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 ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/build.yml ================================================ 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 ================================================ FILE: .gitignore ================================================ env.ps1 .vscode /.quarto/ github.ipynb build.sh ================================================ FILE: .nojekyll ================================================ ================================================ FILE: README.md ================================================ # Awesome Quant A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance). [![](https://awesome.re/badge.svg)](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 (). - [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 - [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) ![Github last commit (branch)](https://img.shields.io/github/last-commit/JustinGuese/python_tradingbot_framework/main) - 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! - [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! - [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 () 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 . - [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 https://cran.r-project.org/web/packages/data.table/index.html,https://github.com/Rdatatable/data,Rdatatable/data 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/tis/index.html,, https://cran.r-project.org/web/packages/tfplot/index.html,, https://cran.r-project.org/web/packages/tframe/index.html,, https://cran.r-project.org/web/packages/IBrokers/index.html,, 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https://cran.r-project.org/web/packages/fPortfolio/index.html,, https://cran.r-project.org/web/packages/portfolio/index.html,https://github.com/dgerlanc/portfolio,dgerlanc/portfolio 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://github.com/blenezet/credule,blenezet/credule 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://github.com/joshuaulrich/TTR,joshuaulrich/TTR https://cran.r-project.org/web/packages/PerformanceAnalytics/index.html,https://github.com/braverock/PerformanceAnalytics,braverock/PerformanceAnalytics 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://github.com/joshuaulrich/xts,joshuaulrich/xts 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://github.com/alexiosg/rmgarch,alexiosg/rmgarch https://cran.r-project.org/web/packages/timeDate/index.html,, https://cran.r-project.org/web/packages/bizdays/index.html,https://github.com/wilsonfreitas/R-bizdays,wilsonfreitas/R-bizdays ================================================ 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' urls = [ 'https://cran.r-project.org/web/packages/xts/index.html', 'https://cran.r-project.org/web/packages/data.table/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/tis/index.html', 'https://cran.r-project.org/web/packages/tfplot/index.html', 'https://cran.r-project.org/web/packages/tframe/index.html', 'https://cran.r-project.org/web/packages/IBrokers/index.html', 'https://cran.r-project.org/web/packages/Rblpapi/index.html', 'https://cran.r-project.org/web/packages/Rbitcoin/index.html', 'https://cran.r-project.org/web/packages/GetTDData/index.html', 'https://cran.r-project.org/web/packages/GetHFData/index.html', 'https://cran.r-project.org/package=td', 'https://cran.r-project.org/web/packages/quantmod/index.html', 'https://cran.r-project.org/web/packages/fAsianOptions/index.html', '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 "] 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: | --- ## 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: | --- 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: | --- A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance). [![](https://awesome.re/badge.svg)](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 (). - [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 - [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) ![Github last commit (branch)](https://img.shields.io/github/last-commit/JustinGuese/python_tradingbot_framework/main) - 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! - [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! - [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 () 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 . - [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 ().,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 ",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 > Trading & Backtesting,2023-10-13,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.,True,False,jmrichardson/tuneta AutoTrader,Python > Trading & Backtesting,2025-05-04,https://github.com/kieran-mackle/AutoTrader,A Python-based development platform for automated trading systems - from backtesting to optimization to livetrading.,True,False,kieran-mackle/AutoTrader fast-trade,Python > Trading & Backtesting,2025-02-21,https://github.com/jrmeier/fast-trade,A library built with backtest portability and performance in mind for backtest trading strategies.,True,False,jrmeier/fast-trade qf-lib,Python > Trading & Backtesting,2025-11-17,https://github.com/quarkfin/qf-lib,QF-Lib is a Python library that provides high quality tools for quantitative finance.,True,False,quarkfin/qf-lib tda-api,Python > Trading & Backtesting,2024-06-16,https://github.com/alexgolec/tda-api,"Gather data and trade equities, options, and ETFs via TDAmeritrade.",True,False,alexgolec/tda-api vectorbt,Python > Trading & Backtesting,2026-01-03,https://github.com/polakowo/vectorbt,"Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.",True,False,polakowo/vectorbt Lean,Python > Trading & Backtesting,2026-01-02,https://github.com/QuantConnect/Lean,"Lean Algorithmic Trading Engine by QuantConnect (Python, C#).",True,False,QuantConnect/Lean fast-trade,Python > Trading & Backtesting,2025-02-21,https://github.com/jrmeier/fast-trade,Low code backtesting library utilizing pandas and technical analysis indicators.,True,False,jrmeier/fast-trade pysystemtrade,Python > Trading & Backtesting,2025-11-27,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/).",True,False,robcarver17/pysystemtrade pytrendseries,Python > Trading & Backtesting,2025-02-06,https://github.com/rafa-rod/pytrendseries,"Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.",True,False,rafa-rod/pytrendseries PyLOB,Python > Trading & Backtesting,2023-01-01,https://github.com/DrAshBooth/PyLOB,Fully functioning fast Limit Order Book written in Python.,True,False,DrAshBooth/PyLOB PyBroker,Python > Trading & Backtesting,2025-12-05,https://github.com/edtechre/pybroker,Algorithmic Trading with Machine Learning.,True,False,edtechre/pybroker OctoBot Script,Python > Trading & Backtesting,2025-12-29,https://github.com/Drakkar-Software/OctoBot-Script,A quant framework to create cryptocurrencies strategies - from backtesting to optimization to livetrading.,True,False,Drakkar-Software/OctoBot-Script hftbacktest,Python > Trading & Backtesting,2025-12-23,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.",True,False,nkaz001/hftbacktest vnpy,Python > Trading & Backtesting,2025-12-24,https://github.com/vnpy/vnpy,VeighNa is a Python-based open source quantitative trading system development framework.,True,False,vnpy/vnpy Intelligent Trading Bot,Python > Trading & Backtesting,2025-11-02,https://github.com/asavinov/intelligent-trading-bot,Automatically generating signals and trading based on machine learning and feature engineering,True,False,asavinov/intelligent-trading-bot fastquant,Python > Trading & Backtesting,2023-09-15,https://github.com/enzoampil/fastquant,fastquant allows you to easily backtest investment strategies with as few as 3 lines of python code.,True,False,enzoampil/fastquant nautilus_trader,Python > Trading & Backtesting,2026-01-04,https://github.com/nautechsystems/nautilus_trader,A high-performance algorithmic trading platform and event-driven backtester.,True,False,nautechsystems/nautilus_trader YABTE,Python > Trading & Backtesting,2024-05-11,https://github.com/bsdz/yabte,Yet Another (Python) BackTesting Engine.,True,False,bsdz/yabte Trading Strategy,Python > Trading & Backtesting,2025-12-21,https://github.com/tradingstrategy-ai/getting-started,"TradingStrategy.ai is a market data, backtesting, live trading and investor management framework for decentralised finance",True,False,tradingstrategy-ai/getting-started Hikyuu,Python > Trading & Backtesting,2026-01-04,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.",True,False,fasiondog/hikyuu rust_bt,Python > Trading & Backtesting,2025-12-28,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.",True,False,jensnesten/rust_bt Gunbot Quant,Python > Trading & Backtesting,2025-08-19,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.",True,False,GuntharDeNiro/gunbot-quant StrateQueue,Python > Trading & Backtesting,2025-12-30,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.",True,False,StrateQueue/StrateQueue QuantLibRisks,Python > Risk Analysis,2024-04-04,https://github.com/auto-differentiation/QuantLib-Risks-Py,Fast risks with QuantLib,True,False,auto-differentiation/QuantLib-Risks-Py XAD,Python > Risk Analysis,2024-05-21,https://github.com/auto-differentiation/xad-py,Automatic Differentation (AAD) Library,True,False,auto-differentiation/xad-py pyfolio,Python > Risk Analysis,2020-02-28,https://github.com/quantopian/pyfolio,Portfolio and risk analytics in Python.,True,False,quantopian/pyfolio empyrical,Python > Risk Analysis,2020-10-14,https://github.com/quantopian/empyrical,Common financial risk and performance metrics.,True,False,quantopian/empyrical fecon235,Python > Risk Analysis,2018-12-03,https://github.com/rsvp/fecon235,"Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios.",True,False,rsvp/fecon235 finance,Python > Risk Analysis,,https://pypi.org/project/finance/,Financial Risk Calculations. Optimized for ease of use through class construction and operator overload.,False,False, qfrm,Python > Risk Analysis,,https://pypi.org/project/qfrm/,"Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios.",False,False, visualize-wealth,Python > Risk Analysis,2015-06-10,https://github.com/benjaminmgross/visualize-wealth,Portfolio construction and quantitative analysis.,True,False,benjaminmgross/visualize-wealth VisualPortfolio,Python > Risk Analysis,2017-02-28,https://github.com/wegamekinglc/VisualPortfolio,This tool is used to visualize the performance of a portfolio.,True,False,wegamekinglc/VisualPortfolio universal-portfolios,Python > Risk Analysis,2025-09-11,https://github.com/Marigold/universal-portfolios,Collection of algorithms for online portfolio selection.,True,False,Marigold/universal-portfolios FinQuant,Python > Risk Analysis,2023-09-03,https://github.com/fmilthaler/FinQuant,"A program for financial portfolio management, analysis and optimization.",True,False,fmilthaler/FinQuant Empyrial,Python > Risk Analysis,2025-09-14,https://github.com/ssantoshp/Empyrial,Portfolio's risk and performance analytics and returns predictions.,True,False,ssantoshp/Empyrial risktools,Python > Risk Analysis,2024-12-07,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.,True,False,bbcho/risktools-dev Riskfolio-Lib,Python > Risk Analysis,2026-01-02,https://github.com/dcajasn/Riskfolio-Lib,Portfolio Optimization and Quantitative Strategic Asset Allocation in Python.,True,False,dcajasn/Riskfolio-Lib empyrical-reloaded,Python > Risk Analysis,2025-07-29,https://github.com/stefan-jansen/empyrical-reloaded,Common financial risk and performance metrics. [empyrical](https://github.com/quantopian/empyrical) fork.,True,False,stefan-jansen/empyrical-reloaded pyfolio-reloaded,Python > Risk Analysis,2025-06-02,https://github.com/stefan-jansen/pyfolio-reloaded,Portfolio and risk analytics in Python. [pyfolio](https://github.com/quantopian/pyfolio) fork.,True,False,stefan-jansen/pyfolio-reloaded fortitudo.tech,Python > Risk Analysis,2025-12-18,https://github.com/fortitudo-tech/fortitudo.tech,Conditional Value-at-Risk (CVaR) portfolio optimization and Entropy Pooling views / stress-testing in Python.,True,False,fortitudo-tech/fortitudo.tech quantitative-finance-tools,Python > Risk Analysis,2025-12-13,https://github.com/omichauhan-lgtm/quantitative-finance-tools,Library for portfolio optimization (MVO) and rigorous risk metrics (VaR/CVaR).,True,False,omichauhan-lgtm/quantitative-finance-tools alphalens,Python > Factor Analysis,2020-04-27,https://github.com/quantopian/alphalens,Performance analysis of predictive alpha factors.,True,False,quantopian/alphalens alphalens-reloaded,Python > Factor Analysis,2025-06-02,https://github.com/stefan-jansen/alphalens-reloaded,Performance analysis of predictive (alpha) stock factors.,True,False,stefan-jansen/alphalens-reloaded Spectre,Python > Factor Analysis,2025-04-15,https://github.com/Heerozh/spectre,GPU-accelerated Factors analysis library and Backtester,True,False,Heerozh/spectre Asset News Sentiment Analyzer,Python > Sentiment Analysis,2024-07-27,https://github.com/KVignesh122/AssetNewsSentimentAnalyzer,Sentiment analysis and report generation package for financial assets and securities utilizing GPT models.,True,False,KVignesh122/AssetNewsSentimentAnalyzer Jupyter Quant,Python > Quant Research Environment,2024-06-14,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.",True,False,gnzsnz/jupyter-quant ARCH,Python > Time Series,2025-12-02,https://github.com/bashtage/arch,ARCH models in Python.,True,False,bashtage/arch statsmodels,Python > Time Series,,http://statsmodels.sourceforge.net,"Python module that allows users to explore data, estimate statistical models, and perform statistical tests.",False,False, dynts,Python > Time Series,2016-11-02,https://github.com/quantmind/dynts,Python package for timeseries analysis and manipulation.,True,False,quantmind/dynts PyFlux,Python > Time Series,2018-12-16,https://github.com/RJT1990/pyflux,Python library for timeseries modelling and inference (frequentist and Bayesian) on models.,True,False,RJT1990/pyflux tsfresh,Python > Time Series,2025-11-15,https://github.com/blue-yonder/tsfresh,Automatic extraction of relevant features from time series.,True,False,blue-yonder/tsfresh hasura/quandl-metabase,Python > Time Series,,https://platform.hasura.io/hub/projects/anirudhm/quandl-metabase-time-series,Hasura quickstart to visualize Quandl's timeseries datasets with Metabase.,False,False, Facebook Prophet,Python > Time Series,2025-10-21,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.,True,False,facebook/prophet tsmoothie,Python > Time Series,2023-11-23,https://github.com/cerlymarco/tsmoothie,A python library for time-series smoothing and outlier detection in a vectorized way.,True,False,cerlymarco/tsmoothie pmdarima,Python > Time Series,2025-11-17,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.",True,False,alkaline-ml/pmdarima gluon-ts,Python > Time Series,2025-08-14,https://github.com/awslabs/gluon-ts,vProbabilistic time series modeling in Python.,True,False,awslabs/gluon-ts functime,Python > Time Series,2024-06-15,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.,True,False,functime-org/functime exchange_calendars,Python > Calendars,2025-11-07,https://github.com/gerrymanoim/exchange_calendars,Stock Exchange Trading Calendars.,True,False,gerrymanoim/exchange_calendars bizdays,Python > Calendars,2026-01-04,https://github.com/wilsonfreitas/python-bizdays,Business days calculations and utilities.,True,False,wilsonfreitas/python-bizdays pandas_market_calendars,Python > Calendars,2025-12-28,https://github.com/rsheftel/pandas_market_calendars,Exchange calendars to use with pandas for trading applications.,True,False,rsheftel/pandas_market_calendars yfinance,Python > Data Sources,2025-12-22,https://github.com/ranaroussi/yfinance,Yahoo! Finance market data downloader (+faster Pandas Datareader),True,False,ranaroussi/yfinance defeatbeta-api,Python > Data Sources,2026-01-04,https://github.com/defeat-beta/defeatbeta-api,An open-source alternative to Yahoo Finance's market data APIs with higher reliability.,True,False,defeat-beta/defeatbeta-api findatapy,Python > Data Sources,2026-01-02,https://github.com/cuemacro/findatapy,"Python library to download market data via Bloomberg, Quandl, Yahoo etc.",True,False,cuemacro/findatapy googlefinance,Python > Data Sources,2018-09-23,https://github.com/hongtaocai/googlefinance,Python module to get real-time stock data from Google Finance API.,True,False,hongtaocai/googlefinance yahoo-finance,Python > Data Sources,2021-12-15,https://github.com/lukaszbanasiak/yahoo-finance,Python module to get stock data from Yahoo! Finance.,True,False,lukaszbanasiak/yahoo-finance pandas-datareader,Python > Data Sources,2025-04-03,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.",True,False,pydata/pandas-datareader pandas-finance,Python > Data Sources,2025-03-07,https://github.com/davidastephens/pandas-finance,High level API for access to and analysis of financial data.,True,False,davidastephens/pandas-finance pyhoofinance,Python > Data Sources,2016-10-07,https://github.com/innes213/pyhoofinance,Rapidly queries Yahoo Finance for multiple tickers and returns typed data for analysis.,True,False,innes213/pyhoofinance yfinanceapi,Python > Data Sources,2020-05-26,https://github.com/Karthik005/yfinanceapi,Finance API for Python.,True,False,Karthik005/yfinanceapi yql-finance,Python > Data Sources,2015-08-29,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).",True,False,slawek87/yql-finance ystockquote,Python > Data Sources,2017-03-10,https://github.com/cgoldberg/ystockquote,Retrieve stock quote data from Yahoo Finance.,True,False,cgoldberg/ystockquote wallstreet,Python > Data Sources,2024-03-09,https://github.com/mcdallas/wallstreet,Real time stock and option data.,True,False,mcdallas/wallstreet stock_extractor,Python > Data Sources,2016-09-10,https://github.com/ZachLiuGIS/stock_extractor,General Purpose Stock Extractors from Online Resources.,True,False,ZachLiuGIS/stock_extractor Stockex,Python > Data Sources,2021-09-15,https://github.com/cttn/Stockex,Python wrapper for Yahoo! Finance API.,True,False,cttn/Stockex finsymbols,Python > Data Sources,2017-07-23,https://github.com/skillachie/finsymbols,"Obtains stock symbols and relating information for SP500, AMEX, NYSE, and NASDAQ.",True,False,skillachie/finsymbols FRB,Python > Data Sources,2018-12-22,https://github.com/avelkoski/FRB,Python Client for FRED® API.,True,False,avelkoski/FRB inquisitor,Python > Data Sources,2019-10-10,https://github.com/econdb/inquisitor,Python Interface to Econdb.com API.,True,False,econdb/inquisitor yfi,Python > Data Sources,2016-02-12,https://github.com/nickelkr/yfi,Yahoo! YQL library.,True,False,nickelkr/yfi chinesestockapi,Python > Data Sources,,https://pypi.org/project/chinesestockapi/,Python API to get Chinese stock price.,False,False, exchange,Python > Data Sources,2015-07-07,https://github.com/akarat/exchange,Get current exchange rate.,True,False,akarat/exchange ticks,Python > Data Sources,2016-01-08,https://github.com/jamescnowell/ticks,Simple command line tool to get stock ticker data.,True,False,jamescnowell/ticks pybbg,Python > Data Sources,2015-01-20,https://github.com/bpsmith/pybbg,Python interface to Bloomberg COM APIs.,True,False,bpsmith/pybbg ccy,Python > Data Sources,2025-12-28,https://github.com/lsbardel/ccy,Python module for currencies.,True,False,lsbardel/ccy tushare,Python > Data Sources,,https://pypi.org/project/tushare/,A utility for crawling historical and Real-time Quotes data of China stocks.,False,False, jsm,Python > Data Sources,,https://pypi.org/project/jsm/,Get the japanese stock market data.,False,False, cn_stock_src,Python > Data Sources,2016-02-29,https://github.com/jealous/cn_stock_src,Utility for retrieving basic China stock data from different sources.,True,False,jealous/cn_stock_src coinmarketcap,Python > Data Sources,2023-05-23,https://github.com/barnumbirr/coinmarketcap,Python API for coinmarketcap.,True,False,barnumbirr/coinmarketcap after-hours,Python > Data Sources,2020-06-22,https://github.com/datawrestler/after-hours,Obtain pre market and after hours stock prices for a given symbol.,True,False,datawrestler/after-hours bronto-python,Python > Data Sources,,https://pypi.org/project/bronto-python/,Bronto API Integration for Python.,False,False, pytdx,Python > Data Sources,2020-04-15,https://github.com/rainx/pytdx,Python Interface for retrieving chinese stock realtime quote data from TongDaXin Nodes.,True,False,rainx/pytdx pdblp,Python > Data Sources,2024-12-14,https://github.com/matthewgilbert/pdblp,A simple interface to integrate pandas and the Bloomberg Open API.,True,False,matthewgilbert/pdblp tiingo,Python > Data Sources,2025-06-22,https://github.com/hydrosquall/tiingo-python,"Python interface for daily composite prices/OHLC/Volume + Real-time News Feeds, powered by the Tiingo Data Platform.",True,False,hydrosquall/tiingo-python iexfinance,Python > Data Sources,2021-01-02,https://github.com/addisonlynch/iexfinance,Python Interface for retrieving real-time and historical prices and equities data from The Investor's Exchange.,True,False,addisonlynch/iexfinance pyEX,Python > Data Sources,2024-02-05,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.",True,False,timkpaine/pyEX alpaca-trade-api,Python > Data Sources,2024-01-12,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.,True,False,alpacahq/alpaca-trade-api-python metatrader5,Python > Data Sources,,https://pypi.org/project/MetaTrader5/,API Connector to MetaTrader 5 Terminal,False,False, akshare,Python > Data Sources,2026-01-04,https://github.com/jindaxiang/akshare,"AkShare is an elegant and simple financial data interface library for Python, built for human beings! ",True,False,jindaxiang/akshare yahooquery,Python > Data Sources,2025-05-15,https://github.com/dpguthrie/yahooquery,Python interface for retrieving data through unofficial Yahoo Finance API.,True,False,dpguthrie/yahooquery investpy,Python > Data Sources,2022-10-02,https://github.com/alvarobartt/investpy,Financial Data Extraction from Investing.com with Python! ,True,False,alvarobartt/investpy yliveticker,Python > Data Sources,2021-04-29,https://github.com/yahoofinancelive/yliveticker,Live stream of market data from Yahoo Finance websocket.,True,False,yahoofinancelive/yliveticker bbgbridge,Python > Data Sources,2020-01-07,https://github.com/ran404/bbgbridge,Easy to use Bloomberg Desktop API wrapper for Python.,True,False,ran404/bbgbridge polygon.io,Python > Data Sources,2025-12-29,https://github.com/polygon-io/client-python,A python library for Polygon.io financial data APIs.,True,False,polygon-io/client-python alpha_vantage,Python > Data Sources,2025-07-27,https://github.com/RomelTorres/alpha_vantage,A python wrapper for Alpha Vantage API for financial data.,True,False,RomelTorres/alpha_vantage oilpriceapi,Python > Data Sources,2025-12-27,https://github.com/OilpriceAPI/python-sdk,"Python SDK for real-time oil and commodity prices (WTI, Brent, Urals, natural gas, coal) with OpenBB integration.",True,False,OilpriceAPI/python-sdk FinanceDataReader,Python > Data Sources,2025-12-21,https://github.com/FinanceData/FinanceDataReader,"Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks",True,False,FinanceData/FinanceDataReader pystlouisfed,Python > Data Sources,2024-01-09,https://github.com/TomasKoutek/pystlouisfed,"Python client for Federal Reserve Bank of St. Louis API - FRED, ALFRED, GeoFRED and FRASER.",True,False,TomasKoutek/pystlouisfed python-bcb,Python > Data Sources,2025-04-21,https://github.com/wilsonfreitas/python-bcb,Python interface to Brazilian Central Bank web services.,True,False,wilsonfreitas/python-bcb market-prices,Python > Data Sources,2025-10-02,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).,True,False,maread99/market_prices tardis-python,Python > Data Sources,2024-12-05,https://github.com/tardis-dev/tardis-python,Python interface for Tardis.dev high frequency crypto market data,True,False,tardis-dev/tardis-python lake-api,Python > Data Sources,2025-11-02,https://github.com/crypto-lake/lake-api,Python interface for Crypto Lake high frequency crypto market data,True,False,crypto-lake/lake-api tessa,Python > Data Sources,2025-03-14,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.",True,False,ymyke/tessa pandaSDMX,Python > Data Sources,2023-02-25,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.",True,False,dr-leo/pandaSDMX cif,Python > Data Sources,2022-06-18,https://github.com/LenkaV/CIF,"Python package that include few composite indicators, which summarize multidimensional relationships between individual economic indicators.",True,False,LenkaV/CIF finagg,Python > Data Sources,2025-10-20,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.",True,False,theOGognf/finagg FinanceDatabase,Python > Data Sources,2026-01-04,https://github.com/JerBouma/FinanceDatabase,"This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.",True,False,JerBouma/FinanceDatabase Trading Strategy,Python > Data Sources,,https://github.com/tradingstrategy-ai/trading-strategy/,download price data for decentralised exchanges and lending protocols (DeFi),True,False, datamule-python,Python > Data Sources,2026-01-04,https://github.com/john-friedman/datamule-python,A package to work with SEC data. Incorporates datamule endpoints.,True,False,john-friedman/datamule-python Earnings Feed,Python > Data Sources,,https://earningsfeed.com/api,"Real-time SEC filings, insider trades, and institutional holdings API.",False,False, Financial Data,Python > Data Sources,,https://financialdata.net/,Stock Market and Financial Data API.,False,False, SaxoOpenAPI,Python > Data Sources,,https://www.developer.saxo/,Saxo Bank financial data API.,False,False, fsynth,Python > Data Sources,2025-12-27,https://github.com/welcra/fsynth,Python library for high-fidelity unlimited synthetic financial data generation using Heston Stochastic Volatility and Merton Jump Diffusion.,True,False,welcra/fsynth fedfred,Python > Data Sources,,https://nikhilxsunder.github.io/fedfred/,"FRED & GeoFRED Economic data API with preprocessed dataframe output in pandas/geopandas, polars/polars_st, and dask dataframes/geodataframes.",False,False, edgar-sec,Python > Data Sources,,https://nikhilxsunder.github.io/edgar-sec/,EDGAR Financial data API with preprocessed dataclass outputs.,False,False, xlwings,Python > Excel Integration,,https://www.xlwings.org/,Make Excel fly with Python.,False,False, openpyxl,Python > Excel Integration,,https://openpyxl.readthedocs.io/en/latest/,Read/Write Excel 2007 xlsx/xlsm files.,False,False, xlrd,Python > Excel Integration,2025-06-14,https://github.com/python-excel/xlrd,Library for developers to extract data from Microsoft Excel spreadsheet files.,True,False,python-excel/xlrd xlsxwriter,Python > Excel Integration,,https://xlsxwriter.readthedocs.io/,Write files in the Excel 2007+ XLSX file format.,False,False, xlwt,Python > Excel Integration,2018-09-16,https://github.com/python-excel/xlwt,"Library to create spreadsheet files compatible with MS Excel 97/2000/XP/2003 XLS files, on any platform.",True,False,python-excel/xlwt DataNitro,Python > Excel Integration,,https://datanitro.com/,"DataNitro also offers full-featured Python-Excel integration, including UDFs. Trial downloads are available, but users must purchase a license.",False,False, xlloop,Python > Excel Integration,,http://xlloop.sourceforge.net,XLLoop is an open source framework for implementing Excel user-defined functions (UDFs) on a centralised server (a function server).,False,False, expy,Python > Excel Integration,,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.",False,False, pyxll,Python > Excel Integration,,https://www.pyxll.com,PyXLL is an Excel add-in that enables you to extend Excel using nothing but Python code.,False,False, D-Tale,Python > Visualization,2025-12-10,https://github.com/man-group/dtale,Visualizer for pandas dataframes and xarray datasets.,True,False,man-group/dtale mplfinance,Python > Visualization,2024-04-02,https://github.com/matplotlib/mplfinance,"matplotlib utilities for the visualization, and visual analysis, of financial data.",True,False,matplotlib/mplfinance finplot,Python > Visualization,2025-10-20,https://github.com/highfestiva/finplot,Performant and effortless finance plotting for Python.,True,False,highfestiva/finplot finvizfinance,Python > Visualization,2026-01-03,https://github.com/lit26/finvizfinance,Finviz analysis python library.,True,False,lit26/finvizfinance market-analy,Python > Visualization,2025-10-02,https://github.com/maread99/market_analy,Analysis and interactive charting using [market-prices](https://github.com/maread99/market_prices) and bqplot.,True,False,maread99/market_analy QuantInvestStrats,Python > Visualization,2025-11-22,https://github.com/ArturSepp/QuantInvestStrats,"Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.",True,False,ArturSepp/QuantInvestStrats xts,R > Numerical Libraries & Data Structures,2025-08-04,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.",True,False,joshuaulrich/xts data.table,R > Numerical Libraries & Data Structures,2026-01-03,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.",True,False,Rdatatable/data.table sparseEigen,R > Numerical Libraries & Data Structures,2018-12-22,https://github.com/dppalomar/sparseEigen,Sparse principal component analysis.,True,False,dppalomar/sparseEigen TSdbi,R > Numerical Libraries & Data Structures,,http://tsdbi.r-forge.r-project.org/,Provides a common interface to time series databases.,False,False, tseries,R > Numerical Libraries & Data Structures,,https://cran.r-project.org/web/packages/tseries/index.html,Time Series Analysis and Computational Finance.,False,True, zoo,R > Numerical Libraries & Data Structures,,https://cran.r-project.org/web/packages/zoo/index.html,S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).,False,True, tis,R > Numerical Libraries & Data Structures,,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.",False,True, tfplot,R > Numerical Libraries & Data Structures,,https://cran.r-project.org/web/packages/tfplot/index.html,Utilities for simple manipulation and quick plotting of time series data.,False,True, tframe,R > Numerical Libraries & Data Structures,,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.,False,True, IBrokers,R > Data Sources,,https://cran.r-project.org/web/packages/IBrokers/index.html,Provides native R access to Interactive Brokers Trader Workstation API.,False,True, Rblpapi,R > Data Sources,2025-03-31,https://github.com/Rblp/Rblpapi,An R Interface to 'Bloomberg' is provided via the 'Blp API'.,True,False,Rblp/Rblpapi Quandl,R > Data Sources,,https://www.quandl.com/tools/r,Get Financial Data Directly Into R.,False,False, Rbitcoin,R > Data Sources,2016-10-25,https://github.com/jangorecki/Rbitcoin,"Unified markets API interface (bitstamp, kraken, btce, bitmarket).",True,False,jangorecki/Rbitcoin GetTDData,R > Data Sources,2025-05-19,https://github.com/msperlin/GetTDData,Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto.,True,False,msperlin/GetTDData GetHFData,R > Data Sources,2020-06-30,https://github.com/msperlin/GetHFData,Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site.,True,False,msperlin/GetHFData Reddit WallstreetBets API,R > Data Sources,,https://dashboard.nbshare.io/apps/reddit/api/,Provides daily top 50 stocks from reddit (subreddit) Wallstreetbets and their sentiments via the API.,False,False, td,R > Data Sources,2025-10-04,https://github.com/eddelbuettel/td,Interfaces the 'twelvedata' API for stocks and (digital and standard) currencies.,True,False,eddelbuettel/td rbcb,R > Data Sources,2024-01-23,https://github.com/wilsonfreitas/rbcb,R interface to Brazilian Central Bank web services.,True,False,wilsonfreitas/rbcb rb3,R > Data Sources,2025-11-01,https://github.com/ropensci/rb3,A bunch of downloaders and parsers for data delivered from B3.,True,False,ropensci/rb3 simfinapi,R > Data Sources,2025-08-13,https://github.com/matthiasgomolka/simfinapi,Makes 'SimFin' data () easily accessible in R.,True,False,matthiasgomolka/simfinapi tidyfinance,R > Data Sources,2025-06-18,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",True,False,tidy-finance/r-tidyfinance RQuantLib,R > Financial Instruments and Pricing,2025-09-25,https://github.com/eddelbuettel/rquantlib,RQuantLib connects GNU R with QuantLib.,True,False,eddelbuettel/rquantlib quantmod,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/quantmod/index.html,Quantitative Financial Modelling Framework.,False,True, Rmetrics,R > Financial Instruments and Pricing,,https://www.rmetrics.org,The premier open source software solution for teaching and training quantitative finance.,False,False, fAsianOptions,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fAsianOptions/index.html,EBM and Asian Option Valuation.,False,True, fAssets,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fAssets/index.html,Analysing and Modelling Financial Assets.,False,True, fBasics,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fBasics/index.html,Markets and Basic Statistics.,False,True, fBonds,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fBonds/index.html,Bonds and Interest Rate Models.,False,True, fExoticOptions,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fExoticOptions/index.html,Exotic Option Valuation.,False,True, fOptions,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fOptions/index.html,Pricing and Evaluating Basic Options.,False,True, fPortfolio,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/fPortfolio/index.html,Portfolio Selection and Optimization.,False,True, portfolio,R > Financial Instruments and Pricing,2024-08-19,https://github.com/dgerlanc/portfolio,Analysing equity portfolios.,True,False,dgerlanc/portfolio sparseIndexTracking,R > Financial Instruments and Pricing,2023-05-28,https://github.com/dppalomar/sparseIndexTracking,Portfolio design to track an index.,True,False,dppalomar/sparseIndexTracking covFactorModel,R > Financial Instruments and Pricing,2019-03-25,https://github.com/dppalomar/covFactorModel,Covariance matrix estimation via factor models.,True,False,dppalomar/covFactorModel riskParityPortfolio,R > Financial Instruments and Pricing,2022-11-15,https://github.com/dppalomar/riskParityPortfolio,Blazingly fast design of risk parity portfolios.,True,False,dppalomar/riskParityPortfolio sde,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/sde/index.html,Simulation and Inference for Stochastic Differential Equations.,False,True, YieldCurve,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/YieldCurve/index.html,Modelling and estimation of the yield curve.,False,True, SmithWilsonYieldCurve,R > Financial Instruments and Pricing,,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.,False,True, ycinterextra,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/ycinterextra/index.html,Yield curve or zero-coupon prices interpolation and extrapolation.,False,True, AmericanCallOpt,R > Financial Instruments and Pricing,,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.,False,True, VarSwapPrice,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/VarSwapPrice/index.html,Pricing a variance swap on an equity index.,False,True, RND,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/RND/index.html,Risk Neutral Density Extraction Package.,False,True, LSMonteCarlo,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/LSMonteCarlo/index.html,American options pricing with Least Squares Monte Carlo method.,False,True, OptHedging,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/OptHedging/index.html,Estimation of value and hedging strategy of call and put options.,False,True, tvm,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/tvm/index.html,Time Value of Money Functions.,False,True, OptionPricing,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/OptionPricing/index.html,Option Pricing with Efficient Simulation Algorithms.,False,True, credule,R > Financial Instruments and Pricing,2015-08-05,https://github.com/blenezet/credule,Credit Default Swap Functions.,True,False,blenezet/credule derivmkts,R > Financial Instruments and Pricing,,https://cran.r-project.org/web/packages/derivmkts/index.html,Functions and R Code to Accompany Derivatives Markets.,False,True, FinCal,R > Financial Instruments and Pricing,2025-10-30,https://github.com/felixfan/FinCal,"Package for time value of money calculation, time series analysis and computational finance.",True,False,felixfan/FinCal r-quant,R > Financial Instruments and Pricing,2014-02-19,https://github.com/artyyouth/r-quant,R code for quantitative analysis in finance.,True,False,artyyouth/r-quant options.studies,R > Financial Instruments and Pricing,2015-12-17,https://github.com/taylorizing/options.studies,options trading studies functions for use with options.data package and shiny.,True,False,taylorizing/options.studies PortfolioAnalytics,R > Financial Instruments and Pricing,2025-05-11,https://github.com/braverock/PortfolioAnalytics,"Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios.",True,False,braverock/PortfolioAnalytics fmbasics,R > Financial Instruments and Pricing,2019-12-03,https://github.com/imanuelcostigan/fmbasics,Financial Market Building Blocks.,True,False,imanuelcostigan/fmbasics R-fixedincome,R > Financial Instruments and Pricing,2025-05-10,https://github.com/wilsonfreitas/R-fixedincome,Fixed income tools for R.,True,False,wilsonfreitas/R-fixedincome backtest,R > Trading,,https://cran.r-project.org/web/packages/backtest/index.html,Exploring Portfolio-Based Conjectures About Financial Instruments.,False,True, pa,R > Trading,,https://cran.r-project.org/web/packages/pa/index.html,Performance Attribution for Equity Portfolios.,False,True, TTR,R > Trading,2025-05-13,https://github.com/joshuaulrich/TTR,Technical Trading Rules.,True,False,joshuaulrich/TTR QuantTools,R > Trading,,https://quanttools.bitbucket.io/_site/index.html,Enhanced Quantitative Trading Modelling.,False,False, blotter,R > Trading,2024-12-13,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.",True,False,braverock/blotter quantstrat,R > Backtesting,2023-09-14,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.,True,False,braverock/quantstrat PerformanceAnalytics,R > Risk Analysis,2025-08-21,https://github.com/braverock/PerformanceAnalytics,Econometric tools for performance and risk analysis.,True,False,braverock/PerformanceAnalytics FactorAnalytics,R > Factor Analysis,2024-12-12,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.",True,False,braverock/FactorAnalytics Expected Returns,R > Factor Analysis,2025-08-12,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.",True,False,JustinMShea/ExpectedReturns tseries,R > Time Series,,https://cran.r-project.org/web/packages/tseries/index.html,Time Series Analysis and Computational Finance.,False,True, fGarch,R > Time Series,,https://cran.r-project.org/web/packages/fGarch/index.html,Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.,False,True, timeSeries,R > Time Series,,https://cran.r-project.org/web/packages/timeSeries/index.html,Rmetrics - Financial Time Series Objects.,False,True, rugarch,R > Time Series,2025-06-16,https://github.com/alexiosg/rugarch,Univariate GARCH Models.,True,False,alexiosg/rugarch rmgarch,R > Time Series,2025-08-31,https://github.com/alexiosg/rmgarch,Multivariate GARCH Models.,True,False,alexiosg/rmgarch tidypredict,R > Time Series,2021-09-28,https://github.com/edgararuiz/tidypredict,Run predictions inside the database .,True,False,edgararuiz/tidypredict tidyquant,R > Time Series,2025-08-28,https://github.com/business-science/tidyquant,Bringing financial analysis to the tidyverse.,True,False,business-science/tidyquant timetk,R > Time Series,2025-08-29,https://github.com/business-science/timetk,A toolkit for working with time series in R.,True,False,business-science/timetk tibbletime,R > Time Series,2024-12-03,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.",True,False,business-science/tibbletime matrixprofile,R > Time Series,2022-11-25,https://github.com/matrix-profile-foundation/matrixprofile,Time series data mining library built on top of the novel Matrix Profile data structure and algorithms.,True,False,matrix-profile-foundation/matrixprofile garchmodels,R > Time Series,2022-08-11,https://github.com/AlbertoAlmuinha/garchmodels,A parsnip backend for GARCH models.,True,False,AlbertoAlmuinha/garchmodels timeDate,R > Calendars,,https://cran.r-project.org/web/packages/timeDate/index.html,Chronological and Calendar Objects,False,True, bizdays,R > Calendars,2025-01-08,https://github.com/wilsonfreitas/R-bizdays,Business days calculations and utilities,True,False,wilsonfreitas/R-bizdays RunMat,Matlab > Alternatives,,https://runmat.org,"High performance, Open Source, MATLAB syntax runtime.",False,False, QUANTAXIS,Matlab > FrameWorks,2025-10-26,https://github.com/yutiansut/quantaxis,Integrated Quantitative Toolbox with Matlab.,True,False,yutiansut/quantaxis PROJ_Option_Pricing_Matlab,Matlab > FrameWorks,2024-11-19,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",True,False,jkirkby3/PROJ_Option_Pricing_Matlab CcyConv,Julia,2025-10-14,https://github.com/bhftbootcamp/CcyConv.jl,Currency conversion library for Julia,True,False,bhftbootcamp/CcyConv.jl CryptoExchangeAPIs.jl,Julia,2025-11-27,https://github.com/bhftbootcamp/CryptoExchangeAPIs.jl,A Julia library for cryptocurrency exchange APIs,True,False,bhftbootcamp/CryptoExchangeAPIs.jl Fastback.jl,Julia,2025-10-04,https://github.com/rbeeli/Fastback.jl,Blazing fast Julia backtester.,True,False,rbeeli/Fastback.jl Lucky.jl,Julia,2025-12-15,https://github.com/oliviermilla/Lucky.jl,"Modular, asynchronous trading engine in pure Julia.",True,False,oliviermilla/Lucky.jl QuantLib.jl,Julia,2020-02-18,https://github.com/pazzo83/QuantLib.jl,Quantlib implementation in pure Julia.,True,False,pazzo83/QuantLib.jl Ito.jl,Julia,2017-03-21,https://github.com/aviks/Ito.jl,A Julia package for quantitative finance.,True,False,aviks/Ito.jl LightweightCharts.jl,Julia,2025-10-22,https://github.com/bhftbootcamp/LightweightCharts.jl,Julia wrapper for Lightweight Charts™ by TradingView.,True,False,bhftbootcamp/LightweightCharts.jl TALib.jl,Julia,2017-08-22,https://github.com/femtotrader/TALib.jl,A Julia wrapper for TA-Lib.,True,False,femtotrader/TALib.jl Miletus.jl,Julia,2023-12-07,https://github.com/JuliaComputing/Miletus.jl,"A financial contract definition, modeling language, and valuation framework.",True,False,JuliaComputing/Miletus.jl Temporal.jl,Julia,2021-12-28,https://github.com/dysonance/Temporal.jl,Flexible and efficient time series class & methods.,True,False,dysonance/Temporal.jl Indicators.jl,Julia,2022-12-06,https://github.com/dysonance/Indicators.jl,Financial market technical analysis & indicators on top of Temporal.,True,False,dysonance/Indicators.jl Strategems.jl,Julia,2021-04-06,https://github.com/dysonance/Strategems.jl,Quantitative systematic trading strategy development and backtesting.,True,False,dysonance/Strategems.jl TimeSeries.jl,Julia,2025-12-31,https://github.com/JuliaStats/TimeSeries.jl,Time series toolkit for Julia.,True,False,JuliaStats/TimeSeries.jl TechnicalIndicatorCharts.jl,Julia,2025-11-29,https://github.com/g-gundam/TechnicalIndicatorCharts.jl,Visualize OnlineTechnicalIndicators.jl using LightweightCharts.jl.,True,False,g-gundam/TechnicalIndicatorCharts.jl MarketTechnicals.jl,Julia,2021-07-12,https://github.com/JuliaQuant/MarketTechnicals.jl,Technical analysis of financial time series on top of TimeSeries.,True,False,JuliaQuant/MarketTechnicals.jl MarketData.jl,Julia,2025-11-10,https://github.com/JuliaQuant/MarketData.jl,Time series market data.,True,False,JuliaQuant/MarketData.jl OnlineTechnicalIndicators.jl,Julia,2026-01-04,https://github.com/femtotrader/OnlineTechnicalIndicators.jl,Julia Technical Analysis Indicators via online algorithms.,True,False,femtotrader/OnlineTechnicalIndicators.jl OnlineTechnicalIndicators,Julia,2026-01-03,https://github.com/femtotrader/OnlinePortfolioAnalytics.jl,A Julia quantitative portfolio analytics (risk / performance) via online algorithms.,True,False,femtotrader/OnlinePortfolioAnalytics.jl OnlineResamplers.jl,Julia,2026-01-01,https://github.com/femtotrader/OnlineResamplers.jl,High-performance Julia package for real-time resampling of financial market data.,True,False,femtotrader/OnlineResamplers.jl RiskPerf.jl,Julia,2025-10-01,https://github.com/rbeeli/RiskPerf.jl,Quantitative risk and performance analysis package for financial time series powered by the Julia language.,True,False,rbeeli/RiskPerf.jl TimeFrames.jl,Julia,2025-11-27,https://github.com/femtotrader/TimeFrames.jl,A Julia library that defines TimeFrame (essentially for resampling TimeSeries).,True,False,femtotrader/TimeFrames.jl DataFrames.jl,Julia,2025-12-08,https://github.com/JuliaData/DataFrames.jl,In-memory tabular data in Julia,True,False,JuliaData/DataFrames.jl TSFrames.jl,Julia,2024-06-18,https://github.com/xKDR/TSFrames.jl,Handle timeseries data on top of the powerful and mature DataFrames.jl,True,False,xKDR/TSFrames.jl TimeArrays.jl,Julia,2025-10-15,https://github.com/bhftbootcamp/TimeArrays.jl,Time series handling for Julia,True,False,bhftbootcamp/TimeArrays.jl Strata,Java,,http://strata.opengamma.io/,Modern open-source analytics and market risk library designed and written in Java.,False,False, JQuantLib,Java,2016-02-26,https://github.com/frgomes/jquantlib,"JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java.",True,False,frgomes/jquantlib finmath.net,Java,,http://finmath.net,Java library with algorithms and methodologies related to mathematical finance.,False,False, quantcomponents,Java,2015-10-07,https://github.com/lsgro/quantcomponents,Free Java components for Quantitative Finance and Algorithmic Trading.,True,False,lsgro/quantcomponents DRIP,Java,,https://lakshmidrip.github.io/DRIP,"Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries.",False,False, ta4j,Java,2025-12-30,https://github.com/ta4j/ta4j,A Java library for technical analysis.,True,False,ta4j/ta4j finance.js,JavaScript,2018-10-11,https://github.com/ebradyjobory/finance.js,A JavaScript library for common financial calculations.,True,False,ebradyjobory/finance.js portfolio-allocation,JavaScript,2022-08-11,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...",True,False,lequant40/portfolio_allocation_js Ghostfolio,JavaScript,2026-01-04,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.",True,False,ghostfolio/ghostfolio IndicatorTS,JavaScript,2025-02-26,https://github.com/cinar/indicatorts,"Indicator is a TypeScript module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.",True,False,cinar/indicatorts chart-patterns,JavaScript,error,https://github.com/focus1691/chart-patterns,"Technical analysis library for Market Profile, Volume Profile, Stacked Imbalances and High Volume Node indicators.",True,False,focus1691/chart-patterns orderflow,JavaScript,2025-03-31,https://github.com/focus1691/orderflow,Orderflow trade aggregator for building Footprint Candles from exchange websocket data.,True,False,focus1691/orderflow ccxt,JavaScript,2026-01-02,https://github.com/ccxt/ccxt,A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges.,True,False,ccxt/ccxt PENDAX,JavaScript,2024-05-09,https://github.com/CompendiumFi/PENDAX-SDK,"Javascript SDK for Trading/Data API and Websockets for FTX, FTXUS, OKX, Bybit, & More.",True,False,CompendiumFi/PENDAX-SDK QUANTAXIS_Webkit,JavaScript > Data Visualization,2017-07-30,https://github.com/yutiansut/QUANTAXIS_Webkit,An awesome visualization center based on quantaxis.,True,False,yutiansut/QUANTAXIS_Webkit quantfin,Haskell,2019-04-06,https://github.com/boundedvariation/quantfin,quant finance in pure haskell.,True,False,boundedvariation/quantfin Haxcel,Haskell,2022-09-13,https://github.com/MarcusRainbow/Haxcel,Excel Addin for Haskell.,True,False,MarcusRainbow/Haxcel Ffinar,Haskell,2021-11-26,https://github.com/MarcusRainbow/Ffinar,A financial maths library in Haskell.,True,False,MarcusRainbow/Ffinar QuantScale,Scala,2014-01-14,https://github.com/choucrifahed/quantscale,Scala Quantitative Finance Library.,True,False,choucrifahed/quantscale Scala Quant,Scala,2017-05-06,https://github.com/frankcash/Scala-Quant,Scala library for working with stock data from IFTTT recipes or Google Finance.,True,False,frankcash/Scala-Quant Jiji,Ruby,2019-01-22,https://github.com/unageanu/jiji2,Open Source Forex algorithmic trading framework using OANDA REST API.,True,False,unageanu/jiji2 Tai,Elixir/Erlang,2024-12-06,https://github.com/fremantle-capital/tai,"Open Source composable, real time, market data and trade execution toolkit.",True,False,fremantle-capital/tai Workbench,Elixir/Erlang,2022-06-06,https://github.com/fremantle-industries/workbench,From Idea to Execution - Manage your trading operation across a globally distributed cluster,True,False,fremantle-industries/workbench Prop,Elixir/Erlang,2022-06-06,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.",True,False,fremantle-industries/prop Kelp,Golang,2021-11-26,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).,True,False,stellar/kelp marketstore,Golang,error,https://github.com/alpacahq/marketstore,DataFrame Server for Financial Timeseries Data.,True,False,alpacahq/marketstore IndicatorGo,Golang,2025-09-27,https://github.com/cinar/indicator,"IndicatorGo is a Golang module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.",True,False,cinar/indicator QuantLib,CPP,2026-01-03,https://github.com/lballabio/QuantLib,The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.,True,False,lballabio/QuantLib QuantLibRisks,CPP,2025-09-28,https://github.com/auto-differentiation/QuantLib-Risks-Cpp,Fast risks with QuantLib in C++,True,False,auto-differentiation/QuantLib-Risks-Cpp XAD,CPP,2025-12-31,https://github.com/auto-differentiation/xad,Automatic Differentation (AAD) Library,True,False,auto-differentiation/xad TradeFrame,CPP,2026-01-04,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.,True,False,rburkholder/trade-frame Hikyuu,CPP,2026-01-04,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.",True,False,fasiondog/hikyuu QuantLib,Frameworks,2026-01-03,https://github.com/lballabio/QuantLib,The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.,True,False,lballabio/QuantLib JQuantLib,Frameworks,2016-02-26,https://github.com/frgomes/jquantlib,Java port.,True,False,frgomes/jquantlib RQuantLib,Frameworks,2025-09-25,https://github.com/eddelbuettel/rquantlib,R port.,True,False,eddelbuettel/rquantlib QuantLibAddin,Frameworks,,https://www.quantlib.org/quantlibaddin/,Excel support.,False,False, QuantLibXL,Frameworks,,https://www.quantlib.org/quantlibxl/,Excel support.,False,False, QLNet,Frameworks,2025-12-23,https://github.com/amaggiulli/qlnet,.Net port.,True,False,amaggiulli/qlnet PyQL,Frameworks,2025-08-20,https://github.com/enthought/pyql,Python port.,True,False,enthought/pyql QuantLib.jl,Frameworks,2020-02-18,https://github.com/pazzo83/QuantLib.jl,Julia port.,True,False,pazzo83/QuantLib.jl QuantLib-Python Documentation,Frameworks,,https://quantlib-python-docs.readthedocs.io/,Documentation for the Python bindings for the QuantLib library,False,False, TA-Lib,Frameworks,,https://ta-lib.org,perform technical analysis of financial market data.,False,False, Portfolio Optimizer,Frameworks,,https://portfoliooptimizer.io/,Portfolio Optimizer is a Web API for portfolio analysis and optimization.,False,False, QuantConnect,CSharp,2026-01-02,https://github.com/QuantConnect/Lean,Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage.,True,False,QuantConnect/Lean StockSharp,CSharp,2026-01-02,https://github.com/StockSharp/StockSharp,"Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).",True,False,StockSharp/StockSharp TDAmeritrade.DotNetCore,CSharp,2023-03-10,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.",True,False,NVentimiglia/TDAmeritrade.DotNetCore QuantMath,Rust,2020-05-28,https://github.com/MarcusRainbow/QuantMath,Financial maths library for risk-neutral pricing and risk,True,False,MarcusRainbow/QuantMath Barter,Rust,2025-10-17,https://github.com/barter-rs/barter-rs,Open-source Rust framework for building event-driven live-trading & backtesting systems,True,False,barter-rs/barter-rs LFEST,Rust,2025-10-23,https://github.com/MathisWellmann/lfest-rs,Simulated perpetual futures exchange to trade your strategy against.,True,False,MathisWellmann/lfest-rs TradeAggregation,Rust,2025-07-08,https://github.com/MathisWellmann/trade_aggregation-rs,Aggregate trades into user-defined candles using information driven rules.,True,False,MathisWellmann/trade_aggregation-rs SlidingFeatures,Rust,2025-08-24,https://github.com/MathisWellmann/sliding_features-rs,Chainable tree-like sliding windows for signal processing and technical analysis.,True,False,MathisWellmann/sliding_features-rs RustQuant,Rust,2025-09-01,https://github.com/avhz/RustQuant,Quantitative finance library written in Rust.,True,False,avhz/RustQuant finalytics,Rust,2025-10-23,https://github.com/Nnamdi-sys/finalytics,A rust library for financial data analysis.,True,False,Nnamdi-sys/finalytics RunMat,Rust,2025-12-30,https://github.com/runmat-org/runmat,Rust runtime for MATLAB-syntax array math with automatic CPU/GPU execution and fused kernels for quant simulations.,True,False,runmat-org/runmat Auto-Differentiation Website,"Reproducing Works, Training & Books",,https://auto-differentiation.github.io/,Background and resources on Automatic Differentiation (AD) / Adjoint Algorithmic Differentitation (AAD).,False,False, Derman Papers,"Reproducing Works, Training & Books",2017-10-21,https://github.com/MarcosCarreira/DermanPapers,Notebooks that replicate original quantitative finance papers from Emanuel Derman.,True,False,MarcosCarreira/DermanPapers ML-Quant,"Reproducing Works, Training & Books",,https://www.ml-quant.com/,"Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.",False,False, volatility-trading,"Reproducing Works, Training & Books",2024-10-21,https://github.com/jasonstrimpel/volatility-trading,A complete set of volatility estimators based on Euan Sinclair's Volatility Trading.,True,False,jasonstrimpel/volatility-trading quant,"Reproducing Works, Training & Books",2015-07-14,https://github.com/paulperry/quant,"Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas.",True,False,paulperry/quant fecon235,"Reproducing Works, Training & Books",2018-12-03,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.,True,False,rsvp/fecon235 Quantitative-Notebooks,"Reproducing Works, Training & Books",2020-07-02,https://github.com/LongOnly/Quantitative-Notebooks,"Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy",True,False,LongOnly/Quantitative-Notebooks QuantEcon,"Reproducing Works, Training & Books",,https://quantecon.org/,"Lecture series on economics, finance, econometrics and data science; QuantEcon.py, QuantEcon.jl, notebooks",False,False, FinanceHub,"Reproducing Works, Training & Books",2021-05-25,https://github.com/Finance-Hub/FinanceHub,Resources for Quantitative Finance,True,False,Finance-Hub/FinanceHub Python_Option_Pricing,"Reproducing Works, Training & Books",2025-05-13,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.",True,False,dedwards25/Python_Option_Pricing python-training,"Reproducing Works, Training & Books",2023-11-27,https://github.com/jpmorganchase/python-training,J.P. Morgan's Python training for business analysts and traders.,True,False,jpmorganchase/python-training Stock_Analysis_For_Quant,"Reproducing Works, Training & Books",2025-05-04,https://github.com/LastAncientOne/Stock_Analysis_For_Quant,"Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau.",True,False,LastAncientOne/Stock_Analysis_For_Quant algorithmic-trading-with-python,"Reproducing Works, Training & Books",2021-06-01,https://github.com/chrisconlan/algorithmic-trading-with-python,Source code for Algorithmic Trading with Python (2020) by Chris Conlan.,True,False,chrisconlan/algorithmic-trading-with-python MEDIUM_NoteBook,"Reproducing Works, Training & Books",2024-09-22,https://github.com/cerlymarco/MEDIUM_NoteBook,Repository containing notebooks of [cerlymarco](https://github.com/cerlymarco)'s posts on Medium.,True,False,cerlymarco/MEDIUM_NoteBook QuantFinance,"Reproducing Works, Training & Books",2025-09-02,https://github.com/PythonCharmers/QuantFinance,Training materials in quantitative finance.,True,False,PythonCharmers/QuantFinance IPythonScripts,"Reproducing Works, Training & Books",2018-11-18,https://github.com/mgroncki/IPythonScripts,"Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.",True,False,mgroncki/IPythonScripts Computational-Finance-Course,"Reproducing Works, Training & Books",2024-03-01,https://github.com/LechGrzelak/Computational-Finance-Course,Materials for the course of Computational Finance.,True,False,LechGrzelak/Computational-Finance-Course Machine-Learning-for-Asset-Managers,"Reproducing Works, Training & Books",2025-01-29,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.",True,False,emoen/Machine-Learning-for-Asset-Managers Python-for-Finance-Cookbook,"Reproducing Works, Training & Books",2025-12-15,https://github.com/PacktPublishing/Python-for-Finance-Cookbook,"Python for Finance Cookbook, published by Packt.",True,False,PacktPublishing/Python-for-Finance-Cookbook modelos_vol_derivativos,"Reproducing Works, Training & Books",2023-08-19,https://github.com/ysaporito/modelos_vol_derivativos,"""Modelos de Volatilidade para Derivativos"" book's Jupyter notebooks",True,False,ysaporito/modelos_vol_derivativos NMOF,"Reproducing Works, Training & Books",2025-10-27,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).",True,False,enricoschumann/NMOF py4fi2nd,"Reproducing Works, Training & Books",2025-06-06,https://github.com/yhilpisch/py4fi2nd,"Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.",True,False,yhilpisch/py4fi2nd aiif,"Reproducing Works, Training & Books",2023-10-09,https://github.com/yhilpisch/aiif,Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.,True,False,yhilpisch/aiif py4at,"Reproducing Works, Training & Books",2023-10-09,https://github.com/yhilpisch/py4at,Jupyter Notebooks and code for the book Python for Algorithmic Trading (O'Reilly) by Yves Hilpisch.,True,False,yhilpisch/py4at dawp,"Reproducing Works, Training & Books",2021-02-22,https://github.com/yhilpisch/dawp,Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.,True,False,yhilpisch/dawp dx,"Reproducing Works, Training & Books",2025-04-05,https://github.com/yhilpisch/dx,DX Analytics | Financial and Derivatives Analytics with Python.,True,False,yhilpisch/dx QuantFinanceBook,"Reproducing Works, Training & Books",2025-04-14,https://github.com/LechGrzelak/QuantFinanceBook,Quantitative Finance book.,True,False,LechGrzelak/QuantFinanceBook rough_bergomi,"Reproducing Works, Training & Books",2018-09-17,https://github.com/ryanmccrickerd/rough_bergomi,A Python implementation of the rough Bergomi model.,True,False,ryanmccrickerd/rough_bergomi frh-fx,"Reproducing Works, Training & Books",2018-05-24,https://github.com/ryanmccrickerd/frh-fx,A python implementation of the fast-reversion Heston model of Mechkov for FX purposes.,True,False,ryanmccrickerd/frh-fx Value Investing Studies,"Reproducing Works, Training & Books",2021-10-26,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.,True,False,euclidjda/value-investing-studies Machine Learning Asset Management,"Reproducing Works, Training & Books",2021-12-17,https://github.com/firmai/machine-learning-asset-management,Machine Learning in Asset Management (by @firmai).,True,False,firmai/machine-learning-asset-management Deep Learning Machine Learning Stock,"Reproducing Works, Training & Books",2024-03-01,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.,True,False,LastAncientOne/Deep-Learning-Machine-Learning-Stock Technical Analysis and Feature Engineering,"Reproducing Works, Training & Books",2024-02-16,https://github.com/jo-cho/Technical_Analysis_and_Feature_Engineering,Feature Engineering and Feature Importance of Machine Learning in Financial Market.,True,False,jo-cho/Technical_Analysis_and_Feature_Engineering Differential Machine Learning and Axes that matter by Brian Huge and Antoine Savine,"Reproducing Works, Training & Books",2022-10-05,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.",True,False,differential-machine-learning/notebooks systematictradingexamples,"Reproducing Works, Training & Books",2020-07-22,https://github.com/robcarver17/systematictradingexamples,Examples of code related to book [Systematic Trading](www.systematictrading.org) and [blog](http://qoppac.blogspot.com),True,False,robcarver17/systematictradingexamples pysystemtrade_examples,"Reproducing Works, Training & Books",2018-02-21,https://github.com/robcarver17/pysystemtrade_examples,Examples using pysystemtrade for Robert Carver's [blog](http://qoppac.blogspot.com).,True,False,robcarver17/pysystemtrade_examples ML_Finance_Codes,"Reproducing Works, Training & Books",2020-06-13,https://github.com/mfrdixon/ML_Finance_Codes,Machine Learning in Finance: From Theory to Practice Book,True,False,mfrdixon/ML_Finance_Codes Hands-On Machine Learning for Algorithmic Trading,"Reproducing Works, Training & Books",2023-01-18,https://github.com/packtpublishing/hands-on-machine-learning-for-algorithmic-trading,"Hands-On Machine Learning for Algorithmic Trading, published by Packt",True,False,packtpublishing/hands-on-machine-learning-for-algorithmic-trading financialnoob-misc,"Reproducing Works, Training & Books",2024-08-26,https://github.com/financialnoob/misc,Codes from @financialnoob's posts,True,False,financialnoob/misc MesoSim Options Trading Strategy Library,"Reproducing Works, Training & Books",2024-04-06,https://github.com/deltaray-io/strategy-library,Free and public Options Trading strategy library for MesoSim. ,True,False,deltaray-io/strategy-library Quant-Finance-With-Python-Code,"Reproducing Works, Training & Books",2023-11-16,https://github.com/lingyixu/Quant-Finance-With-Python-Code,Repo for code examples in Quantitative Finance with Python by Chris Kelliher,True,False,lingyixu/Quant-Finance-With-Python-Code QuantFinanceTraining,"Reproducing Works, Training & Books",2024-02-20,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.",True,False,JoaoJungblut/QuantFinanceTraining Statistical-Learning-based-Portfolio-Optimization,"Reproducing Works, Training & Books",error,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).",True,False,YannickKae/Statistical-Learning-based-Portfolio-Optimization book_irds3,"Reproducing Works, Training & Books",2022-10-29,https://github.com/attack68/book_irds3,Code repository for Pricing and Trading Interest Rate Derivatives.,True,False,attack68/book_irds3 Autoencoder-Asset-Pricing-Models,"Reproducing Works, Training & Books",2025-08-17,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)).",True,False,RichardS0268/Autoencoder-Asset-Pricing-Models Finance,"Reproducing Works, Training & Books",2025-05-12,https://github.com/shashankvemuri/Finance,"150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.",True,False,shashankvemuri/Finance 101_formulaic_alphas,"Reproducing Works, Training & Books",2022-07-11,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.,True,False,ram-ki/101_formulaic_alphas Tidy Finance,"Reproducing Works, Training & Books",,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.",False,False, RoughVolatilityWorkshop,"Reproducing Works, Training & Books",2025-09-06,https://github.com/jgatheral/RoughVolatilityWorkshop,2024 QuantMind's Rough Volatility Workshop lectures.,True,False,jgatheral/RoughVolatilityWorkshop AFML,"Reproducing Works, Training & Books",2024-09-05,https://github.com/boyboi86/AFML,All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.,True,False,boyboi86/AFML AlgoTradingLib,"Reproducing Works, Training & Books",2025-12-27,https://github.com/usdaud/algotradinglib.github.io,"A catalog of algorithmic trading libraries, frameworks, strategies, and educational materials.",True,False,usdaud/algotradinglib.github.io ================================================ FILE: site/projects.qmd ================================================ --- title: "Projects" format: html: df-print: kable include-in-header: - text: | --- Compilation of projects providing access to the date of last commit or publication date. ```{r message=FALSE, warning=FALSE, echo=FALSE, table.cap="Projects"} #| label: tbl-projects #| tbl-cap: Projects #| results: asis df <- read.csv("projects.csv") df$last_commit <- as.Date(df$last_commit) df <- df[!is.na(df$last_commit),] df$project <- paste0("", df$project, "") df <- df[,c("project", "section", "last_commit")] df <- df[order(df$last_commit, decreasing = TRUE),] rownames(df) <- NULL htmltools::tagList(DT::datatable(df, list(pageLength = 50), escape = FALSE)) ``` ================================================ FILE: styles.css ================================================ /* css styles */ ================================================ FILE: topic.py ================================================ import os from github import Github # using an access token g = Github(os.environ['GITHUB_ACCESS_TOKEN']) # ts = g.search_topics('trading') # for t in ts: # print(t) # t = ts[0] # print(t) # print(t.name) # print(t.updated_at) # print(t.score) topic = 'quant' repos = g.search_repositories(query=f'topic:{topic}') for repo in repos: if repo.stargazers_count < 1000: break print(repo.name, repo.stargazers_count, repo.language, repo.html_url, repo.description, repo.updated_at, repo.archived)