!quant.md"
- "!github.ipynb"
website:
title: "Awesome Quant"
navbar:
left:
- href: index.qmd
text: Home
- href: projects.qmd
text: Projects
- href: CODE_OF_CONDUCT.qmd
text: Code of Conduct
format:
html:
theme: cosmo
css: styles.css
toc: true
================================================
FILE: site/about.qmd
================================================
---
title: "About"
include-in-header:
- text: |
<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-7994446359957143"
crossorigin="anonymous"></script>
---
About this site
================================================
FILE: site/index.qmd
================================================
---
title: "Awesome Quant"
date-modified: last-modified
keywords: ["r packages", "python packages", "julia packages",
"software development", "software engineering", "financial computing",
"r", "python", "julia", "rust", "java"]
include-in-header:
- text: |
<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-7994446359957143"
crossorigin="anonymous"></script>
---
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
[](https://awesome.re)
## Python
### Numerical Libraries & Data Structures
- [numpy](https://www.numpy.org) - NumPy is the fundamental package for scientific computing with Python.
- [scipy](https://www.scipy.org) - SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
- [pandas](https://pandas.pydata.org) - 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.
- [polars](https://docs.pola.rs/) - Polars is a blazingly fast DataFrame library for manipulating structured data.
- [quantdsl](https://github.com/johnbywater/quantdsl) - Domain specific language for quantitative analytics in finance and trading.
- [statistics](https://docs.python.org/3/library/statistics.html) - Builtin Python library for all basic statistical calculations.
- [sympy](https://www.sympy.org/) - SymPy is a Python library for symbolic mathematics.
- [pymc3](https://docs.pymc.io/) - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano.
- [modelx](https://docs.modelx.io/) - Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas.
- [ArcticDB](https://github.com/man-group/ArcticDB) - High performance datastore for time series and tick data.
- [pmxt](https://github.com/pmxt-dev/pmxt) - The CCXT for prediction markets. A unified API for trading on Polymarket, Kalshi, and more.
### Financial Instruments and Pricing
- [OpenBB Terminal](https://github.com/OpenBB-finance/OpenBBTerminal) - Terminal for investment research for everyone.
- [Fincept Terminal](https://github.com/Fincept-Corporation/FinceptTerminal) - Advance Data Based A.I Terminal for all Types of Financial Asset Research.
- [PyQL](https://github.com/enthought/pyql) - QuantLib's Python port.
- [pyfin](https://github.com/opendoor-labs/pyfin) - Basic options pricing in Python. *ARCHIVED*
- [vollib](https://github.com/vollib/vollib) - vollib is a python library for calculating option prices, implied volatility and greeks.
- [QuantPy](https://github.com/jsmidt/QuantPy) - A framework for quantitative finance In python.
- [Finance-Python](https://github.com/alpha-miner/Finance-Python) - Python tools for Finance.
- [ffn](https://github.com/pmorissette/ffn) - A financial function library for Python.
- [pynance](https://github.com/GriffinAustin/pynance) - Lightweight Python library for assembling and analyzing financial data.
- [tia](https://github.com/bpsmith/tia) - Toolkit for integration and analysis.
- [hasura/base-python-dash](https://platform.hasura.io/hub/projects/hasura/base-python-dash) - Hasura quick start to deploy Dash framework. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python.
- [hasura/base-python-bokeh](https://platform.hasura.io/hub/projects/hasura/base-python-bokeh) - Hasura quick start to visualize data with bokeh library.
- [pysabr](https://github.com/ynouri/pysabr) - SABR model Python implementation.
- [FinancePy](https://github.com/domokane/FinancePy) - 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](https://github.com/goldmansachs/gs-quant) - Python toolkit for quantitative finance
- [willowtree](https://github.com/federicomariamassari/willowtree) - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.
- [financial-engineering](https://github.com/federicomariamassari/financial-engineering) - Applications of Monte Carlo methods to financial engineering projects, in Python.
- [optlib](https://github.com/dbrojas/optlib) - A library for financial options pricing written in Python.
- [tf-quant-finance](https://github.com/google/tf-quant-finance) - High-performance TensorFlow library for quantitative finance.
- [Q-Fin](https://github.com/RomanMichaelPaolucci/Q-Fin) - A Python library for mathematical finance.
- [Quantsbin](https://github.com/quantsbin/Quantsbin) - Tools for pricing and plotting of vanilla option prices, greeks and various other analysis around them.
- [finoptions](https://github.com/bbcho/finoptions-dev) - Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.
- [pypme](https://github.com/ymyke/pypme) - PME (Public Market Equivalent) calculation.
- [AbsBox](https://github.com/yellowbean/AbsBox) - A Python based library to model cashflow for structured product like Asset-backed securities (ABS) and Mortgage-backed securities (MBS).
- [Intrinsic-Value-Calculator](https://github.com/akashaero/Intrinsic-Value-Calculator) - A Python tool for quick calculations of a stock's fair value using Discounted Cash Flow analysis.
- [Kelly-Criterion](https://github.com/deltaray-io/kelly-criterion) - Kelly Criterion implemented in Python to size portfolios based on J. L. Kelly Jr's formula.
- [rateslib](https://github.com/attack68/rateslib) - A fixed income library for pricing bonds and bond futures, and derivatives such as IRS, cross-currency and FX swaps.
- [fypy](https://github.com/jkirkby3/fypy) - 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.
- [quantra](https://github.com/joseprupi/quantraserver) High-performance pricing engine built on QuantLib. It exposes QuantLib's functionality through gRPC and REST APIs, enabling distributed computations with FlatBuffers serialization.
- [optionlab](https://github.com/rgaveiga/optionlab) - A Python library for evaluating option trading strategies.
### Indicators
- [pandas_talib](https://github.com/femtotrader/pandas_talib) - A Python Pandas implementation of technical analysis indicators.
- [finta](https://github.com/peerchemist/finta) - Common financial technical analysis indicators implemented in Pandas.
- [Tulipy](https://github.com/cirla/tulipy) - Financial Technical Analysis Indicator Library (Python bindings for [tulipindicators](https://github.com/TulipCharts/tulipindicators))
- [lppls](https://github.com/Boulder-Investment-Technologies/lppls) - A Python module for fitting the [Log-Periodic Power Law Singularity (LPPLS)](https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.
- [talipp](https://github.com/nardew/talipp) - Incremental technical analysis library for Python.
- [streaming_indicators](https://github.com/mr-easy/streaming_indicators) - A python library for computing technical analysis indicators on streaming data.
### Trading & Backtesting
- [the0](https://github.com/alexanderwanyoike/the0) - Self-hosted execution engine for algorithmic trading bots. Write strategies in Python, TypeScript, Rust, C++, C#, Scala, or Haskell and deploy with one command. Each bot runs in an isolated container with scheduled or streaming execution.
- [skfolio](https://github.com/skfolio/skfolio) - Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models.
- [Investing algorithm framework](https://github.com/coding-kitties/investing-algorithm-framework) - Framework for developing, backtesting, and deploying automated trading algorithms.
- [QSTrader](https://github.com/mhallsmoore/qstrader) - QSTrader backtesting simulation engine.
- [Blankly](https://github.com/Blankly-Finance/Blankly) - Fully integrated backtesting, paper trading, and live deployment.
- [TA-Lib](https://github.com/mrjbq7/ta-lib) - Python wrapper for TA-Lib (<http://ta-lib.org/>).
- [zipline](https://github.com/quantopian/zipline) - Pythonic algorithmic trading library.
- [zipline-reloaded](https://github.com/stefan-jansen/zipline-reloaded) - Zipline, a Pythonic Algorithmic Trading Library.
- [QuantSoftware Toolkit](https://github.com/QuantSoftware/QuantSoftwareToolkit) - Python-based open source software framework designed to support portfolio construction and management.
- [quantitative](https://github.com/jeffrey-liang/quantitative) - Quantitative finance, and backtesting library.
- [analyzer](https://github.com/llazzaro/analyzer) - Python framework for real-time financial and backtesting trading strategies.
- [bt](https://github.com/pmorissette/bt) - Flexible Backtesting for Python.
- [backtrader](https://github.com/backtrader/backtrader) - Python Backtesting library for trading strategies.
- [pythalesians](https://github.com/thalesians/pythalesians) - Python library to backtest trading strategies, plot charts, seamlessly download market data, analyze market patterns etc.
- [pybacktest](https://github.com/ematvey/pybacktest) - Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier.
- [pyalgotrade](https://github.com/gbeced/pyalgotrade) - Python Algorithmic Trading Library.
- [basana](https://github.com/gbeced/basana) - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
- [tradingWithPython](https://pypi.org/project/tradingWithPython/) - A collection of functions and classes for Quantitative trading.
- [ta](https://github.com/bukosabino/ta) - Technical Analysis Library using Pandas (Python)
- [algobroker](https://github.com/joequant/algobroker) - This is an execution engine for algo trading.
- [pysentosa](https://pypi.org/project/pysentosa/) - Python API for sentosa trading system.
- [finmarketpy](https://github.com/cuemacro/finmarketpy) - Python library for backtesting trading strategies and analyzing financial markets.
- [binary-martingale](https://github.com/metaperl/binary-martingale) - Computer program to automatically trade binary options martingale style.
- [fooltrader](https://github.com/foolcage/fooltrader) - the project using big-data technology to provide an uniform way to analyze the whole market.
- [zvt](https://github.com/zvtvz/zvt) - the project using sql, pandas to provide an uniform and extendable way to record data, computing factors, select securities, backtesting, realtime trading and it could show all of them in clearly charts in realtime.
- [pylivetrader](https://github.com/alpacahq/pylivetrader) - zipline-compatible live trading library.
- [pipeline-live](https://github.com/alpacahq/pipeline-live) - zipline's pipeline capability with IEX for live trading.
- [zipline-extensions](https://github.com/quantrocket-llc/zipline-extensions) - Zipline extensions and adapters for QuantRocket.
- [moonshot](https://github.com/quantrocket-llc/moonshot) - Vectorized backtester and trading engine for QuantRocket based on Pandas.
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt) - Financial portfolio optimization in python, including classical efficient frontier and advanced methods.
- [Eiten](https://github.com/tradytics/eiten) - Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios.
- [riskparity.py](https://github.com/dppalomar/riskparity.py) - fast and scalable design of risk parity portfolios with TensorFlow 2.0
- [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) - Implementations regarding "Advances in Financial Machine Learning" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling)
- [pyqstrat](https://github.com/abbass2/pyqstrat) - A fast, extensible, transparent python library for backtesting quantitative strategies.
- [NowTrade](https://github.com/edouardpoitras/NowTrade) - Python library for backtesting technical/mechanical strategies in the stock and currency markets.
- [pinkfish](https://github.com/fja05680/pinkfish) - A backtester and spreadsheet library for security analysis.
- [PRISM-INSIGHT](https://github.com/dragon1086/prism-insight) - AI-powered stock analysis system with 13 specialized agents, automated trading via KIS API, supporting Korean & US markets.
- [aat](https://github.com/timkpaine/aat) - Async Algorithmic Trading Engine
- [Backtesting.py](https://kernc.github.io/backtesting.py/) - Backtest trading strategies in Python
- [catalyst](https://github.com/enigmampc/catalyst) - An Algorithmic Trading Library for Crypto-Assets in Python
- [quantstats](https://github.com/ranaroussi/quantstats) - Portfolio analytics for quants, written in Python
- [qtpylib](https://github.com/ranaroussi/qtpylib) - QTPyLib, Pythonic Algorithmic Trading <http://qtpylib.io>
- [Quantdom](https://github.com/constverum/Quantdom) - Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]
- [freqtrade](https://github.com/freqtrade/freqtrade) - Free, open source crypto trading bot
- [algorithmic-trading-with-python](https://github.com/chrisconlan/algorithmic-trading-with-python) - Free `pandas` and `scikit-learn` resources for trading simulation, backtesting, and machine learning on financial data.
- [DeepDow](https://github.com/jankrepl/deepdow) - Portfolio optimization with deep learning
- [Qlib](https://github.com/microsoft/qlib) - An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.
- [machine-learning-for-trading](https://github.com/stefan-jansen/machine-learning-for-trading) - Code and resources for Machine Learning for Algorithmic Trading
- [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) - Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost
- [jesse](https://github.com/jesse-ai/jesse) - An advanced crypto trading bot written in Python
- [rqalpha](https://github.com/ricequant/rqalpha) - A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities.
- [FinRL-Library](https://github.com/AI4Finance-LLC/FinRL-Library) - A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020.
- [bulbea](https://github.com/achillesrasquinha/bulbea) - Deep Learning based Python Library for Stock Market Prediction and Modelling.
- [ib_nope](https://github.com/ajhpark/ib_nope) - Automated trading system for NOPE strategy over IBKR TWS.
- [OctoBot](https://github.com/Drakkar-Software/OctoBot) - Open source cryptocurrency trading bot for high frequency, arbitrage, TA and social trading with an advanced web interface.
- [bta-lib](https://github.com/mementum/bta-lib) - Technical Analysis library in pandas for backtesting algotrading and quantitative analysis.
- [Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models) - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations.
- [TuneTA](https://github.com/jmrichardson/tuneta) - TuneTA optimizes technical indicators using a distance correlation measure to a user defined target feature such as next day return.
- [AutoTrader](https://github.com/kieran-mackle/AutoTrader) - A Python-based development platform for automated trading systems - from backtesting to optimization to livetrading.
- [fast-trade](https://github.com/jrmeier/fast-trade) - A library built with backtest portability and performance in mind for backtest trading strategies.
- [qf-lib](https://github.com/quarkfin/qf-lib) - QF-Lib is a Python library that provides high quality tools for quantitative finance.
- [tda-api](https://github.com/alexgolec/tda-api) - Gather data and trade equities, options, and ETFs via TDAmeritrade.
- [vectorbt](https://github.com/polakowo/vectorbt) - Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.
- [Lean](https://github.com/QuantConnect/Lean) - Lean Algorithmic Trading Engine by QuantConnect (Python, C#).
- [fast-trade](https://github.com/jrmeier/fast-trade) - Low code backtesting library utilizing pandas and technical analysis indicators.
- [pysystemtrade](https://github.com/robcarver17/pysystemtrade) - pysystemtrade is the open source version of Robert Carver's backtesting and trading engine that implements systems according to the framework outlined in his book "Systematic Trading", which is further developed on his [blog](https://qoppac.blogspot.com/).
- [pytrendseries](https://github.com/rafa-rod/pytrendseries) - Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.
- [PyLOB](https://github.com/DrAshBooth/PyLOB) - Fully functioning fast Limit Order Book written in Python.
- [PyBroker](https://github.com/edtechre/pybroker) - Algorithmic Trading with Machine Learning.
- [OctoBot Script](https://github.com/Drakkar-Software/OctoBot-Script) - A quant framework to create cryptocurrencies strategies - from backtesting to optimization to livetrading.
- [hftbacktest](https://github.com/nkaz001/hftbacktest) - A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
- [vnpy](https://github.com/vnpy/vnpy) - VeighNa is a Python-based open source quantitative trading system development framework.
- [Intelligent Trading Bot](https://github.com/asavinov/intelligent-trading-bot) - Automatically generating signals and trading based on machine learning and feature engineering
- [fastquant](https://github.com/enzoampil/fastquant) - fastquant allows you to easily backtest investment strategies with as few as 3 lines of python code.
- [nautilus_trader](https://github.com/nautechsystems/nautilus_trader) - A high-performance algorithmic trading platform and event-driven backtester.
- [YABTE](https://github.com/bsdz/yabte) - Yet Another (Python) BackTesting Engine.
- [Trading Strategy](https://github.com/tradingstrategy-ai/getting-started) - TradingStrategy.ai is a market data, backtesting, live trading and investor management framework for decentralised finance
- [Hikyuu](https://github.com/fasiondog/hikyuu) - A base on Python/C++ open source high-performance quant framework for faster analysis and backtesting, contains the complete trading system components for reuse and combination.
- [rust_bt](https://github.com/jensnesten/rust_bt) - A high performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust.
- [Gunbot Quant](https://github.com/GuntharDeNiro/gunbot-quant) - Toolkit for quantitative trading analysis. It integrates an advanced market screener, a multi-strategy, multi-asset backtesting engine. Use with built-in GUI or through CLI.
- [StrateQueue](https://github.com/StrateQueue/StrateQueue) - An open‑source, broker‑agnostic Python library that lets you seamlessly deploy strategies from any major backtesting engine to live (or paper) trading with zero code changes and built‑in safety controls.
- [Chartscout](https://chartscout.io) - Real-time cryptocurrency chart pattern detection with automated alerts across multiple exchanges
- [DayTradingBench](https://daytradingbench.com) - Live autonomous benchmark that evaluates LLM trading performance on DAX and Nasdaq indices using identical strategies and real-time market data. API access available.
- [CoinTester](https://cointester.io) - No-code crypto backtesting platform with 100+ indicators, AI sentiment signals, and 5+ years of historical data across 1,000+ trading pairs.
- [PythonTradingFramework](https://github.com/JustinGuese/python_tradingbot_framework)  - Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.
- [QTradeX-AI-Agents](https://github.com/squidKid-deluxe/QTradeX-AI-Agents) - Example strategies for the QTradeX platfrom.
- [QTradeX-Algo-Trading-SDK](https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK) - AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
- [antback](https://github.com/ts-kontakt/antback) - A lightweight, event-loop-style backtest engine that allows a function-driven imperative style using efficient stateful helper functions and data containers.
### Risk Analysis
- [QuantLibRisks](https://github.com/auto-differentiation/QuantLib-Risks-Py) - Fast risks with QuantLib
- [XAD](https://github.com/auto-differentiation/xad-py) - Automatic Differentation (AAD) Library
- [pyfolio](https://github.com/quantopian/pyfolio) - Portfolio and risk analytics in Python.
- [empyrical](https://github.com/quantopian/empyrical) - Common financial risk and performance metrics.
- [fecon235](https://github.com/rsvp/fecon235) - Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios.
- [finance](https://pypi.org/project/finance/) - Financial Risk Calculations. Optimized for ease of use through class construction and operator overload.
- [qfrm](https://pypi.org/project/qfrm/) - Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios.
- [visualize-wealth](https://github.com/benjaminmgross/visualize-wealth) - Portfolio construction and quantitative analysis.
- [VisualPortfolio](https://github.com/wegamekinglc/VisualPortfolio) - This tool is used to visualize the performance of a portfolio.
- [universal-portfolios](https://github.com/Marigold/universal-portfolios) - Collection of algorithms for online portfolio selection.
- [FinQuant](https://github.com/fmilthaler/FinQuant) - A program for financial portfolio management, analysis and optimization.
- [Empyrial](https://github.com/ssantoshp/Empyrial) - Portfolio's risk and performance analytics and returns predictions.
- [risktools](https://github.com/bbcho/risktools-dev) - Risk tools for use within the crude and crude products trading space with partial implementation of R's PerformanceAnalytics.
- [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib) - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python.
- [empyrical-reloaded](https://github.com/stefan-jansen/empyrical-reloaded) - Common financial risk and performance metrics. [empyrical](https://github.com/quantopian/empyrical) fork.
- [pyfolio-reloaded](https://github.com/stefan-jansen/pyfolio-reloaded) - Portfolio and risk analytics in Python. [pyfolio](https://github.com/quantopian/pyfolio) fork.
- [fortitudo.tech](https://github.com/fortitudo-tech/fortitudo.tech) - Conditional Value-at-Risk (CVaR) portfolio optimization and Entropy Pooling views / stress-testing in Python.
- [Quant Lab Alpha](https://github.com/husainm97/quant-lab-alpha) — Portfolio risk decomposition and Monte Carlo simulation toolkit with factor-based modeling.
- [quantitative-finance-tools](https://github.com/omichauhan-lgtm/quantitative-finance-tools) - Library for portfolio optimization (MVO) and rigorous risk metrics (VaR/CVaR).
- [curistat](https://github.com/moxiespirit/MyClone/tree/main/volatility_platform) - Futures volatility forecasting platform for ES/NQ. Proprietary CVN rating (1-10), regime detection (CRC composite), 8 directional signals, economic event impact analytics. Includes MCP server for AI agent integration.
- [Prop Trader Compass](https://otto-ships.github.io/prop-trader-compass/) - Interactive risk and payout calculator for Futures and CFD traders; features one-time fee firm comparisons.
### Factor Analysis
- [alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive alpha factors.
- [alphalens-reloaded](https://github.com/stefan-jansen/alphalens-reloaded) - Performance analysis of predictive (alpha) stock factors.
- [Spectre](https://github.com/Heerozh/spectre) - GPU-accelerated Factors analysis library and Backtester
- [quant-lab-alpha](https://github.com/husainm97/quant-lab-alpha) - Open-source investment analytics platform bridging academic research and retail finance.
### Sentiment Analysis
- [Asset News Sentiment Analyzer](https://github.com/KVignesh122/AssetNewsSentimentAnalyzer) - Sentiment analysis and report generation package for financial assets and securities utilizing GPT models.
- [Social Stock Sentiment API](https://api.adanos.org/docs) - REST API analyzing Reddit and X/Twitter for stock mentions and sentiment, providing buzz scores, trending stocks, and AI-generated trend explanations.
### Quant Research Environment
- [Jupyter Quant](https://github.com/gnzsnz/jupyter-quant) - A dockerized Jupyter quant research environment with preloaded tools for quant analysis, statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, etc.
### Time Series
- [ARCH](https://github.com/bashtage/arch) - ARCH models in Python.
- [statsmodels](http://statsmodels.sourceforge.net) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
- [dynts](https://github.com/quantmind/dynts) - Python package for timeseries analysis and manipulation.
- [PyFlux](https://github.com/RJT1990/pyflux) - Python library for timeseries modelling and inference (frequentist and Bayesian) on models.
- [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.
- [hasura/quandl-metabase](https://platform.hasura.io/hub/projects/anirudhm/quandl-metabase-time-series) - Hasura quickstart to visualize Quandl's timeseries datasets with Metabase.
- [Facebook Prophet](https://github.com/facebook/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- [tsmoothie](https://github.com/cerlymarco/tsmoothie) - A python library for time-series smoothing and outlier detection in a vectorized way.
- [pmdarima](https://github.com/alkaline-ml/pmdarima) - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
- [gluon-ts](https://github.com/awslabs/gluon-ts) - vProbabilistic time series modeling in Python.
- [functime](https://github.com/functime-org/functime) - Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.
### Calendars
- [exchange_calendars](https://github.com/gerrymanoim/exchange_calendars) - Stock Exchange Trading Calendars.
- [bizdays](https://github.com/wilsonfreitas/python-bizdays) - Business days calculations and utilities.
- [pandas_market_calendars](https://github.com/rsheftel/pandas_market_calendars) - Exchange calendars to use with pandas for trading applications.
### Data Sources
- [goMacro.ai](https://gomacro.ai) - AI-powered economic calendar with institutional-grade insights, bull/bear/base case scenario planning for NFP, CPI, PPI and other macro data releases.
- [StockAPI](https://stockapi.com.cn) – Free real-time Chinese stock data (REST & WebSocket).
- [yfinance](https://github.com/ranaroussi/yfinance) - Yahoo! Finance market data downloader (+faster Pandas Datareader)
- [defeatbeta-api](https://github.com/defeat-beta/defeatbeta-api) - An open-source alternative to Yahoo Finance's market data APIs with higher reliability.
- [findatapy](https://github.com/cuemacro/findatapy) - Python library to download market data via Bloomberg, Quandl, Yahoo etc.
- [googlefinance](https://github.com/hongtaocai/googlefinance) - Python module to get real-time stock data from Google Finance API.
- [yahoo-finance](https://github.com/lukaszbanasiak/yahoo-finance) - Python module to get stock data from Yahoo! Finance.
- [pandas-datareader](https://github.com/pydata/pandas-datareader) - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
- [pandas-finance](https://github.com/davidastephens/pandas-finance) - High level API for access to and analysis of financial data.
- [pyhoofinance](https://github.com/innes213/pyhoofinance) - Rapidly queries Yahoo Finance for multiple tickers and returns typed data for analysis.
- [yfinanceapi](https://github.com/Karthik005/yfinanceapi) - Finance API for Python.
- [yql-finance](https://github.com/slawek87/yql-finance) - yql-finance is simple and fast. API returns stock closing prices for current period of time and current stock ticker (i.e. APPL, GOOGL).
- [ystockquote](https://github.com/cgoldberg/ystockquote) - Retrieve stock quote data from Yahoo Finance.
- [wallstreet](https://github.com/mcdallas/wallstreet) - Real time stock and option data.
- [stock_extractor](https://github.com/ZachLiuGIS/stock_extractor) - General Purpose Stock Extractors from Online Resources.
- [Stockex](https://github.com/cttn/Stockex) - Python wrapper for Yahoo! Finance API.
- [StockAInsights](https://stockainsights.com) - AI-extracted financial statements API covering SEC filings including foreign filers (20-F, 6-K, 40-F), normalized quarterly and annual data from 2014+.
- [finsymbols](https://github.com/skillachie/finsymbols) - Obtains stock symbols and relating information for SP500, AMEX, NYSE, and NASDAQ.
- [FRB](https://github.com/avelkoski/FRB) - Python Client for FRED® API.
- [inquisitor](https://github.com/econdb/inquisitor) - Python Interface to Econdb.com API.
- [yfi](https://github.com/nickelkr/yfi) - Yahoo! YQL library.
- [chinesestockapi](https://pypi.org/project/chinesestockapi/) - Python API to get Chinese stock price.
- [exchange](https://github.com/akarat/exchange) - Get current exchange rate.
- [ticks](https://github.com/jamescnowell/ticks) - Simple command line tool to get stock ticker data.
- [pybbg](https://github.com/bpsmith/pybbg) - Python interface to Bloomberg COM APIs.
- [ccy](https://github.com/lsbardel/ccy) - Python module for currencies.
- [tushare](https://pypi.org/project/tushare/) - A utility for crawling historical and Real-time Quotes data of China stocks.
- [jsm](https://pypi.org/project/jsm/) - Get the japanese stock market data.
- [edinet-mcp](https://github.com/ajtgjmdjp/edinet-mcp) - Parse Japanese XBRL financial statements from EDINET with 161 normalized labels, 26 financial metrics, and multi-company screening.
- [estat-mcp](https://github.com/ajtgjmdjp/estat-mcp) - Access Japanese government statistics (e-Stat) covering population, GDP, CPI, labor, and trade data with MCP integration and Polars export.
- [tdnet-disclosure-mcp](https://github.com/ajtgjmdjp/tdnet-disclosure-mcp) - Access Japanese timely disclosures (TDNet) via MCP. Retrieve earnings, dividends, forecasts, buybacks, and other filings for 4,000+ listed companies. No API key required.
- [cn_stock_src](https://github.com/jealous/cn_stock_src) - Utility for retrieving basic China stock data from different sources.
- [coinmarketcap](https://github.com/barnumbirr/coinmarketcap) - Python API for coinmarketcap.
- [coinpulse](https://github.com/soutone/coinpulse-python) - Python SDK for cryptocurrency portfolio tracking with real-time prices, P/L calculations, and price alerts. Free tier available.
- [after-hours](https://github.com/datawrestler/after-hours) - Obtain pre market and after hours stock prices for a given symbol.
- [bronto-python](https://pypi.org/project/bronto-python/) - Bronto API Integration for Python.
- [pytdx](https://github.com/rainx/pytdx) - Python Interface for retrieving chinese stock realtime quote data from TongDaXin Nodes.
- [pdblp](https://github.com/matthewgilbert/pdblp) - A simple interface to integrate pandas and the Bloomberg Open API.
- [tiingo](https://github.com/hydrosquall/tiingo-python) - Python interface for daily composite prices/OHLC/Volume + Real-time News Feeds, powered by the Tiingo Data Platform.
- [iexfinance](https://github.com/addisonlynch/iexfinance) - Python Interface for retrieving real-time and historical prices and equities data from The Investor's Exchange.
- [pyEX](https://github.com/timkpaine/pyEX) - Python interface to IEX with emphasis on pandas, support for streaming data, premium data, points data (economic, rates, commodities), and technical indicators.
- [alpaca-trade-api](https://github.com/alpacahq/alpaca-trade-api-python) - Python interface for retrieving real-time and historical prices from Alpaca API as well as trade execution.
- [metatrader5](https://pypi.org/project/MetaTrader5/) - API Connector to MetaTrader 5 Terminal
- [akshare](https://github.com/jindaxiang/akshare) - AkShare is an elegant and simple financial data interface library for Python, built for human beings! <https://akshare.readthedocs.io>
- [yahooquery](https://github.com/dpguthrie/yahooquery) - Python interface for retrieving data through unofficial Yahoo Finance API.
- [investpy](https://github.com/alvarobartt/investpy) - Financial Data Extraction from Investing.com with Python! <https://investpy.readthedocs.io/>
- [yliveticker](https://github.com/yahoofinancelive/yliveticker) - Live stream of market data from Yahoo Finance websocket.
- [bbgbridge](https://github.com/ran404/bbgbridge) - Easy to use Bloomberg Desktop API wrapper for Python.
- [polygon.io](https://github.com/polygon-io/client-python) - A python library for Polygon.io financial data APIs.
- [alpha_vantage](https://github.com/RomelTorres/alpha_vantage) - A python wrapper for Alpha Vantage API for financial data.
- [oilpriceapi](https://github.com/OilpriceAPI/python-sdk) - Python SDK for real-time oil and commodity prices (WTI, Brent, Urals, natural gas, coal) with OpenBB integration.
- [FinanceDataReader](https://github.com/FinanceData/FinanceDataReader) - Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks
- [pystlouisfed](https://github.com/TomasKoutek/pystlouisfed) - Python client for Federal Reserve Bank of St. Louis API - FRED, ALFRED, GeoFRED and FRASER.
- [python-bcb](https://github.com/wilsonfreitas/python-bcb) - Python interface to Brazilian Central Bank web services.
- [brapi.dev](https://brapi.dev/) - Brazilian stock market data API for B3/Bovespa quotes, historical OHLCV, dividends, and fundamentals.
- [market-prices](https://github.com/maread99/market_prices) - Create meaningful OHLCV datasets from knowledge of [exchange-calendars](https://github.com/gerrymanoim/exchange_calendars) (works out-the-box with data from Yahoo Finance).
- [tardis-python](https://github.com/tardis-dev/tardis-python) - Python interface for Tardis.dev high frequency crypto market data
- [lake-api](https://github.com/crypto-lake/lake-api) - Python interface for Crypto Lake high frequency crypto market data
- [tessa](https://github.com/ymyke/tessa) - simple, hassle-free access to price information of financial assets (currently based on yfinance and pycoingecko), including search and a symbol class.
- [pandaSDMX](https://github.com/dr-leo/pandaSDMX) - Python package that implements SDMX 2.1 (ISO 17369:2013), a format for exchange of statistical data and metadata used by national statistical agencies, central banks, and international organisations.
- [cif](https://github.com/LenkaV/CIF) - Python package that include few composite indicators, which summarize multidimensional relationships between individual economic indicators.
- [finagg](https://github.com/theOGognf/finagg) - finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML.
- [FinanceDatabase](https://github.com/JerBouma/FinanceDatabase) - This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
- [Trading Strategy](https://github.com/tradingstrategy-ai/trading-strategy/) - download price data for decentralised exchanges and lending protocols (DeFi)
- [datamule-python](https://github.com/john-friedman/datamule-python) - A package to work with SEC data. Incorporates datamule endpoints.
- [13F Insight](https://13finsight.com/) - Track institutional investor 13F holdings with AI-powered analysis, position change alerts, and filing summaries.
- [Earnings Feed](https://earningsfeed.com/api) - Real-time SEC filings, insider trades, and institutional holdings API.
- [Financial Data](https://financialdata.net/) - Stock Market and Financial Data API.
- [SaxoOpenAPI](https://www.developer.saxo/) - Saxo Bank financial data API.
- [fsynth](https://github.com/welcra/fsynth) - Python library for high-fidelity unlimited synthetic financial data generation using Heston Stochastic Volatility and Merton Jump Diffusion.
- [fedfred](https://nikhilxsunder.github.io/fedfred/) - FRED & GeoFRED Economic data API with preprocessed dataframe output in pandas/geopandas, polars/polars_st, and dask dataframes/geodataframes.
- [edgar-sec](https://nikhilxsunder.github.io/edgar-sec/) - EDGAR Financial data API with preprocessed dataclass outputs.
- [edgartools](https://github.com/dgunning/edgartools) - AI-native SEC EDGAR library with XBRL financials, clean text extraction, 17+ typed forms, and pandas DataFrames.
- [FXMacroData](https://fxmacrodata.com/) - Real-time forex macroeconomic API for all major currency pairs sourced from central bank announcements.
- [wallstreet](https://github.com/mcdallas/wallstreet) - Real time stock and option data.
### Excel Integration
- [xlwings](https://www.xlwings.org/) - Make Excel fly with Python.
- [openpyxl](https://openpyxl.readthedocs.io/en/latest/) - Read/Write Excel 2007 xlsx/xlsm files.
- [xlrd](https://github.com/python-excel/xlrd) - Library for developers to extract data from Microsoft Excel spreadsheet files.
- [xlsxwriter](https://xlsxwriter.readthedocs.io/) - Write files in the Excel 2007+ XLSX file format.
- [xlwt](https://github.com/python-excel/xlwt) - Library to create spreadsheet files compatible with MS Excel 97/2000/XP/2003 XLS files, on any platform.
- [DataNitro](https://datanitro.com/) - DataNitro also offers full-featured Python-Excel integration, including UDFs. Trial downloads are available, but users must purchase a license.
- [xlloop](http://xlloop.sourceforge.net) - XLLoop is an open source framework for implementing Excel user-defined functions (UDFs) on a centralised server (a function server).
- [expy](http://www.bnikolic.co.uk/expy/expy.html) - The ExPy add-in allows easy use of Python directly from within an Microsoft Excel spreadsheet, both to execute arbitrary code and to define new Excel functions.
- [pyxll](https://www.pyxll.com) - PyXLL is an Excel add-in that enables you to extend Excel using nothing but Python code.
### Visualization
- [D-Tale](https://github.com/man-group/dtale) - Visualizer for pandas dataframes and xarray datasets.
- [mplfinance](https://github.com/matplotlib/mplfinance) - matplotlib utilities for the visualization, and visual analysis, of financial data.
- [finplot](https://github.com/highfestiva/finplot) - Performant and effortless finance plotting for Python.
- [finvizfinance](https://github.com/lit26/finvizfinance) - Finviz analysis python library.
- [market-analy](https://github.com/maread99/market_analy) - Analysis and interactive charting using [market-prices](https://github.com/maread99/market_prices) and bqplot.
- [QuantInvestStrats](https://github.com/ArturSepp/QuantInvestStrats) - Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.
- [rallyplot](https://rallyplot.com) - Fast, GPU-accelerated financial plotting library
## R
### Numerical Libraries & Data Structures
- [xts](https://github.com/joshuaulrich/xts) - 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](https://github.com/Rdatatable/data.table) - 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](https://github.com/dppalomar/sparseEigen) - Sparse principal component analysis.
- [TSdbi](http://tsdbi.r-forge.r-project.org/) - Provides a common interface to time series databases.
- [tseries](https://cran.r-project.org/web/packages/tseries/index.html) - Time Series Analysis and Computational Finance.
- [zoo](https://cran.r-project.org/web/packages/zoo/index.html) - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).
- [tis](https://cran.r-project.org/web/packages/tis/index.html) - Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.
- [tfplot](https://cran.r-project.org/web/packages/tfplot/index.html) - Utilities for simple manipulation and quick plotting of time series data.
- [tframe](https://cran.r-project.org/web/packages/tframe/index.html) - A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.
### Data Sources
- [IBrokers](https://cran.r-project.org/web/packages/IBrokers/index.html) - Provides native R access to Interactive Brokers Trader Workstation API.
- [Rblpapi](https://github.com/Rblp/Rblpapi) - An R Interface to 'Bloomberg' is provided via the 'Blp API'.
- [Quandl](https://www.quandl.com/tools/r) - Get Financial Data Directly Into R.
- [Rbitcoin](https://github.com/jangorecki/Rbitcoin) - Unified markets API interface (bitstamp, kraken, btce, bitmarket).
- [GetTDData](https://github.com/msperlin/GetTDData) - Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto.
- [GetHFData](https://github.com/msperlin/GetHFData) - Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site.
- [Reddit WallstreetBets API](https://dashboard.nbshare.io/apps/reddit/api/) - Provides daily top 50 stocks from reddit (subreddit) Wallstreetbets and their sentiments via the API.
- [td](https://github.com/eddelbuettel/td) - Interfaces the 'twelvedata' API for stocks and (digital and standard) currencies.
- [rbcb](https://github.com/wilsonfreitas/rbcb) - R interface to Brazilian Central Bank web services.
- [rb3](https://github.com/ropensci/rb3) - A bunch of downloaders and parsers for data delivered from B3.
- [simfinapi](https://github.com/matthiasgomolka/simfinapi) - Makes 'SimFin' data (<https://simfin.com/>) easily accessible in R.
- [tidyfinance](https://github.com/tidy-finance/r-tidyfinance) - Tidy Finance helper functions to download financial data and process the raw data into a structured Format (tidy data), including
date conversion, scaling factor values, and filtering by the specified date.
### Financial Instruments and Pricing
- [RQuantLib](https://github.com/eddelbuettel/rquantlib) - RQuantLib connects GNU R with QuantLib.
- [quantmod](https://cran.r-project.org/web/packages/quantmod/index.html) - Quantitative Financial Modelling Framework.
- [Rmetrics](https://www.rmetrics.org) - The premier open source software solution for teaching and training quantitative finance.
- [fAsianOptions](https://cran.r-project.org/web/packages/fAsianOptions/index.html) - EBM and Asian Option Valuation.
- [fAssets](https://cran.r-project.org/web/packages/fAssets/index.html) - Analysing and Modelling Financial Assets.
- [fBasics](https://cran.r-project.org/web/packages/fBasics/index.html) - Markets and Basic Statistics.
- [fBonds](https://cran.r-project.org/web/packages/fBonds/index.html) - Bonds and Interest Rate Models.
- [fExoticOptions](https://cran.r-project.org/web/packages/fExoticOptions/index.html) - Exotic Option Valuation.
- [fOptions](https://cran.r-project.org/web/packages/fOptions/index.html) - Pricing and Evaluating Basic Options.
- [fPortfolio](https://cran.r-project.org/web/packages/fPortfolio/index.html) - Portfolio Selection and Optimization.
- [portfolio](https://github.com/dgerlanc/portfolio) - Analysing equity portfolios.
- [sparseIndexTracking](https://github.com/dppalomar/sparseIndexTracking) - Portfolio design to track an index.
- [covFactorModel](https://github.com/dppalomar/covFactorModel) - Covariance matrix estimation via factor models.
- [riskParityPortfolio](https://github.com/dppalomar/riskParityPortfolio) - Blazingly fast design of risk parity portfolios.
- [sde](https://cran.r-project.org/web/packages/sde/index.html) - Simulation and Inference for Stochastic Differential Equations.
- [YieldCurve](https://cran.r-project.org/web/packages/YieldCurve/index.html) - Modelling and estimation of the yield curve.
- [SmithWilsonYieldCurve](https://cran.r-project.org/web/packages/SmithWilsonYieldCurve/index.html) - Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.
- [ycinterextra](https://cran.r-project.org/web/packages/ycinterextra/index.html) - Yield curve or zero-coupon prices interpolation and extrapolation.
- [AmericanCallOpt](https://cran.r-project.org/web/packages/AmericanCallOpt/index.html) - This package includes pricing function for selected American call options with underlying assets that generate payouts.
- [VarSwapPrice](https://cran.r-project.org/web/packages/VarSwapPrice/index.html) - Pricing a variance swap on an equity index.
- [RND](https://cran.r-project.org/web/packages/RND/index.html) - Risk Neutral Density Extraction Package.
- [LSMonteCarlo](https://cran.r-project.org/web/packages/LSMonteCarlo/index.html) - American options pricing with Least Squares Monte Carlo method.
- [OptHedging](https://cran.r-project.org/web/packages/OptHedging/index.html) - Estimation of value and hedging strategy of call and put options.
- [tvm](https://cran.r-project.org/web/packages/tvm/index.html) - Time Value of Money Functions.
- [OptionPricing](https://cran.r-project.org/web/packages/OptionPricing/index.html) - Option Pricing with Efficient Simulation Algorithms.
- [credule](https://github.com/blenezet/credule) - Credit Default Swap Functions.
- [derivmkts](https://cran.r-project.org/web/packages/derivmkts/index.html) - Functions and R Code to Accompany Derivatives Markets.
- [FinCal](https://github.com/felixfan/FinCal) - Package for time value of money calculation, time series analysis and computational finance.
- [r-quant](https://github.com/artyyouth/r-quant) - R code for quantitative analysis in finance.
- [options.studies](https://github.com/taylorizing/options.studies) - options trading studies functions for use with options.data package and shiny.
- [PortfolioAnalytics](https://github.com/braverock/PortfolioAnalytics) - Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios.
- [fmbasics](https://github.com/imanuelcostigan/fmbasics) - Financial Market Building Blocks.
- [R-fixedincome](https://github.com/wilsonfreitas/R-fixedincome) - Fixed income tools for R.
### Trading
- [backtest](https://cran.r-project.org/web/packages/backtest/index.html) - Exploring Portfolio-Based Conjectures About Financial Instruments.
- [pa](https://cran.r-project.org/web/packages/pa/index.html) - Performance Attribution for Equity Portfolios.
- [TTR](https://github.com/joshuaulrich/TTR) - Technical Trading Rules.
- [QuantTools](https://quanttools.bitbucket.io/_site/index.html) - Enhanced Quantitative Trading Modelling.
- [blotter](https://github.com/braverock/blotter) - Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.
### Backtesting
- [quantstrat](https://github.com/braverock/quantstrat) - Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other financial research.
### Risk Analysis
- [PerformanceAnalytics](https://github.com/braverock/PerformanceAnalytics) - Econometric tools for performance and risk analysis.
### Factor Analysis
- [FactorAnalytics](https://github.com/braverock/FactorAnalytics) - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.
- [Expected Returns](https://github.com/JustinMShea/ExpectedReturns) - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.
### Time Series
- [tseries](https://cran.r-project.org/web/packages/tseries/index.html) - Time Series Analysis and Computational Finance.
- [fGarch](https://cran.r-project.org/web/packages/fGarch/index.html) - Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.
- [timeSeries](https://cran.r-project.org/web/packages/timeSeries/index.html) - Rmetrics - Financial Time Series Objects.
- [rugarch](https://github.com/alexiosg/rugarch) - Univariate GARCH Models.
- [rmgarch](https://github.com/alexiosg/rmgarch) - Multivariate GARCH Models.
- [tidypredict](https://github.com/edgararuiz/tidypredict) - Run predictions inside the database <https://tidypredict.netlify.com/>.
- [tidyquant](https://github.com/business-science/tidyquant) - Bringing financial analysis to the tidyverse.
- [timetk](https://github.com/business-science/timetk) - A toolkit for working with time series in R.
- [tibbletime](https://github.com/business-science/tibbletime) - Built on top of the tidyverse, tibbletime is an extension that allows for the creation of time aware tibbles through the setting of a time index.
- [matrixprofile](https://github.com/matrix-profile-foundation/matrixprofile) - Time series data mining library built on top of the novel Matrix Profile data structure and algorithms.
- [garchmodels](https://github.com/AlbertoAlmuinha/garchmodels) - A parsnip backend for GARCH models.
### Calendars
- [timeDate](https://cran.r-project.org/web/packages/timeDate/index.html) - Chronological and Calendar Objects
- [bizdays](https://github.com/wilsonfreitas/R-bizdays) - Business days calculations and utilities
## Matlab
### Alternatives
- [RunMat](https://runmat.org) - High performance, Open Source, MATLAB syntax runtime.
### FrameWorks
- [QUANTAXIS](https://github.com/yutiansut/quantaxis) - Integrated Quantitative Toolbox with Matlab.
- [PROJ_Option_Pricing_Matlab](https://github.com/jkirkby3/PROJ_Option_Pricing_Matlab) - Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader
## Julia
- [CcyConv.jl](https://github.com/bhftbootcamp/CcyConv.jl) - Currency conversion library for Julia
- [CryptoExchangeAPIs.jl](https://github.com/bhftbootcamp/CryptoExchangeAPIs.jl) - A Julia library for cryptocurrency exchange APIs
- [Fastback.jl](https://github.com/rbeeli/Fastback.jl) - Blazing fast Julia backtester.
- [Lucky.jl](https://github.com/oliviermilla/Lucky.jl) - Modular, asynchronous trading engine in pure Julia.
- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) - Quantlib implementation in pure Julia.
- [Ito.jl](https://github.com/aviks/Ito.jl) - A Julia package for quantitative finance.
- [LightweightCharts.jl](https://github.com/bhftbootcamp/LightweightCharts.jl) - Julia wrapper for Lightweight Charts™ by TradingView.
- [TALib.jl](https://github.com/femtotrader/TALib.jl) - A Julia wrapper for TA-Lib.
- [Miletus.jl](https://github.com/JuliaComputing/Miletus.jl) - A financial contract definition, modeling language, and valuation framework.
- [Temporal.jl](https://github.com/dysonance/Temporal.jl) - Flexible and efficient time series class & methods.
- [Indicators.jl](https://github.com/dysonance/Indicators.jl) - Financial market technical analysis & indicators on top of Temporal.
- [Strategems.jl](https://github.com/dysonance/Strategems.jl) - Quantitative systematic trading strategy development and backtesting.
- [TimeSeries.jl](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia.
- [TechnicalIndicatorCharts.jl](https://github.com/g-gundam/TechnicalIndicatorCharts.jl) - Visualize OnlineTechnicalIndicators.jl using LightweightCharts.jl.
- [MarketTechnicals.jl](https://github.com/JuliaQuant/MarketTechnicals.jl) - Technical analysis of financial time series on top of TimeSeries.
- [MarketData.jl](https://github.com/JuliaQuant/MarketData.jl) - Time series market data.
- [OnlineTechnicalIndicators.jl](https://github.com/femtotrader/OnlineTechnicalIndicators.jl) - Julia Technical Analysis Indicators via online algorithms.
- [OnlinePortfolioAnalytics.jl](https://github.com/femtotrader/OnlinePortfolioAnalytics.jl) - A Julia quantitative portfolio analytics (risk / performance) via online algorithms.
- [OnlineResamplers.jl](https://github.com/femtotrader/OnlineResamplers.jl) - High-performance Julia package for real-time resampling of financial market data.
- [RiskPerf.jl](https://github.com/rbeeli/RiskPerf.jl) - Quantitative risk and performance analysis package for financial time series powered by the Julia language.
- [TimeFrames.jl](https://github.com/femtotrader/TimeFrames.jl) - A Julia library that defines TimeFrame (essentially for resampling TimeSeries).
- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) - In-memory tabular data in Julia
- [TSFrames.jl](https://github.com/xKDR/TSFrames.jl) - Handle timeseries data on top of the powerful and mature DataFrames.jl
- [TimeArrays.jl](https://github.com/bhftbootcamp/TimeArrays.jl) - Time series handling for Julia
## Java
- [Strata](http://strata.opengamma.io/) - Modern open-source analytics and market risk library designed and written in Java.
- [JQuantLib](https://github.com/frgomes/jquantlib) - JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java.
- [finmath.net](http://finmath.net) - Java library with algorithms and methodologies related to mathematical finance.
- [quantcomponents](https://github.com/lsgro/quantcomponents) - Free Java components for Quantitative Finance and Algorithmic Trading.
- [DRIP](https://lakshmidrip.github.io/DRIP) - Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries.
- [ta4j](https://github.com/ta4j/ta4j) - A Java library for technical analysis.
## JavaScript
- [finance.js](https://github.com/ebradyjobory/finance.js) - A JavaScript library for common financial calculations.
- [portfolio-allocation](https://github.com/lequant40/portfolio_allocation_js) - PortfolioAllocation is a JavaScript library designed to help constructing financial portfolios made of several assets: bonds, commodities, cryptocurrencies, currencies, exchange traded funds (ETFs), mutual funds, stocks...
- [Ghostfolio](https://github.com/ghostfolio/ghostfolio) - Wealth management software to keep track of financial assets like stocks, ETFs or cryptocurrencies and make solid, data-driven investment decisions.
- [IndicatorTS](https://github.com/cinar/indicatorts) - Indicator is a TypeScript module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.
- [chart-patterns](https://github.com/focus1691/chart-patterns) - Technical analysis library for Market Profile, Volume Profile, Stacked Imbalances and High Volume Node indicators.
- [orderflow](https://github.com/focus1691/orderflow) - Orderflow trade aggregator for building Footprint Candles from exchange websocket data.
- [ccxt](https://github.com/ccxt/ccxt) - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges.
- [PENDAX](https://github.com/CompendiumFi/PENDAX-SDK) - Javascript SDK for Trading/Data API and Websockets for FTX, FTXUS, OKX, Bybit, & More.
- [pmxt](https://github.com/pmxt-dev/pmxt) - The CCXT for prediction markets. A unified API for trading on Polymarket, Kalshi, and more.
- [pmxt](https://github.com/qoery-com/pmxt) - A unified API for accessing prediction market data across multiple exchanges. CCXT for prediction markets.
### Data Visualization
- [QUANTAXIS_Webkit](https://github.com/yutiansut/QUANTAXIS_Webkit) - An awesome visualization center based on quantaxis.
## Haskell
- [quantfin](https://github.com/boundedvariation/quantfin) - quant finance in pure haskell.
- [Haxcel](https://github.com/MarcusRainbow/Haxcel) - Excel Addin for Haskell.
- [Ffinar](https://github.com/MarcusRainbow/Ffinar) - A financial maths library in Haskell.
## Scala
- [QuantScale](https://github.com/choucrifahed/quantscale) - Scala Quantitative Finance Library.
- [Scala Quant](https://github.com/frankcash/Scala-Quant) - Scala library for working with stock data from IFTTT recipes or Google Finance.
## Ruby
- [Jiji](https://github.com/unageanu/jiji2) - Open Source Forex algorithmic trading framework using OANDA REST API.
## Elixir/Erlang
- [Tai](https://github.com/fremantle-capital/tai) - Open Source composable, real time, market data and trade execution toolkit.
- [Workbench](https://github.com/fremantle-industries/workbench) - From Idea to Execution - Manage your trading operation across a globally distributed cluster
- [Prop](https://github.com/fremantle-industries/prop) - An open and opinionated trading platform using productive & familiar open source libraries and tools for strategy research, execution and operation.
## Golang
- [Kelp](https://github.com/stellar/kelp) - Kelp is an open-source Golang algorithmic cryptocurrency trading bot that runs on centralized exchanges and Stellar DEX (command-line usage and desktop GUI).
- [marketstore](https://github.com/alpacahq/marketstore) - DataFrame Server for Financial Timeseries Data.
- [IndicatorGo](https://github.com/cinar/indicator) - IndicatorGo is a Golang module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.
## CPP
- [QuantLib](https://github.com/lballabio/QuantLib) - The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.
- [QuantLibRisks](https://github.com/auto-differentiation/QuantLib-Risks-Cpp) - Fast risks with QuantLib in C++
- [XAD](https://github.com/auto-differentiation/xad) - Automatic Differentation (AAD) Library
- [TradeFrame](https://github.com/rburkholder/trade-frame) - C++ 17 based framework/library (with sample applications) for testing options based automated trading ideas using DTN IQ real time data feed and Interactive Brokers (TWS API) for trade execution. Comes with built-in [Option Greeks/IV](https://github.com/rburkholder/trade-frame/tree/master/lib/TFOptions) calculation library.
- [Hikyuu](https://github.com/fasiondog/hikyuu) - A base on Python/C++ open source high-performance quant framework for faster analysis and backtesting, contains the complete trading system components for reuse and combination. You can use python or c++ freely.
- [OrderMatchingEngine](https://github.com/PIYUSH-KUMAR1809/order-matching-engine) - A production-grade, lock-free, high-frequency trading matching engine achieving 150M+ orders/sec.
- [rallyplot](https://rallyplot.com) - Fast, GPU-accelerated financial plotting library
- [PandoraTrader](https://github.com/pegasusTrader/PandoraTrader) - A C++ CTP trading framework, with very clear logic
## Frameworks
- [QuantLib](https://github.com/lballabio/QuantLib) - The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.
- QuantLibRisks - Fast risks with QuantLib in [Python](https://pypi.org/project/QuantLib-Risks/) and [C++](https://github.com/auto-differentiation/QuantLib-Risks-Cpp)
- XAD - Automatic Differentiation (AAD) Library in [Python](https://pypi.org/project/xad/) and [C++](https://github.com/auto-differentiation/xad/)
- [JQuantLib](https://github.com/frgomes/jquantlib) - Java port.
- [RQuantLib](https://github.com/eddelbuettel/rquantlib) - R port.
- [QuantLibAddin](https://www.quantlib.org/quantlibaddin/) - Excel support.
- [QuantLibXL](https://www.quantlib.org/quantlibxl/) - Excel support.
- [QLNet](https://github.com/amaggiulli/qlnet) - .Net port.
- [PyQL](https://github.com/enthought/pyql) - Python port.
- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) - Julia port.
- [QuantLib-Python Documentation](https://quantlib-python-docs.readthedocs.io/) - Documentation for the Python bindings for the QuantLib library
- [TA-Lib](https://ta-lib.org) - perform technical analysis of financial market data.
- [ta-lib-python](https://github.com/TA-Lib/ta-lib-python)
- [ta-lib](https://github.com/TA-Lib/ta-lib)
- [Portfolio Optimizer](https://portfoliooptimizer.io/) - Portfolio Optimizer is a Web API for portfolio analysis and optimization.
- XAD: Automatic Differentation (AAD) Library for [Python](https://pypi.org/project/xad/) and [C++](https://github.com/auto-differentiation/xad)
## CSharp
- [QuantConnect](https://github.com/QuantConnect/Lean) - Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage.
- [StockSharp](https://github.com/StockSharp/StockSharp) - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
- [TDAmeritrade.DotNetCore](https://github.com/NVentimiglia/TDAmeritrade.DotNetCore) - Free, open-source .NET Client for the TD Ameritrade Trading Platform. Helps developers integrate TD Ameritrade API into custom trading solutions.
## Rust
- [QuantMath](https://github.com/MarcusRainbow/QuantMath) - Financial maths library for risk-neutral pricing and risk
- [Barter](https://github.com/barter-rs/barter-rs) - Open-source Rust framework for building event-driven live-trading & backtesting systems
- [LFEST](https://github.com/MathisWellmann/lfest-rs) - Simulated perpetual futures exchange to trade your strategy against.
- [TradeAggregation](https://github.com/MathisWellmann/trade_aggregation-rs) - Aggregate trades into user-defined candles using information driven rules.
- [SlidingFeatures](https://github.com/MathisWellmann/sliding_features-rs) - Chainable tree-like sliding windows for signal processing and technical analysis.
- [RustQuant](https://github.com/avhz/RustQuant) - Quantitative finance library written in Rust.
- [finalytics](https://github.com/Nnamdi-sys/finalytics) - A rust library for financial data analysis.
- [RunMat](https://github.com/runmat-org/runmat) - Rust runtime for MATLAB-syntax array math with automatic CPU/GPU execution and fused kernels for quant simulations.
## Reproducing Works, Training & Books
- [Auto-Differentiation Website](https://auto-differentiation.github.io/) - Background and resources on Automatic Differentiation (AD) / Adjoint Algorithmic Differentitation (AAD).
- [Derman Papers](https://github.com/MarcosCarreira/DermanPapers) - Notebooks that replicate original quantitative finance papers from Emanuel Derman.
- [KeepRule](https://keeprule.com/) - Curated library of decision-making principles and investment wisdom from masters like Buffett and Munger, featuring mental models for better investment thinking.
- [ML-Quant](https://www.ml-quant.com/) - Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.
- [volatility-trading](https://github.com/jasonstrimpel/volatility-trading) - A complete set of volatility estimators based on Euan Sinclair's Volatility Trading.
- [quant](https://github.com/paulperry/quant) - Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas.
- [fecon235](https://github.com/rsvp/fecon235) - Open source project for software tools in financial economics. Many jupyter notebook to verify theoretical ideas and practical methods interactively.
- [Quantitative-Notebooks](https://github.com/LongOnly/Quantitative-Notebooks) - Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
- [QuantEcon](https://quantecon.org/) - Lecture series on economics, finance, econometrics and data science; QuantEcon.py, QuantEcon.jl, notebooks
- [FinanceHub](https://github.com/Finance-Hub/FinanceHub) - Resources for Quantitative Finance
- [Python_Option_Pricing](https://github.com/dedwards25/Python_Option_Pricing) - An library to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options.
- [python-training](https://github.com/jpmorganchase/python-training) - J.P. Morgan's Python training for business analysts and traders.
- [Stock_Analysis_For_Quant](https://github.com/LastAncientOne/Stock_Analysis_For_Quant) - Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau.
- [algorithmic-trading-with-python](https://github.com/chrisconlan/algorithmic-trading-with-python) - Source code for Algorithmic Trading with Python (2020) by Chris Conlan.
- [MEDIUM_NoteBook](https://github.com/cerlymarco/MEDIUM_NoteBook) - Repository containing notebooks of [cerlymarco](https://github.com/cerlymarco)'s posts on Medium.
- [QuantFinance](https://github.com/PythonCharmers/QuantFinance) - Training materials in quantitative finance.
- [IPythonScripts](https://github.com/mgroncki/IPythonScripts) - Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.
- [Computational-Finance-Course](https://github.com/LechGrzelak/Computational-Finance-Course) - Materials for the course of Computational Finance.
- [Machine-Learning-for-Asset-Managers](https://github.com/emoen/Machine-Learning-for-Asset-Managers) - Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
- [Python-for-Finance-Cookbook](https://github.com/PacktPublishing/Python-for-Finance-Cookbook) - Python for Finance Cookbook, published by Packt.
- [modelos_vol_derivativos](https://github.com/ysaporito/modelos_vol_derivativos) - "Modelos de Volatilidade para Derivativos" book's Jupyter notebooks
- [NMOF](https://github.com/enricoschumann/NMOF) - Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).
- [py4fi2nd](https://github.com/yhilpisch/py4fi2nd) - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.
- [aiif](https://github.com/yhilpisch/aiif) - Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.
- [py4at](https://github.com/yhilpisch/py4at) - Jupyter Notebooks and code for the book Python for Algorithmic Trading (O'Reilly) by Yves Hilpisch.
- [dawp](https://github.com/yhilpisch/dawp) - Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.
- [dx](https://github.com/yhilpisch/dx) - DX Analytics | Financial and Derivatives Analytics with Python.
- [QuantFinanceBook](https://github.com/LechGrzelak/QuantFinanceBook) - Quantitative Finance book.
- [rough_bergomi](https://github.com/ryanmccrickerd/rough_bergomi) - A Python implementation of the rough Bergomi model.
- [frh-fx](https://github.com/ryanmccrickerd/frh-fx) - A python implementation of the fast-reversion Heston model of Mechkov for FX purposes.
- [Value Investing Studies](https://github.com/euclidjda/value-investing-studies) - A collection of data analysis studies that examine the performance and characteristics of value investing over long periods of time.
- [Machine Learning Asset Management](https://github.com/firmai/machine-learning-asset-management) - Machine Learning in Asset Management (by @firmai).
- [Deep Learning Machine Learning Stock](https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock) - Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.
- [Technical Analysis and Feature Engineering](https://github.com/jo-cho/Technical_Analysis_and_Feature_Engineering) - Feature Engineering and Feature Importance of Machine Learning in Financial Market.
- [Differential Machine Learning and Axes that matter by Brian Huge and Antoine Savine](https://github.com/differential-machine-learning/notebooks) - Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
- [systematictradingexamples](https://github.com/robcarver17/systematictradingexamples) - Examples of code related to book [Systematic Trading](www.systematictrading.org) and [blog](http://qoppac.blogspot.com)
- [pysystemtrade_examples](https://github.com/robcarver17/pysystemtrade_examples) - Examples using pysystemtrade for Robert Carver's [blog](http://qoppac.blogspot.com).
- [ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes) - Machine Learning in Finance: From Theory to Practice Book
- [Hands-On Machine Learning for Algorithmic Trading](https://github.com/packtpublishing/hands-on-machine-learning-for-algorithmic-trading) - Hands-On Machine Learning for Algorithmic Trading, published by Packt
- [financialnoob-misc](https://github.com/financialnoob/misc) - Codes from @financialnoob's posts
- [MesoSim Options Trading Strategy Library](https://github.com/deltaray-io/strategy-library) - Free and public Options Trading strategy library for MesoSim.
- [Quant-Finance-With-Python-Code](https://github.com/lingyixu/Quant-Finance-With-Python-Code) - Repo for code examples in Quantitative Finance with Python by Chris Kelliher
- [QuantFinanceTraining](https://github.com/JoaoJungblut/QuantFinanceTraining) - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative Finance). The codes are organized by class, facilitating navigation and reference.
- [Statistical-Learning-based-Portfolio-Optimization](https://github.com/YannickKae/Statistical-Learning-based-Portfolio-Optimization) - This R Shiny App utilizes the Hierarchical Equal Risk Contribution (HERC) approach, a modern portfolio optimization method developed by Raffinot (2018).
- [book_irds3](https://github.com/attack68/book_irds3) - Code repository for Pricing and Trading Interest Rate Derivatives.
- [Autoencoder-Asset-Pricing-Models](https://github.com/RichardS0268/Autoencoder-Asset-Pricing-Models) - Reimplementation of Autoencoder Asset Pricing Models ([GKX, 2019](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3335536)).
- [Finance](https://github.com/shashankvemuri/Finance) - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.
- [101_formulaic_alphas](https://github.com/ram-ki/101_formulaic_alphas) - Implementation of [101 formulaic alphas](https://arxiv.org/ftp/arxiv/papers/1601/1601.00991.pdf) using qstrader.
- [Tidy Finance](https://www.tidy-finance.org/) - An opinionated approach to empirical research in financial economics - a fully transparent, open-source code base in multiple programming languages (Python and R) to enable the reproducible implementation of financial research projects for students and practitioners.
- [RoughVolatilityWorkshop](https://github.com/jgatheral/RoughVolatilityWorkshop) - 2024 QuantMind's Rough Volatility Workshop lectures.
- [AFML](https://github.com/boyboi86/AFML) - All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.
- [AlgoTradingLib](https://github.com/usdaud/algotradinglib.github.io) - A catalog of algorithmic trading libraries, frameworks, strategies, and educational materials.
Add Prop Trader Compass to Risk Management tools
## Related Lists
- [awesome-sec-filings](https://github.com/vibeyclaw/awesome-sec-filings) - A curated list of tools, data sources, libraries, and resources for working with SEC filings (13F, 10-K, 10-Q, 8-K).
================================================
FILE: site/projects.csv
================================================
project,section,last_commit,url,description,github,cran,repo
numpy,Python > Numerical Libraries & Data Structures,,https://www.numpy.org,NumPy is the fundamental package for scientific computing with Python.,False,False,
scipy,Python > Numerical Libraries & Data Structures,,https://www.scipy.org,"SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.",False,False,
pandas,Python > Numerical Libraries & Data Structures,,https://pandas.pydata.org,"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.",False,False,
polars,Python > Numerical Libraries & Data Structures,,https://docs.pola.rs/,Polars is a blazingly fast DataFrame library for manipulating structured data.,False,False,
quantdsl,Python > Numerical Libraries & Data Structures,2017-10-26,https://github.com/johnbywater/quantdsl,Domain specific language for quantitative analytics in finance and trading.,True,False,johnbywater/quantdsl
statistics,Python > Numerical Libraries & Data Structures,,https://docs.python.org/3/library/statistics.html,Builtin Python library for all basic statistical calculations.,False,False,
sympy,Python > Numerical Libraries & Data Structures,,https://www.sympy.org/,SymPy is a Python library for symbolic mathematics.,False,False,
pymc3,Python > Numerical Libraries & Data Structures,,https://docs.pymc.io/,Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano.,False,False,
modelx,Python > Numerical Libraries & Data Structures,,https://docs.modelx.io/,Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas.,False,False,
ArcticDB,Python > Numerical Libraries & Data Structures,2025-12-30,https://github.com/man-group/ArcticDB,High performance datastore for time series and tick data.,True,False,man-group/ArcticDB
OpenBB Terminal,Python > Financial Instruments and Pricing,2026-01-02,https://github.com/OpenBB-finance/OpenBBTerminal,Terminal for investment research for everyone.,True,False,OpenBB-finance/OpenBBTerminal
Fincept Terminal,Python > Financial Instruments and Pricing,2026-01-03,https://github.com/Fincept-Corporation/FinceptTerminal,Advance Data Based A.I Terminal for all Types of Financial Asset Research.,True,False,Fincept-Corporation/FinceptTerminal
PyQL,Python > Financial Instruments and Pricing,2025-08-20,https://github.com/enthought/pyql,QuantLib's Python port.,True,False,enthought/pyql
pyfin,Python > Financial Instruments and Pricing,2014-12-03,https://github.com/opendoor-labs/pyfin,Basic options pricing in Python. *ARCHIVED*,True,False,opendoor-labs/pyfin
vollib,Python > Financial Instruments and Pricing,2023-04-01,https://github.com/vollib/vollib,"vollib is a python library for calculating option prices, implied volatility and greeks.",True,False,vollib/vollib
QuantPy,Python > Financial Instruments and Pricing,2017-11-28,https://github.com/jsmidt/QuantPy,A framework for quantitative finance In python.,True,False,jsmidt/QuantPy
Finance-Python,Python > Financial Instruments and Pricing,2024-01-01,https://github.com/alpha-miner/Finance-Python,Python tools for Finance.,True,False,alpha-miner/Finance-Python
ffn,Python > Financial Instruments and Pricing,2025-12-15,https://github.com/pmorissette/ffn,A financial function library for Python.,True,False,pmorissette/ffn
pynance,Python > Financial Instruments and Pricing,2021-02-03,https://github.com/GriffinAustin/pynance,Lightweight Python library for assembling and analyzing financial data.,True,False,GriffinAustin/pynance
tia,Python > Financial Instruments and Pricing,2017-06-05,https://github.com/bpsmith/tia,Toolkit for integration and analysis.,True,False,bpsmith/tia
hasura/base-python-dash,Python > Financial Instruments and Pricing,,https://platform.hasura.io/hub/projects/hasura/base-python-dash,"Hasura quick start to deploy Dash framework. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python.",False,False,
hasura/base-python-bokeh,Python > Financial Instruments and Pricing,,https://platform.hasura.io/hub/projects/hasura/base-python-bokeh,Hasura quick start to visualize data with bokeh library.,False,False,
pysabr,Python > Financial Instruments and Pricing,2022-04-21,https://github.com/ynouri/pysabr,SABR model Python implementation.,True,False,ynouri/pysabr
FinancePy,Python > Financial Instruments and Pricing,2025-11-07,https://github.com/domokane/FinancePy,"A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.",True,False,domokane/FinancePy
gs-quant,Python > Financial Instruments and Pricing,2025-12-18,https://github.com/goldmansachs/gs-quant,Python toolkit for quantitative finance,True,False,goldmansachs/gs-quant
willowtree,Python > Financial Instruments and Pricing,2018-07-14,https://github.com/federicomariamassari/willowtree,Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.,True,False,federicomariamassari/willowtree
financial-engineering,Python > Financial Instruments and Pricing,2017-11-20,https://github.com/federicomariamassari/financial-engineering,"Applications of Monte Carlo methods to financial engineering projects, in Python.",True,False,federicomariamassari/financial-engineering
optlib,Python > Financial Instruments and Pricing,2022-11-18,https://github.com/dbrojas/optlib,A library for financial options pricing written in Python.,True,False,dbrojas/optlib
tf-quant-finance,Python > Financial Instruments and Pricing,2025-03-21,https://github.com/google/tf-quant-finance,High-performance TensorFlow library for quantitative finance.,True,False,google/tf-quant-finance
Q-Fin,Python > Financial Instruments and Pricing,2023-04-07,https://github.com/RomanMichaelPaolucci/Q-Fin,A Python library for mathematical finance.,True,False,RomanMichaelPaolucci/Q-Fin
Quantsbin,Python > Financial Instruments and Pricing,2021-05-23,https://github.com/quantsbin/Quantsbin,"Tools for pricing and plotting of vanilla option prices, greeks and various other analysis around them.",True,False,quantsbin/Quantsbin
finoptions,Python > Financial Instruments and Pricing,2024-02-01,https://github.com/bbcho/finoptions-dev,Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.,True,False,bbcho/finoptions-dev
pypme,Python > Financial Instruments and Pricing,2025-03-31,https://github.com/ymyke/pypme,PME (Public Market Equivalent) calculation.,True,False,ymyke/pypme
AbsBox,Python > Financial Instruments and Pricing,2025-09-19,https://github.com/yellowbean/AbsBox,A Python based library to model cashflow for structured product like Asset-backed securities (ABS) and Mortgage-backed securities (MBS).,True,False,yellowbean/AbsBox
Intrinsic-Value-Calculator,Python > Financial Instruments and Pricing,2025-07-02,https://github.com/akashaero/Intrinsic-Value-Calculator,A Python tool for quick calculations of a stock's fair value using Discounted Cash Flow analysis.,True,False,akashaero/Intrinsic-Value-Calculator
Kelly-Criterion,Python > Financial Instruments and Pricing,2019-02-16,https://github.com/deltaray-io/kelly-criterion,Kelly Criterion implemented in Python to size portfolios based on J. L. Kelly Jr's formula.,True,False,deltaray-io/kelly-criterion
rateslib,Python > Financial Instruments and Pricing,2025-12-23,https://github.com/attack68/rateslib,"A fixed income library for pricing bonds and bond futures, and derivatives such as IRS, cross-currency and FX swaps.",True,False,attack68/rateslib
fypy,Python > Financial Instruments and Pricing,2025-02-27,https://github.com/jkirkby3/fypy,"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.",True,False,jkirkby3/fypy
pandas_talib,Python > Indicators,2018-05-30,https://github.com/femtotrader/pandas_talib,A Python Pandas implementation of technical analysis indicators.,True,False,femtotrader/pandas_talib
finta,Python > Indicators,2022-07-24,https://github.com/peerchemist/finta,Common financial technical analysis indicators implemented in Pandas.,True,False,peerchemist/finta
Tulipy,Python > Indicators,2019-04-11,https://github.com/cirla/tulipy,Financial Technical Analysis Indicator Library (Python bindings for [tulipindicators](https://github.com/TulipCharts/tulipindicators)),True,False,cirla/tulipy
lppls,Python > Indicators,2024-12-05,https://github.com/Boulder-Investment-Technologies/lppls,A Python module for fitting the [Log-Periodic Power Law Singularity (LPPLS)](https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.,True,False,Boulder-Investment-Technologies/lppls
talipp,Python > Indicators,2025-09-09,https://github.com/nardew/talipp,Incremental technical analysis library for Python.,True,False,nardew/talipp
streaming_indicators,Python > Indicators,2025-04-27,https://github.com/mr-easy/streaming_indicators,A python library for computing technical analysis indicators on streaming data.,True,False,mr-easy/streaming_indicators
skfolio,Python > Trading & Backtesting,2025-12-19,https://github.com/skfolio/skfolio,"Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models.",True,False,skfolio/skfolio
Investing algorithm framework,Python > Trading & Backtesting,2025-12-30,https://github.com/coding-kitties/investing-algorithm-framework,"Framework for developing, backtesting, and deploying automated trading algorithms.",True,False,coding-kitties/investing-algorithm-framework
QSTrader,Python > Trading & Backtesting,2024-06-24,https://github.com/mhallsmoore/qstrader,QSTrader backtesting simulation engine.,True,False,mhallsmoore/qstrader
Blankly,Python > Trading & Backtesting,2024-12-30,https://github.com/Blankly-Finance/Blankly,"Fully integrated backtesting, paper trading, and live deployment.",True,False,Blankly-Finance/Blankly
TA-Lib,Python > Trading & Backtesting,2025-12-22,https://github.com/mrjbq7/ta-lib,Python wrapper for TA-Lib (<http://ta-lib.org/>).,True,False,mrjbq7/ta-lib
zipline,Python > Trading & Backtesting,2020-10-14,https://github.com/quantopian/zipline,Pythonic algorithmic trading library.,True,False,quantopian/zipline
zipline-reloaded,Python > Trading & Backtesting,2025-11-13,https://github.com/stefan-jansen/zipline-reloaded,"Zipline, a Pythonic Algorithmic Trading Library.",True,False,stefan-jansen/zipline-reloaded
QuantSoftware Toolkit,Python > Trading & Backtesting,2016-10-07,https://github.com/QuantSoftware/QuantSoftwareToolkit,Python-based open source software framework designed to support portfolio construction and management.,True,False,QuantSoftware/QuantSoftwareToolkit
quantitative,Python > Trading & Backtesting,2019-03-03,https://github.com/jeffrey-liang/quantitative,"Quantitative finance, and backtesting library.",True,False,jeffrey-liang/quantitative
analyzer,Python > Trading & Backtesting,2015-12-22,https://github.com/llazzaro/analyzer,Python framework for real-time financial and backtesting trading strategies.,True,False,llazzaro/analyzer
bt,Python > Trading & Backtesting,2025-11-24,https://github.com/pmorissette/bt,Flexible Backtesting for Python.,True,False,pmorissette/bt
backtrader,Python > Trading & Backtesting,2023-04-19,https://github.com/backtrader/backtrader,Python Backtesting library for trading strategies.,True,False,backtrader/backtrader
pythalesians,Python > Trading & Backtesting,2016-09-23,https://github.com/thalesians/pythalesians,"Python library to backtest trading strategies, plot charts, seamlessly download market data, analyze market patterns etc.",True,False,thalesians/pythalesians
pybacktest,Python > Trading & Backtesting,2019-09-09,https://github.com/ematvey/pybacktest,"Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier.",True,False,ematvey/pybacktest
pyalgotrade,Python > Trading & Backtesting,2023-03-05,https://github.com/gbeced/pyalgotrade,Python Algorithmic Trading Library.,True,False,gbeced/pyalgotrade
basana,Python > Trading & Backtesting,2025-12-29,https://github.com/gbeced/basana,"A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.",True,False,gbeced/basana
tradingWithPython,Python > Trading & Backtesting,,https://pypi.org/project/tradingWithPython/,A collection of functions and classes for Quantitative trading.,False,False,
Pandas TA,Python > Trading & Backtesting,error,https://github.com/twopirllc/pandas-ta,Pandas TA is an easy to use Python 3 Pandas Extension with 115+ Indicators. Easily build Custom Strategies.,True,False,twopirllc/pandas-ta
ta,Python > Trading & Backtesting,2023-11-02,https://github.com/bukosabino/ta,Technical Analysis Library using Pandas (Python),True,False,bukosabino/ta
algobroker,Python > Trading & Backtesting,2016-03-31,https://github.com/joequant/algobroker,This is an execution engine for algo trading.,True,False,joequant/algobroker
pysentosa,Python > Trading & Backtesting,,https://pypi.org/project/pysentosa/,Python API for sentosa trading system.,False,False,
finmarketpy,Python > Trading & Backtesting,2025-03-10,https://github.com/cuemacro/finmarketpy,Python library for backtesting trading strategies and analyzing financial markets.,True,False,cuemacro/finmarketpy
binary-martingale,Python > Trading & Backtesting,2017-10-16,https://github.com/metaperl/binary-martingale,Computer program to automatically trade binary options martingale style.,True,False,metaperl/binary-martingale
fooltrader,Python > Trading & Backtesting,2020-07-19,https://github.com/foolcage/fooltrader,the project using big-data technology to provide an uniform way to analyze the whole market.,True,False,foolcage/fooltrader
zvt,Python > Trading & Backtesting,2025-11-30,https://github.com/zvtvz/zvt,"the project using sql, pandas to provide an uniform and extendable way to record data, computing factors, select securities, backtesting, realtime trading and it could show all of them in clearly charts in realtime.",True,False,zvtvz/zvt
pylivetrader,Python > Trading & Backtesting,2022-04-11,https://github.com/alpacahq/pylivetrader,zipline-compatible live trading library.,True,False,alpacahq/pylivetrader
pipeline-live,Python > Trading & Backtesting,2022-04-11,https://github.com/alpacahq/pipeline-live,zipline's pipeline capability with IEX for live trading.,True,False,alpacahq/pipeline-live
zipline-extensions,Python > Trading & Backtesting,2018-09-17,https://github.com/quantrocket-llc/zipline-extensions,Zipline extensions and adapters for QuantRocket.,True,False,quantrocket-llc/zipline-extensions
moonshot,Python > Trading & Backtesting,2024-08-14,https://github.com/quantrocket-llc/moonshot,Vectorized backtester and trading engine for QuantRocket based on Pandas.,True,False,quantrocket-llc/moonshot
PyPortfolioOpt,Python > Trading & Backtesting,2025-11-29,https://github.com/robertmartin8/PyPortfolioOpt,"Financial portfolio optimization in python, including classical efficient frontier and advanced methods.",True,False,robertmartin8/PyPortfolioOpt
Eiten,Python > Trading & Backtesting,2020-09-21,https://github.com/tradytics/eiten,"Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios.",True,False,tradytics/eiten
riskparity.py,Python > Trading & Backtesting,2024-05-27,https://github.com/dppalomar/riskparity.py,fast and scalable design of risk parity portfolios with TensorFlow 2.0,True,False,dppalomar/riskparity.py
mlfinlab,Python > Trading & Backtesting,2021-12-01,https://github.com/hudson-and-thames/mlfinlab,"Implementations regarding ""Advances in Financial Machine Learning"" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling)",True,False,hudson-and-thames/mlfinlab
pyqstrat,Python > Trading & Backtesting,2023-11-05,https://github.com/abbass2/pyqstrat,"A fast, extensible, transparent python library for backtesting quantitative strategies.",True,False,abbass2/pyqstrat
NowTrade,Python > Trading & Backtesting,2017-02-07,https://github.com/edouardpoitras/NowTrade,Python library for backtesting technical/mechanical strategies in the stock and currency markets.,True,False,edouardpoitras/NowTrade
pinkfish,Python > Trading & Backtesting,2025-05-12,https://github.com/fja05680/pinkfish,A backtester and spreadsheet library for security analysis.,True,False,fja05680/pinkfish
aat,Python > Trading & Backtesting,2025-12-15,https://github.com/timkpaine/aat,Async Algorithmic Trading Engine,True,False,timkpaine/aat
Backtesting.py,Python > Trading & Backtesting,,https://kernc.github.io/backtesting.py/,Backtest trading strategies in Python,False,False,
catalyst,Python > Trading & Backtesting,2021-09-22,https://github.com/enigmampc/catalyst,An Algorithmic Trading Library for Crypto-Assets in Python,True,False,enigmampc/catalyst
quantstats,Python > Trading & Backtesting,2025-09-05,https://github.com/ranaroussi/quantstats,"Portfolio analytics for quants, written in Python",True,False,ranaroussi/quantstats
qtpylib,Python > Trading & Backtesting,2021-03-24,https://github.com/ranaroussi/qtpylib,"QTPyLib, Pythonic Algorithmic Trading <http://qtpylib.io>",True,False,ranaroussi/qtpylib
Quantdom,Python > Trading & Backtesting,2019-03-12,https://github.com/constverum/Quantdom,Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:],True,False,constverum/Quantdom
freqtrade,Python > Trading & Backtesting,2026-01-03,https://github.com/freqtrade/freqtrade,"Free, open source crypto trading bot",True,False,freqtrade/freqtrade
algorithmic-trading-with-python,Python > Trading & Backtesting,2021-06-01,https://github.com/chrisconlan/algorithmic-trading-with-python,"Free `pandas` and `scikit-learn` resources for trading simulation, backtesting, and machine learning on financial data.",True,False,chrisconlan/algorithmic-trading-with-python
DeepDow,Python > Trading & Backtesting,2024-01-24,https://github.com/jankrepl/deepdow,Portfolio optimization with deep learning,True,False,jankrepl/deepdow
Qlib,Python > Trading & Backtesting,2025-12-30,https://github.com/microsoft/qlib,"An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.",True,False,microsoft/qlib
machine-learning-for-trading,Python > Trading & Backtesting,2023-03-05,https://github.com/stefan-jansen/machine-learning-for-trading,Code and resources for Machine Learning for Algorithmic Trading,True,False,stefan-jansen/machine-learning-for-trading
AlphaPy,Python > Trading & Backtesting,2025-08-24,https://github.com/ScottfreeLLC/AlphaPy,"Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost",True,False,ScottfreeLLC/AlphaPy
jesse,Python > Trading & Backtesting,2025-12-14,https://github.com/jesse-ai/jesse,An advanced crypto trading bot written in Python,True,False,jesse-ai/jesse
rqalpha,Python > Trading & Backtesting,2025-12-01,https://github.com/ricequant/rqalpha,"A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities.",True,False,ricequant/rqalpha
FinRL-Library,Python > Trading & Backtesting,2025-12-06,https://github.com/AI4Finance-LLC/FinRL-Library,A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020.,True,False,AI4Finance-LLC/FinRL-Library
bulbea,Python > Trading & Backtesting,2017-03-19,https://github.com/achillesrasquinha/bulbea,Deep Learning based Python Library for Stock Market Prediction and Modelling.,True,False,achillesrasquinha/bulbea
ib_nope,Python > Trading & Backtesting,2021-04-22,https://github.com/ajhpark/ib_nope,Automated trading system for NOPE strategy over IBKR TWS.,True,False,ajhpark/ib_nope
OctoBot,Python > Trading & Backtesting,2025-12-29,https://github.com/Drakkar-Software/OctoBot,"Open source cryptocurrency trading bot for high frequency, arbitrage, TA and social trading with an advanced web interface.",True,False,Drakkar-Software/OctoBot
bta-lib,Python > Trading & Backtesting,2020-03-11,https://github.com/mementum/bta-lib,Technical Analysis library in pandas for backtesting algotrading and quantitative analysis.,True,False,mementum/bta-lib
Stock-Prediction-Models,Python > Trading & Backtesting,2021-01-05,https://github.com/huseinzol05/Stock-Prediction-Models,Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations.,True,False,huseinzol05/Stock-Prediction-Models
TuneTA,Python
gitextract_kr02khkb/ ├── .github/ │ └── workflows/ │ └── build.yml ├── .gitignore ├── .nojekyll ├── README.md ├── cran.csv ├── cranscrape.py ├── legacy.txt ├── parse.py ├── pyproject.toml ├── quants.md ├── recommendation.ipynb ├── site/ │ ├── .gitignore │ ├── CODE_OF_CONDUCT.qmd │ ├── _quarto.yml │ ├── about.qmd │ ├── index.qmd │ ├── projects.csv │ └── projects.qmd ├── styles.css └── topic.py
SYMBOL INDEX (6 symbols across 2 files)
FILE: cranscrape.py
function get_data (line 65) | def get_data(url):
FILE: parse.py
function extract_repo (line 13) | def extract_repo(url):
function get_last_commit (line 22) | def get_last_commit(repo):
class Project (line 35) | class Project(Thread):
method __init__ (line 36) | def __init__(self, match, section):
method run (line 42) | def run(self):
Condensed preview — 20 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (275K chars).
[
{
"path": ".github/workflows/build.yml",
"chars": 1180,
"preview": "name: Update site\n\non:\n schedule:\n - cron: \"0 1 * * *\"\n push:\n branches: [ master ]\n\njobs:\n build:\n runs-on:"
},
{
"path": ".gitignore",
"chars": 47,
"preview": "env.ps1\n.vscode\n/.quarto/\ngithub.ipynb\nbuild.sh"
},
{
"path": ".nojekyll",
"chars": 0,
"preview": ""
},
{
"path": "README.md",
"chars": 74065,
"preview": "# Awesome Quant\n\nA curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)."
},
{
"path": "cran.csv",
"chars": 3895,
"preview": "cran,github,repo\nhttps://cran.r-project.org/web/packages/xts/index.html,https://github.com/joshuaulrich/xts,joshuaulrich"
},
{
"path": "cranscrape.py",
"chars": 3902,
"preview": "\nimport requests\nimport re\nimport pandas as pd\n\nreu = re.compile(r'https://github.com/([\\w-]+/[\\w-]+)')\nred = re.compile"
},
{
"path": "legacy.txt",
"chars": 728,
"preview": "\nhttps://cran.r-project.org/web/views/Finance.html\n\nhttp://www.bloomberglabs.com/api/libraries/\n\nhttps://github.com/bpsm"
},
{
"path": "parse.py",
"chars": 2409,
"preview": "import os\nimport re\nimport pandas as pd\nfrom threading import Thread\n\nfrom github import Auth, Github\n\n# using an access"
},
{
"path": "pyproject.toml",
"chars": 334,
"preview": "[tool.poetry]\nname = \"awesome-quant\"\nversion = \"0.1.0\"\ndescription = \"\"\nauthors = [\"Wilson Freitas <wilson.freitas@gmail"
},
{
"path": "quants.md",
"chars": 113,
"preview": "quants\n\nhttps://sites.google.com/site/peterreinhardhansen/\n\nhttp://www.dixiederivatives.com/excelpricingfiles.htm"
},
{
"path": "recommendation.ipynb",
"chars": 9854,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Recommendation System for Apna_"
},
{
"path": "site/.gitignore",
"chars": 14,
"preview": "/.quarto/\ndocs"
},
{
"path": "site/CODE_OF_CONDUCT.qmd",
"chars": 5430,
"preview": "---\ntitle: \"Contributor Covenant Code of Conduct\"\ninclude-in-header:\n - text: |\n <script async src=\"https://pagead"
},
{
"path": "site/_quarto.yml",
"chars": 405,
"preview": "project:\n type: website\n output-dir: docs\n render:\n - \"*.qmd\"\n - \"*.ipynb\"\n - \"!quant.md\"\n - \"!github.ipy"
},
{
"path": "site/about.qmd",
"chars": 231,
"preview": "---\ntitle: \"About\"\ninclude-in-header:\n - text: |\n <script async src=\"https://pagead2.googlesyndication.com/pagead/"
},
{
"path": "site/index.qmd",
"chars": 74091,
"preview": "---\ntitle: \"Awesome Quant\"\ndate-modified: last-modified\nkeywords: [\"r packages\", \"python packages\", \"julia packages\",\n "
},
{
"path": "site/projects.csv",
"chars": 91872,
"preview": "project,section,last_commit,url,description,github,cran,repo\nnumpy,Python > Numerical Libraries & Data Structures,,https"
},
{
"path": "site/projects.qmd",
"chars": 858,
"preview": "---\ntitle: \"Projects\"\nformat: \n html:\n df-print: kable\ninclude-in-header:\n - text: |\n <script async src=\"htt"
},
{
"path": "styles.css",
"chars": 17,
"preview": "/* css styles */\n"
},
{
"path": "topic.py",
"chars": 528,
"preview": "\nimport os\nfrom github import Github\n\n# using an access token\ng = Github(os.environ['GITHUB_ACCESS_TOKEN'])\n\n# ts = g.se"
}
]
About this extraction
This page contains the full source code of the wilsonfreitas/awesome-quant GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 20 files (263.6 KB), approximately 68.9k tokens, and a symbol index with 6 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.