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Zhe Wang
@@ -99,69 +99,69 @@ And I count crypto as whole new category: [>> Click ME to Systematic Crypto](cry
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Note: the one marked as `Live Trading` has reasonable live trading support for at least 1 broker. Otherwise, backtest
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function only.
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- [aat](https://github.com/AsyncAlgoTrading/aat) | `Python`, `C++`, `Live Trading`| - an asynchronous, event-driven framework for writing algorithmic trading strategies in python with optional acceleration in C++. It is designed to be modular and extensible, with support for a wide variety of instruments and strategies, live trading across (and between) multiple exchanges.
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- [* barter-rs](https://github.com/barter-rs/barter-rs) | `Rust` | - Open-source Rust framework for building event-driven live-trading & backtesting systems. Algorithmic trade with the peace of mind that comes from knowing your strategies have been backtested with a near-identical trading Engine.
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- [* bt](https://github.com/pmorissette/bt) | `Python` | - Flexible backtesting for Python based on Algo and Strategy Tree
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- [Better Quant](https://github.com/byrnexu/betterquant) | `C++`, `Live Trading` | - Better quant today, best quant tomorrow. 💪
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- [Botvana](https://github.com/featherenvy/botvana) | `Rust` | - high-performance and event-driven trading system built using Rust
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- [backtrader](https://github.com/mementum/backtrader) | `Python`, `Live Trading` | - Event driven Python Backtesting library for trading strategies
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- [backtesting.py](https://github.com/kernc/backtesting.py) | `Python` | - Backtesting.py is a Python framework for inferring viability of trading strategies on historical (past) data. Improved upon the vision of Backtrader, and by all means surpassingly comparable to other accessible alternatives, Backtesting.py is lightweight, fast, user-friendly, intuitive, interactive, intelligent and, hopefully, future-proof.
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- [FlashFunk](https://github.com/HFQR/FlashFunk) | `Rust` | - High Performance Runtime in Rust
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- [QuantFabric](https://github.com/QuantFabric/QuantFabric)  | `C++` | - QuantFabric是基于Linux/C++开发的中高频量化交易系统，支持中金所、郑商所、大商所、上期所、上海国际能源中心的期货业务品种交易，支持上交所、深交所的股票、债券品种交易。
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- [gobacktest](https://github.com/gobacktest/gobacktest) | `Go` | - A Go implementation of event-driven backtesting framework
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- [Hikyuu](https://github.com/fasiondog/hikyuu) | `C++`, `Python`| - Hikyuu Quant Framework 基于C++/Python的开源量化交易研究框架
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- [Investing Algorithm Framework](https://github.com/coding-kitties/investing-algorithm-framework/tree/main)  | `Python` | - Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.
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- [lumibot](https://github.com/Lumiwealth/lumibot/tree/8da88cadfe9ee35399dd69c94aa5ed3cf995f417) | `Python` | - A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...)
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- [* nautilus_trader](https://github.com/nautechsystems/nautilus_trader) | `Python`, `Cython`, `Rust`, `Live Trading` | - A high-performance algorithmic trading platform and event-driven backtester
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- [PyBroker](https://github.com/edtechre/pybroker)  | `Python` | - Algorithmic Trading in Python with Machine Learning
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- [QuantConnect](https://github.com/QuantConnect/Lean) | `C#`, `.NET`, `Live Trading` | - Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
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- [QUANTAXIS](https://github.com/QUANTAXIS/QUANTAXIS) | `Python`, `Rust`, `Live Trading` | - QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权/港股/虚拟货币 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案
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- [Rqalpha](https://github.com/ricequant/rqalpha) | `Python` | - A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
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- [quanttrader](https://github.com/letianzj/quanttrader) | `Python` | - Backtest and live trading in Python. Event based. Similar to backtesting.py.
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- [qf-lib](https://github.com/quarkfin/qf-lib)  | `Python` | - Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.
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- [sdoosa-algo-trade-python](https://github.com/sreenivasdoosa/sdoosa-algo-trade-python) | `Python` | - This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter.
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- [* vnpy](https://github.com/vnpy/vnpy)  | `Python`, `Stock`, `Futures`, `Crypto`, `Live Trading` | - Python-based open source quantitative trading system development framework, officially released in January 2015, has grown step by step into a full-featured quantitative trading platform
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- [WonderTrader](https://github.com/wondertrader/wondertrader) | `C++`, `Python` | - WonderTrader——量化研发交易一站式框架
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- [zvt](https://github.com/zvtvz/zvt) | `Python`, `Stock`, `Backtest` | - Modular quant framework
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- [zipline](https://github.com/quantopian/zipline) | `Python` | - Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.
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- [PandoraTrader](https://github.com/pegasusTrader/PandoraTrader) | `C++` | - CTP 高频量化交易平台 C++ Trade Platform for quant developer
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- [hftbacktest](https://github.com/nkaz001/hftbacktest)  | `Python`, `numba` | - 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.
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- [Cipher](https://github.com/nanvel/cipher-bt) | `Python` | - Backtesting library with focus on position adjustment that allows testing complicated setups. Pythonic, extensible, well-structured, documented.
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- [aat](https://github.com/AsyncAlgoTrading/aat)   | Python, C++, Live Trading| - an asynchronous, event-driven framework for writing algorithmic trading strategies in python with optional acceleration in C++. It is designed to be modular and extensible, with support for a wide variety of instruments and strategies, live trading across (and between) multiple exchanges.
|
||||
- [* barter-rs](https://github.com/barter-rs/barter-rs)   | Rust | - Open-source Rust framework for building event-driven live-trading & backtesting systems. Algorithmic trade with the peace of mind that comes from knowing your strategies have been backtested with a near-identical trading Engine.
|
||||
- [* bt](https://github.com/pmorissette/bt)   | Python | - Flexible backtesting for Python based on Algo and Strategy Tree
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- [Better Quant](https://github.com/byrnexu/betterquant)   | C++, Live Trading | - Better quant today, best quant tomorrow. 💪
|
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- [Botvana](https://github.com/featherenvy/botvana)   | Rust | - high-performance and event-driven trading system built using Rust
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- [backtrader](https://github.com/mementum/backtrader)   | Python, Live Trading | - Event driven Python Backtesting library for trading strategies
|
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- [backtesting.py](https://github.com/kernc/backtesting.py)   | Python | - Backtesting.py is a Python framework for inferring viability of trading strategies on historical (past) data. Improved upon the vision of Backtrader, and by all means surpassingly comparable to other accessible alternatives, Backtesting.py is lightweight, fast, user-friendly, intuitive, interactive, intelligent and, hopefully, future-proof.
|
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- [FlashFunk](https://github.com/HFQR/FlashFunk)   | Rust | - High Performance Runtime in Rust
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- [QuantFabric](https://github.com/QuantFabric/QuantFabric)   | C++ | - QuantFabric是基于Linux/C++开发的中高频量化交易系统，支持中金所、郑商所、大商所、上期所、上海国际能源中心的期货业务品种交易，支持上交所、深交所的股票、债券品种交易。
|
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- [gobacktest](https://github.com/gobacktest/gobacktest)   | Go | - A Go implementation of event-driven backtesting framework
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- [Hikyuu](https://github.com/fasiondog/hikyuu)   | C++, Python| - Hikyuu Quant Framework 基于C++/Python的开源量化交易研究框架
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- [Investing Algorithm Framework](https://github.com/coding-kitties/investing-algorithm-framework/tree/main)   | Python | - Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.
|
||||
- [lumibot](https://github.com/Lumiwealth/lumibot/tree/8da88cadfe9ee35399dd69c94aa5ed3cf995f417)   | Python | - A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...)
|
||||
- [* nautilus_trader](https://github.com/nautechsystems/nautilus_trader)   | Python, Cython, Rust, Live Trading | - A high-performance algorithmic trading platform and event-driven backtester
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- [PyBroker](https://github.com/edtechre/pybroker)   | Python | - Algorithmic Trading in Python with Machine Learning
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- [QuantConnect](https://github.com/QuantConnect/Lean)   | C#, .NET, Live Trading | - Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
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- [QUANTAXIS](https://github.com/QUANTAXIS/QUANTAXIS)   | Python, Rust, Live Trading | - QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权/港股/虚拟货币 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案
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- [Rqalpha](https://github.com/ricequant/rqalpha)   | Python | - A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
|
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- [quanttrader](https://github.com/letianzj/quanttrader)   | Python | - Backtest and live trading in Python. Event based. Similar to backtesting.py.
|
||||
- [qf-lib](https://github.com/quarkfin/qf-lib)   | Python | - Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.
|
||||
- [sdoosa-algo-trade-python](https://github.com/sreenivasdoosa/sdoosa-algo-trade-python)   | Python | - This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter.
|
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- [* vnpy](https://github.com/vnpy/vnpy)   | Python, Stock, Futures, Crypto, Live Trading | - Python-based open source quantitative trading system development framework, officially released in January 2015, has grown step by step into a full-featured quantitative trading platform
|
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- [WonderTrader](https://github.com/wondertrader/wondertrader)   | C++, Python | - WonderTrader——量化研发交易一站式框架
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- [zvt](https://github.com/zvtvz/zvt)   | Python, Stock, Backtest | - Modular quant framework
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- [zipline](https://github.com/quantopian/zipline)   | Python | - Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.
|
||||
- [PandoraTrader](https://github.com/pegasusTrader/PandoraTrader)   | C++ | - CTP 高频量化交易平台 C++ Trade Platform for quant developer
|
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- [hftbacktest](https://github.com/nkaz001/hftbacktest)   | Python, numba | - 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.
|
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- [Cipher](https://github.com/nanvel/cipher-bt)   | Python | - Backtesting library with focus on position adjustment that allows testing complicated setups. Pythonic, extensible, well-structured, documented.
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> Vector Based Frameworks
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- [FinHack](https://github.com/FinHackCN/finhack)  | `Python` | - 一个易于拓展的量化金融框架，它在当前版本中集成了数据采集、因子计算、因子挖掘、因子分析、机器学习、策略编写、量化回测、实盘接入等全流程的量化投研工作
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- [pysystemtrade](https://github.com/robcarver17/pysystemtrade) | `Python`, `Live Trading` | - Systematic Trading in python from book <Systematic Trading> by Rob Carver
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- [finmarketpy](https://github.com/cuemacro/finmarketpy) | `Python` | - Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)
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- [vectorbt](https://github.com/polakowo/vectorbt) | `Python`, `numba` | - vectorbt takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds.
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- [fund-strategy](https://github.com/SunshowerC/fund-strategy) | `TypeScript` | - 一个简单实用的基金投资策略分析，基金回测工具
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- [fastquant](https://github.com/enzoampil/fastquant) | `Python` | - Backtest and optimize your ML trading strategies with only 3 lines of code
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- [FinHack](https://github.com/FinHackCN/finhack)   | Python | - 一个易于拓展的量化金融框架，它在当前版本中集成了数据采集、因子计算、因子挖掘、因子分析、机器学习、策略编写、量化回测、实盘接入等全流程的量化投研工作
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- [pysystemtrade](https://github.com/robcarver17/pysystemtrade)   | Python, Live Trading | - Systematic Trading in python from book <Systematic Trading> by Rob Carver
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- [finmarketpy](https://github.com/cuemacro/finmarketpy)   | Python | - Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)
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- [vectorbt](https://github.com/polakowo/vectorbt)   | Python, numba | - vectorbt takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds.
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- [fund-strategy](https://github.com/SunshowerC/fund-strategy)   | TypeScript | - 一个简单实用的基金投资策略分析，基金回测工具
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- [fastquant](https://github.com/enzoampil/fastquant)   | Python | - Backtest and optimize your ML trading strategies with only 3 lines of code
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### Crypto currency focus
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- [basana](https://github.com/gbeced/basana) | `Python` | - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
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- [c-binance-future-quant](https://github.com/Melelery/c-binance-future-quant/tree/main)  | `Python` | - 低成本，高效率，简单实现的币安合约量化系统架构
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- [triangular-arbitrage2](https://github.com/zlq4863947/triangular-arbitrage2) | `TypeScript` | - a server side application for perform triangular arbitrage.
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- [bTrader](https://github.com/gabriel-milan/btrader) | `Rust` | - Triangle arbitrage trading bot for Binance
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- [crypto-crawler-rs](https://github.com/crypto-crawler/crypto-crawler-rs) | `Rust` | - Crawl orderbook and trade messages from crypto exchanges
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- [cryptotrader-core](https://github.com/monomadic/cryptotrader-core) | `Rust` | - Simple to use Crypto Exchange REST API client in rust.
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- [openlimits](https://github.com/nash-io/openlimits) | `Rust` | - A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers.
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- [Freqtrade](https://github.com/freqtrade/freqtrade) | `Python` | - Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
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- [* Hummingbot](https://github.com/CoinAlpha/hummingbot)  | `Python`, `Cython`, `Live Trading` | - A client for crypto market making
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- [Jesse](https://github.com/jesse-ai/jesse) | `Python` | - Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.
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- [* OctoBot](https://github.com/Drakkar-Software/OctoBot) | `Python`, `Cython`, `Live Trading`| - Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface
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- [Kelp](https://github.com/stellar/kelp) | `Go`, `Live Trading` | - Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges
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- [exc](https://github.com/Nouzan/exc) | `Rust` | - The abstraction layer of exchanges.
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- [MyCryptoBot](https://github.com/diogomatoschaves/MyCryptoBot) | `Python`, `Js` | - Automated, open source crypto trading and backtesting platform
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- [basana](https://github.com/gbeced/basana)   | Python | - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
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- [c-binance-future-quant](https://github.com/Melelery/c-binance-future-quant/tree/main)   | Python | - 低成本，高效率，简单实现的币安合约量化系统架构
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- [triangular-arbitrage2](https://github.com/zlq4863947/triangular-arbitrage2)   | TypeScript | - a server side application for perform triangular arbitrage.
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- [bTrader](https://github.com/gabriel-milan/btrader)   | Rust | - Triangle arbitrage trading bot for Binance
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- [crypto-crawler-rs](https://github.com/crypto-crawler/crypto-crawler-rs)   | Rust | - Crawl orderbook and trade messages from crypto exchanges
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- [cryptotrader-core](https://github.com/monomadic/cryptotrader-core)   | Rust | - Simple to use Crypto Exchange REST API client in rust.
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- [openlimits](https://github.com/nash-io/openlimits)   | Rust | - A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers.
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- [Freqtrade](https://github.com/freqtrade/freqtrade)   | Python | - Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
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- [* Hummingbot](https://github.com/CoinAlpha/hummingbot)   | Python, Cython, Live Trading | - A client for crypto market making
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- [Jesse](https://github.com/jesse-ai/jesse)   | Python | - Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.
|
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- [* OctoBot](https://github.com/Drakkar-Software/OctoBot)   | Python, Cython, Live Trading| - Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface
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- [Kelp](https://github.com/stellar/kelp)   | Go, Live Trading | - Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges
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- [exc](https://github.com/Nouzan/exc)   | Rust | - The abstraction layer of exchanges.
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- [MyCryptoBot](https://github.com/diogomatoschaves/MyCryptoBot)   | Python, Js | - Automated, open source crypto trading and backtesting platform
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### Machine Learning / Reinforcement Learning Focused
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> ML, RL
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- [FinRL](https://github.com/AI4Finance-Foundation/FinRL)  | `Python` | - FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance.
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- [QLib (Microsoft)](https://github.com/microsoft/qlib)  | `Python`, `Cython` | - Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
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- [TradingGym](https://github.com/Yvictor/TradingGym) | `Python`, `Live Trading` | - Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
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- [Stock Trading Bot using Deep Q-Learning](https://github.com/pskrunner14/trading-bot) | `Python` | - Stock Trading Bot using Deep Q-Learning
|
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- [FinRL](https://github.com/AI4Finance-Foundation/FinRL)   | Python | - FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance.
|
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- [QLib (Microsoft)](https://github.com/microsoft/qlib)   | Python, Cython | - Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
|
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- [TradingGym](https://github.com/Yvictor/TradingGym)   | Python, Live Trading | - Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
|
||||
- [Stock Trading Bot using Deep Q-Learning](https://github.com/pskrunner14/trading-bot)   | Python | - Stock Trading Bot using Deep Q-Learning
|
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## Alpha Collections
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@@ -210,75 +210,75 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
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### Fundamental libraries
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- [Cvxpy](https://github.com/cvxpy/cvxpy)  | `Python`, `C++` | - A Python-embedded modeling language for convex optimization problems.
|
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- [Numpy](https://github.com/numpy/numpy) | `Python`, `C` | - The fundamental package for scientific computing with Python
|
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- [Cvxpy](https://github.com/cvxpy/cvxpy)   | Python, C++ | - A Python-embedded modeling language for convex optimization problems.
|
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- [Numpy](https://github.com/numpy/numpy)   | Python, C | - The fundamental package for scientific computing with Python
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- Modelling
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- [Scipy](https://github.com/scipy/scipy) | `Python`, `C` | - Fundamental algorithms for scientific computing in Python
|
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- [statsmodels](https://github.com/statsmodels/statsmodels/)  - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
|
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- [PyMC](https://github.com/pymc-devs/pymc)  | `Python` | - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
|
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- [DEAP](https://github.com/DEAP/deap)  |`Python`| - Distributed Evolutionary Algorithms in Python
|
||||
- [Scipy](https://github.com/scipy/scipy)   | Python, C | - Fundamental algorithms for scientific computing in Python
|
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- [statsmodels](https://github.com/statsmodels/statsmodels/)   - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
|
||||
- [PyMC](https://github.com/pymc-devs/pymc)   | Python | - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
|
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- [DEAP](https://github.com/DEAP/deap)   |Python| - Distributed Evolutionary Algorithms in Python
|
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- DataFrame
|
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- [Pandas](https://github.com/pandas-dev/pandas)  | `Python`, `Cython` | - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
|
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- [Polars](https://github.com/pola-rs/polars) | `Rust`, `Python` | - Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
|
||||
- [Pandas](https://github.com/pandas-dev/pandas)   | Python, Cython | - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
|
||||
- [Polars](https://github.com/pola-rs/polars)  | Rust, Python | - Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
|
||||
- Machine Learning
|
||||
- [Sikit-learn](https://github.com/scikit-learn/scikit-learn) | `Python`, `Cython` | - Machine learning in Python
|
||||
- [Keras](https://github.com/keras-team/keras) | `Python` | - The most user friendly Deep Learning for humans in Python
|
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- [TensorFlow](https://github.com/tensorflow/tensorflow) | `Python`, `C++` | - More low level Deep Learning framework
|
||||
- [Pytorch](https://github.com/pytorch/pytorch) | `Python` | - Tensors and Dynamic neural networks in Python with strong GPU acceleration
|
||||
- [Hugging Face](https://github.com/huggingface/) - The AI community building the future.
|
||||
- [LangChain](https://github.com/langchain-ai/langchain) - Building applications with LLMs through composability
|
||||
- [Sikit-learn](https://github.com/scikit-learn/scikit-learn)   | Python, Cython | - Machine learning in Python
|
||||
- [Keras](https://github.com/keras-team/keras)   | Python | - The most user friendly Deep Learning for humans in Python
|
||||
- [TensorFlow](https://github.com/tensorflow/tensorflow)   | Python, C++ | - More low level Deep Learning framework
|
||||
- [Pytorch](https://github.com/pytorch/pytorch)   | Python | - Tensors and Dynamic neural networks in Python with strong GPU acceleration
|
||||
- [Hugging Face](https://github.com/huggingface/)  - The AI community building the future.
|
||||
- [LangChain](https://github.com/langchain-ai/langchain)  - Building applications with LLMs through composability
|
||||
- DAG
|
||||
- [Rustworkx](https://github.com/Qiskit/rustworkx/tree/main)  | `Rust`, `Python` | - A high performance Python graph library implemented in Rust.
|
||||
- [Networkx](https://github.com/networkx/networkx)  | `Python` | - Network Analysis in Python
|
||||
- [Rustworkx](https://github.com/Qiskit/rustworkx/tree/main)   | Rust, Python | - A high performance Python graph library implemented in Rust.
|
||||
- [Networkx](https://github.com/networkx/networkx)   | Python | - Network Analysis in Python
|
||||
|
||||
### Computation
|
||||
|
||||
- [Ray](https://github.com/ray-project/ray)  | `Python`, `C++` | - An open source framework that provides a simple, universal API for building distributed applications.
|
||||
- [csp (Point72)](https://github.com/Point72/csp/tree/main)  | `Python`, `C++` | - csp is a high performance reactive stream processing library, written in C++ and Python
|
||||
- [Dask](https://github.com/dask/dask)  | `Python` | - Parallel computing with task scheduling in Python with a Pandas like API
|
||||
- [Spark](https://github.com/apache/spark)  | `Scala` | - Apache Spark - A unified analytics engine for large-scale data processing
|
||||
- [Hamilton](https://github.com/dagworks-inc/hamilton) | `Python` | - A scalable general purpose micro-framework for defining dataflows. You can use it to build dataframes, numpy matrices, python objects, ML models, etc. Embed Hamilton anywhere python runs, e.g. spark, airflow, jupyter, fastapi, python scripts, etc.
|
||||
- [Incremental (JaneStreet)](https://github.com/janestreet/incremental)  | `Ocaml` | - Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications
|
||||
- [Joblib](https://github.com/joblib/joblib)  | `Python` | - running Python functions as pipeline jobs
|
||||
- [Tributary](https://github.com/timkpaine/tributary) | `Python` | - Streaming reactive and dataflow graphs in Python
|
||||
- [GraphKit(No activity)](https://github.com/yahoo/graphkit) | `Python` | - A lightweight Python module for creating and running ordered graphs of computations.
|
||||
- [Man MDF (No activity)](https://github.com/man-group/mdf) | `Python` | - Data-flow programming toolkit for Python
|
||||
- [Anchors - C++(No activity)](https://github.com/oluwatimilehin/anchors) | `C++` | - C++ library for incremental computing
|
||||
- [Anchors - Rust(No activity)](https://github.com/lord/anchors) | `Rust` | - self adjusting computations in rust
|
||||
- [Loman (No activity)](https://github.com/janushendersonassetallocation/loman) | `Python` | - Loman is a Python library designed to allow quantitative researchers to control complex live updating calculation processes
|
||||
- [Ray](https://github.com/ray-project/ray)   | Python, C++ | - An open source framework that provides a simple, universal API for building distributed applications.
|
||||
- [csp (Point72)](https://github.com/Point72/csp/tree/main)   | Python, C++ | - csp is a high performance reactive stream processing library, written in C++ and Python
|
||||
- [Dask](https://github.com/dask/dask)   | Python | - Parallel computing with task scheduling in Python with a Pandas like API
|
||||
- [Spark](https://github.com/apache/spark)   | Scala | - Apache Spark - A unified analytics engine for large-scale data processing
|
||||
- [Hamilton](https://github.com/dagworks-inc/hamilton)  | Python | - A scalable general purpose micro-framework for defining dataflows. You can use it to build dataframes, numpy matrices, python objects, ML models, etc. Embed Hamilton anywhere python runs, e.g. spark, airflow, jupyter, fastapi, python scripts, etc.
|
||||
- [Incremental (JaneStreet)](https://github.com/janestreet/incremental)   | Ocaml | - Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications
|
||||
- [Joblib](https://github.com/joblib/joblib)   | Python | - running Python functions as pipeline jobs
|
||||
- [Tributary](https://github.com/timkpaine/tributary)  | Python | - Streaming reactive and dataflow graphs in Python
|
||||
- [GraphKit(No activity)](https://github.com/yahoo/graphkit)  | Python | - A lightweight Python module for creating and running ordered graphs of computations.
|
||||
- [Man MDF (No activity)](https://github.com/man-group/mdf)  | Python | - Data-flow programming toolkit for Python
|
||||
- [Anchors - C++(No activity)](https://github.com/oluwatimilehin/anchors)  | C++ | - C++ library for incremental computing
|
||||
- [Anchors - Rust(No activity)](https://github.com/lord/anchors)  | Rust | - self adjusting computations in rust
|
||||
- [Loman (No activity)](https://github.com/janushendersonassetallocation/loman)  | Python | - Loman is a Python library designed to allow quantitative researchers to control complex live updating calculation processes
|
||||
|
||||
### Python Performance Booster
|
||||
|
||||
- [cython](https://github.com/cython/cython)  - Cython is a Python compiler that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations.
|
||||
- [numba](https://github.com/numba/numba)  - NumPy aware dynamic Python compiler using LLVM
|
||||
- [pybind11](https://github.com/pybind/pybind11)  - Seamless operability between C++11 and Python
|
||||
- [pyo3](https://github.com/PyO3/pyo3)  - Rust bindings for the Python interpreter
|
||||
- [CuPy](https://github.com/cupy/cupy/)  | `Python`, `C++`, `Cython`, `Cuda` | - CuPy is an open-source array library for GPU-accelerated computing with Python. 100x Boost for some operations
|
||||
- [CuDF](https://github.com/rapidsai/cudf)  | `Python` | - cuDF - GPU DataFrame Library. No-code-change accelerator for pandas.
|
||||
- [codon](https://github.com/exaloop/codon)  | `C++` | - A high-performance, zero-overhead, extensible Python compiler using LLVM
|
||||
- [Bottleneck](https://github.com/pydata/bottleneck)  | `Python`, `C` | - Fast NumPy array functions written in C
|
||||
- [NumExpr](https://github.com/pydata/numexpr)  | `Python`, `C++` | - Fast numerical array expression evaluator for Python, NumPy, PyTables, pandas, bcolz and more
|
||||
- [pandarallel](https://github.com/nalepae/pandarallel)  | `Python` | - A simple and efficient tool to parallelize Pandas operations on all available CPUs
|
||||
- [cython](https://github.com/cython/cython)   - Cython is a Python compiler that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations.
|
||||
- [numba](https://github.com/numba/numba)   - NumPy aware dynamic Python compiler using LLVM
|
||||
- [pybind11](https://github.com/pybind/pybind11)   - Seamless operability between C++11 and Python
|
||||
- [pyo3](https://github.com/PyO3/pyo3)   - Rust bindings for the Python interpreter
|
||||
- [CuPy](https://github.com/cupy/cupy/)   | Python, C++, Cython, Cuda | - CuPy is an open-source array library for GPU-accelerated computing with Python. 100x Boost for some operations
|
||||
- [CuDF](https://github.com/rapidsai/cudf)   | Python | - cuDF - GPU DataFrame Library. No-code-change accelerator for pandas.
|
||||
- [codon](https://github.com/exaloop/codon)   | C++ | - A high-performance, zero-overhead, extensible Python compiler using LLVM
|
||||
- [Bottleneck](https://github.com/pydata/bottleneck)   | Python, C | - Fast NumPy array functions written in C
|
||||
- [NumExpr](https://github.com/pydata/numexpr)   | Python, C++ | - Fast numerical array expression evaluator for Python, NumPy, PyTables, pandas, bcolz and more
|
||||
- [pandarallel](https://github.com/nalepae/pandarallel)   | Python | - A simple and efficient tool to parallelize Pandas operations on all available CPUs
|
||||
|
||||
### Python Profilers
|
||||
|
||||
- [py-spy](https://github.com/benfred/py-spy)  - Sampling profiler for Python programs
|
||||
- [pyinstrument](https://github.com/joerick/pyinstrument)  - Call stack profiler for Python. Shows you why your code is slow!
|
||||
- [Memray](https://github.com/bloomberg/memray)  - Memray is a memory profiler for Python
|
||||
- [py-spy](https://github.com/benfred/py-spy)   - Sampling profiler for Python programs
|
||||
- [pyinstrument](https://github.com/joerick/pyinstrument)   - Call stack profiler for Python. Shows you why your code is slow!
|
||||
- [Memray](https://github.com/bloomberg/memray)   - Memray is a memory profiler for Python
|
||||
|
||||
### Alternative libraries
|
||||
|
||||
#### Numpy Alternatives
|
||||
|
||||
- [ndarray](https://github.com/rust-ndarray/ndarray) | `Rust` | - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
|
||||
- [faer](https://github.com/sarah-ek/faer-rs)  | `Rust` | - Linear algebra foundation for the Rust programming language
|
||||
- [ndarray](https://github.com/rust-ndarray/ndarray)   | Rust | - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
|
||||
- [faer](https://github.com/sarah-ek/faer-rs)   | Rust | - Linear algebra foundation for the Rust programming language
|
||||
|
||||
#### Pandas Alternatives
|
||||
|
||||
- [DataFrame](https://github.com/hosseinmoein/DataFrame)  | `C++` | - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
|
||||
- [Vaex](https://github.com/vaexio/vaex) | `Python`, `C++` | - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second
|
||||
- [Modin](https://github.com/modin-project/modin) | `Python` | - Modin: Speed up your Pandas workflows by changing a single line of code
|
||||
- [Koalas](https://github.com/databricks/koalas) | `Python` | - Koalas: pandas API on Apache Spark
|
||||
- [DataFrame](https://github.com/hosseinmoein/DataFrame)   | C++ | - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
|
||||
- [Vaex](https://github.com/vaexio/vaex)   | Python, C++ | - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second
|
||||
- [Modin](https://github.com/modin-project/modin)   | Python | - Modin: Speed up your Pandas workflows by changing a single line of code
|
||||
- [Koalas](https://github.com/databricks/koalas)   | Python | - Koalas: pandas API on Apache Spark
|
||||
|
||||
## Analytic tools
|
||||
|
||||
@@ -313,13 +313,13 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
|
||||
|
||||
### Optimization
|
||||
|
||||
- [cvxportfolio](https://github.com/cvxgrp/cvxportfolio)  | `Python` | - Portfolio optimization and back-testing.
|
||||
- [skfolio](https://github.com/skfolio/skfolio)  | `Python` | - Python library for portfolio optimization built on top of scikit-learn
|
||||
- [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib)  | `C++`, `Python` | - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
|
||||
- [Deepdow](https://github.com/jankrepl/deepdow) | `Python` | - Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass.
|
||||
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt) | `Python` | - Financial portfolio optimizations in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
|
||||
- [empyrial](https://github.com/ssantoshp/Empyrial) | `Python` | - Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021.
|
||||
- [spectre](https://github.com/Heerozh/spectre) | `Python` | - spectre is a GPU-accelerated Parallel quantitative trading library, focused on performance.
|
||||
- [cvxportfolio](https://github.com/cvxgrp/cvxportfolio)   | Python | - Portfolio optimization and back-testing.
|
||||
- [skfolio](https://github.com/skfolio/skfolio)   | Python | - Python library for portfolio optimization built on top of scikit-learn
|
||||
- [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib)   | C++, Python | - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
|
||||
- [Deepdow](https://github.com/jankrepl/deepdow)   | Python | - Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass.
|
||||
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt)   | Python | - Financial portfolio optimizations in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
|
||||
- [empyrial](https://github.com/ssantoshp/Empyrial)   | Python | - Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021.
|
||||
- [spectre](https://github.com/Heerozh/spectre)   | Python | - spectre is a GPU-accelerated Parallel quantitative trading library, focused on performance.
|
||||
|
||||
### TimeSeries Analysis
|
||||
|
||||
@@ -329,22 +329,22 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
|
||||
|
||||
## Visualization
|
||||
|
||||
- [Matplotlib](https://github.com/matplotlib/matplotlib) | `Python` | - matplotlib: plotting with Python
|
||||
- [Seaborn](https://github.com/mwaskom/seaborn) | `Python` | - Statistical data visualization in Python
|
||||
- [Dash](https://github.com/plotly/dash) | `Python` | - Data Apps & Dashboards for Python. No JavaScript Required.
|
||||
- [Perspective](https://github.com/finos/perspective) | `C++`, `Python` | - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
|
||||
- [Streamlit](https://github.com/streamlit/streamlit) | `Python` | - Streamlit — A faster way to build and share data apps.
|
||||
- [gradio](https://github.com/gradio-app/gradio/) | `Python` | - Build and share delightful machine learning apps, all in Python.
|
||||
- [pylatex](https://github.com/JelteF/PyLaTeX/) | `Python` | - A Python library for creating LaTeX files
|
||||
- [D-Tale (Man Group)](ht：tps://github.com/man-group/dtale) | `JavaScript`, `Python` | - D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures.
|
||||
- [mplfinance](https://github.com/matplotlib/mplfinance) | `Python` | - Financial Markets Data Visualization using Matplotlib
|
||||
- [btplotting](https://github.com/happydasch/btplotting) | `Python`, `bokeh` | - btplotting provides plotting for backtests, optimization results and live data from backtrader.
|
||||
- [Matplotlib](https://github.com/matplotlib/matplotlib)   | Python | - matplotlib: plotting with Python
|
||||
- [Seaborn](https://github.com/mwaskom/seaborn)   | Python | - Statistical data visualization in Python
|
||||
- [Dash](https://github.com/plotly/dash)   | Python | - Data Apps & Dashboards for Python. No JavaScript Required.
|
||||
- [Perspective](https://github.com/finos/perspective)   | C++, Python | - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
|
||||
- [Streamlit](https://github.com/streamlit/streamlit)   | Python | - Streamlit — A faster way to build and share data apps.
|
||||
- [gradio](https://github.com/gradio-app/gradio/)   | Python | - Build and share delightful machine learning apps, all in Python.
|
||||
- [pylatex](https://github.com/JelteF/PyLaTeX/)   | Python | - A Python library for creating LaTeX files
|
||||
- [D-Tale (Man Group)](https://github.com/man-group/dtale)   | JavaScript, Python | - D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures.
|
||||
- [mplfinance](https://github.com/matplotlib/mplfinance)   | Python | - Financial Markets Data Visualization using Matplotlib
|
||||
- [btplotting](https://github.com/happydasch/btplotting)   | Python, bokeh | - btplotting provides plotting for backtests, optimization results and live data from backtrader.
|
||||
|
||||
## Message Queues
|
||||
|
||||
- [Kafka](https://github.com/apache/kafka) | `Java` | - Mirror of Apache Kafka
|
||||
- [RedPanda](https://github.com/redpanda-data/redpanda/) | `C++` | - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
|
||||
- [BlazingMQ](https://github.com/bloomberg/blazingmq) | `C++` | - A modern high-performance open source message queuing system
|
||||
- [Kafka](https://github.com/apache/kafka)   | Java | - Mirror of Apache Kafka
|
||||
- [RedPanda](https://github.com/redpanda-data/redpanda/)   | C++ | - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
|
||||
- [BlazingMQ](https://github.com/bloomberg/blazingmq)   | C++ | - A modern high-performance open source message queuing system
|
||||
|
||||
## Databases
|
||||
|
||||
|
||||
Reference in New Issue
Block a user