Official Python client for Tessera: order-flow-enriched OHLCV, funding-rate,
and positioning datasets built from raw Hyperliquid trades, read into Polars
or DuckDB over a REST API.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds Sharpe to the Data Source > Crypto section as a REST API and MCP market intelligence resource.
Website URL: https://www.sharpe.ai/docs/free-api
Linear: SHA-203
* Add PolyMind to Prediction Markets section
PolyMind provides real-time Polymarket trading alerts with multi-AI analysis (Groq, Claude, Gemini), monitoring 200+ markets every 15 seconds across 12 signal types. Related to issue #88.
* Fix typo in C++ badge link
Helium provides real-time market data with AI analysis, ML options pricing, strategy rankings, and multi-source news sentiment via MCP and REST. Listed under Alternative alongside other alternative-data style tools.
Made-with: Cursor
Coinugget is a free real-time crypto signals dashboard covering RSI,
price action, and volume spikes. No sign-up required. Useful for
systematic traders who need quick crypto market overview.
Added PolyMind - real-time Polymarket trading alerts with multi-AI analysis (Groq, Claude, Gemini). Complements the existing polymarket-whales entry with additional signal types and AI-powered analysis.
Adding Congressional Stock Brain (https://congressionalstockbrain.com) to the Relevant Projects section. CSB is a free AI-powered platform that scores every U.S. STOCK Act disclosure by significance using committee relevance, timing vs. legislative activity, trade size vs. historical baseline, and disclosure delay. Covers 800+ lawmakers and 50,000+ disclosed trades. No login required.
CongressionalStockBrain (congressionalstockbrain.com) is an AI-powered tool that ingests STOCK Act disclosures — the mandatory trade filings submitted by U.S. senators and representatives — and converts them into machine-scored signals for systematic traders and quant researchers.
It belongs in the Alternative data sources section alongside SEC EDGAR tools like edgartools, as it provides a unique, legally-mandated dataset (lawmaker trading activity) that can be used as an alternative signal in equity strategies.
Free tier available. Pro tier at $7.77/month.
System R is a risk intelligence API for autonomous trading agents.
It provides pre-trade risk gating, position sizing (G-formula/Kelly),
drawdown analysis, Monte Carlo simulation, and regime detection.
Available as REST API, Python SDK, and MCP server.
https://agents.systemr.ai
edgartools is a full-featured Python library for SEC EDGAR data,
covering filings, XBRL financials, 13F holdings, insider transactions,
and corporate events. Includes a built-in MCP server for AI workflows.
https://github.com/dgunning/edgartools
[Interested in systematic trading? Check QuantBox](https://quant.funcoder.net/)
A curated list of awesome libraries, packages and resources for Systematic Trading (Quantitative Trading)
> Open access: all rights granted for use and re-use of any kind, by anyone, at no cost, under your choice of either the free MIT License or Creative Commons CC-BY International Public License.
@@ -62,19 +69,42 @@ And I count crypto as whole new category: [>> Click ME to Systematic Crypto](cry
- [Optimization](#optimization)
- [TimeSeries Analysis](#timeseries-analysis)
- [Visualization](#visualization)
- [Message Queues](#message-queues)
- [Databases](#databases)
- [Data Source](#data-source)
- [Stocks and General](#stocks-and-general)
- [Alternative](#alternative)
- [Crypto](#crypto)
- [Broker APIs](#broker-apis)
- [🔥 Quant Shops Code and Blog](#-quant-shops-code-and-blog)
- [Quant Shops Code and Blog](#quant-shops-code-and-blog)
- [Resources](#resources)
- [Research](#research)
- [Books](#books)
- [Blogs](#blogs)
- [Tutorials](#tutorials)
- [Courses](#courses)
- [Relevant Projects](#relevant-projects)
- [Buy me a coffee?](#buy-me-a-coffee)
## 🔥 AI Powered Systematic Trading Systems
- [DepthSight](https://github.com/depthsight-pro/depthsight)   | `Python``TypeScript` | - Self-hosted visual algo-trading platform featuring a drag-and-drop strategy builder, an AI co-pilot for automated strategy design, and integrated billing.
- [TradeSight](https://github.com/rmbell09-lang/tradesight)   | `Python` | - AI-powered trading intelligence platform with automated strategy tournaments, multi-market scanning (stocks + prediction markets), 15+ technical indicators, paper trading, and web dashboard. Self-evolving strategies via overnight optimization cron.
- [AI Hedge Fund](https://github.com/virattt/ai-hedge-fund)   | `Python` | - An AI Hedge Fund Team
- [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.
- [FinGPT](https://github.com/AI4Finance-Foundation/FinGPT)   - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
- [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.
- [Qbot](https://github.com/UFund-Me/Qbot)   | `Python` | - AI 自动量化交易机器人 AI-powered Quantitative Investment Research Platform.
- [VARRD](https://github.com/augiemazza/varrd)   | `Python` | - AI-powered trading research platform that validates any trading idea with event studies, statistical tests, and real market data across 15,000+ instruments. CLI, Python SDK, and MCP server.
- [InvicTrade](https://invictrade.com) - AI-powered trading signals with 74% historical win rate, combining strategies from legendary investors using multi-model AI intelligence.
- [BullBear](https://github.com/DunkinGuys/bullbear)   | `TypeScript` | - Open-source AI agent stock trading battle platform. AI agents get $100K virtual cash, trade real US stock prices, and compete on a public leaderboard with social feed.
- [FinClaw](https://github.com/NeuZhou/finclaw)   | `Python` | - AI-native quantitative finance engine with genetic algorithm strategy evolution. 484 built-in factors, walk-forward validation, multi-market support (A-shares, US, crypto). Strategies evolve themselves via GA — no manual parameter tuning needed.
- [OpenFinClaw](https://github.com/cryptoSUN2049/openFinclaw)   | `TypeScript` | - AI-native systematic trading framework. Natural language strategy generation, multi-market execution (US/HK/CN/Crypto), self-evolving strategy pipeline with community leaderboard. Built on OpenClaw (68K+ stars).
- [StockKit](https://stockkit.net/) ([GitHub](https://github.com/kentmswood-ui/stockkit)) | `TypeScript` | - Free AI-powered stock research reports delivered daily. Wall Street-grade analysis for US, China & HK stocks using Claude Opus and multi-model AI engine. 20+ technical indicators, automated email delivery.
- [stock-analysis](https://github.com/AdvancingTitans/stock-analysis)   | `Python` | - Evidence-driven market recap CLI for A/HK/US stocks and funds, producing Markdown reports and JSON Evidence Packs for AI agent workflows.
- [oracle3](https://github.com/YichengYang-Ethan/oracle3)   | `Python` | - Autonomous trading agent for Kalshi, Polymarket, and Solana DFlow with Wang Transform pricing engine calibrated on 291,309 resolved contracts (λ̂ = 0.183), eight constraint-based arbitrage strategies, hierarchical MLE, model Greeks, and Kelly-sized execution. Backed by SSRN working paper.
- [Eterna](https://github.com/EternaHybridExchange/eterna-ai)   | `MCP` | - Launch your own autonomous perp trading AI via Claude Code in 60 seconds. Hybrid exchange MCP with $10B+ aggregated liquidity across 500+ pairs — Claude executes your strategies 24/7. Endpoint: `https://mcp.eterna.exchange/mcp`
- [Inalpha](https://github.com/mirror29/inalpha)   | `Python``TypeScript` | - AI agent framework for quant research: agents pick the factors working now to time entries (time-series rank IC), write full Python strategies audited in sandboxes, and evolve them under multi-objective fitness. Every order passes machine approval — the LLM has no direct order path. Multi-market: crypto, US/CN/HK equities, global indices, FRED macro.
@@ -85,115 +115,208 @@ And I count crypto as whole new category: [>> Click ME to Systematic Crypto](cry
Note: the one marked as `Live Trading` has reasonable live trading support for at least 1 broker. Otherwise, backtest
function only.
- [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.
- [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.
- [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
- [quanttrader](https://github.com/letianzj/quanttrader) | `Python` | - Backtest and live trading in Python. Event based. Similar to backtesting.py.
- [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.
- [* 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
- [zipline](https://github.com/quantopian/zipline) | `Python` | - Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.
- [the0](https://github.com/alexanderwanyoike/the0)   | Python, TypeScript, Rust, C++, C#, Scala, Haskell, Live Trading | - Self-hosted execution engine for algorithmic trading bots. Each bot runs in an isolated container. No framework imposed — your bot is just normal code.
- [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
- [Better Quant](https://github.com/byrnexu/betterquant)   | C++, Live Trading | - Better quant today, best quant tomorrow. 💪
- [Botvana](https://github.com/featherenvy/botvana)   | Rust | - high-performance and event-driven trading system built using Rust
- [backtrader](https://github.com/mementum/backtrader)   | Python, Live Trading | - Event driven Python Backtesting library for trading strategies
- [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.
- [FlashFunk](https://github.com/HFQR/FlashFunk)   | Rust | - High Performance Runtime in Rust
- [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。
- [gobacktest](https://github.com/gobacktest/gobacktest)   | Go | - A Go implementation of event-driven backtesting framework
- [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)   | 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
- [PyBroker](https://github.com/edtechre/pybroker)   | Python | - Algorithmic Trading in Python with Machine Learning
- [QuantConnect](https://github.com/QuantConnect/Lean)   | C#, .NET, Live Trading | - Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
- [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.
- [* 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
- [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
- [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.
- [flashalpha-fill-simulator](https://github.com/FlashAlpha-lab/flashalpha-fill-simulator)   | Python, Options | - Realistic limit-order fill simulator for options credit/debit spreads. Models post-and-wait limits, stale-quote guards, deterministic same-bar tiebreaks, and a patient-then-cross exit. Engine-agnostic per-bar primitive that embeds in QuantConnect / Backtrader / custom loops; zero runtime dependencies.
- [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.
- [Gunbot Quant](https://github.com/GuntharDeNiro/gunbot-quant)   | Python | - Standalone application for market screening and backtesting. Supports multi-asset, multi-strategy backtests. Includes user interface and CLI options.
- [Tradingview Screener API](https://github.com/jmargieh/tradingview-screener)   | TypeScript | - Query TradingView's financial data with a type-safe, fluent TypeScript API. Screen stocks, crypto, forex, bonds, and futures using 13,000+ data fields. The library includes a Model Context Protocol (MCP) server that lets you query financial markets using natural language in Claude Desktop. This is a unique feature that enables conversational market analysis.
> Vector Based Frameworks
- [bt](https://github.com/pmorissette/bt) | `Python` | - Flexible backtesting for Python based on Algo and Strategy Tree
- [pysystemtrade](https://github.com/robcarver17/pysystemtrade) | `Python`, `Live Trading` | - Systematic Trading in python from book <Systematic Trading> by Rob Carver
- [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.
- [QTradeX](https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK)   | Python, Live Trading | - A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots
- [pysystemtrade](https://github.com/robcarver17/pysystemtrade)   | Python, Live Trading | - Systematic Trading in python from book <Systematic Trading> by Rob Carver
- [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.
- [fastquant](https://github.com/enzoampil/fastquant)   | Python | - Backtest and optimize your ML trading strategies with only 3 lines of code
- [Manifold-BT](https://github.com/manifoldbt/manifoldbt)   | Python, Rust | - High-performance Rust-powered backtesting engine: strategies are written as vectorized signal expressions and run with realistic per-bar fills (fees, slippage and look-ahead handled), testing hundreds of thousands of parameter combinations in seconds, plus walk-forward and Monte Carlo.
- [crypto-crawler-rs](https://github.com/crypto-crawler/crypto-crawler-rs) | `Rust` | - Crawl orderbook and trade messages from crypto exchanges
- [cryptotrader-core](https://github.com/monomadic/cryptotrader-core) | `Rust` | - Simple to use Crypto Exchange REST API client in rust.
- [openlimits](https://github.com/nash-io/openlimits) | `Rust` | - A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers.
- [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.
- [* Hummingbot](https://github.com/CoinAlpha/hummingbot) | `Python`, `Cython`, `Live Trading` | - A client for crypto market making
- [Jesse](https://github.com/jesse-ai/jesse) | `Python` | - Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.
- [* 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
- [Kelp](https://github.com/stellar/kelp) | `Go`, `Live Trading` | - Kelp is a free and open-source tradingbot for the Stellar DEX and 100+ centralized exchanges
- [exc](https://github.com/Nouzan/exc) | `Rust` | - The abstraction layer of exchanges.
- [basana](https://github.com/gbeced/basana)   | Python | - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
- [triangular-arbitrage2](https://github.com/zlq4863947/triangular-arbitrage2)   | TypeScript | - a server side application for perform triangular arbitrage.
- [bTrader](https://github.com/gabriel-milan/btrader)   | Rust | - Triangle arbitrage trading bot for Binance
- [crypto-crawler-rs](https://github.com/crypto-crawler/crypto-crawler-rs)   | Rust | - Crawl orderbook and trade messages from crypto exchanges
- [cryptotrader-core](https://github.com/monomadic/cryptotrader-core)   | Rust | - Simple to use Crypto Exchange REST API client in rust.
- [openlimits](https://github.com/nash-io/openlimits)   | Rust | - A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers.
- [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.
- [* Hummingbot](https://github.com/CoinAlpha/hummingbot)   | Python, Cython, Live Trading | - A client for crypto market making
- [Jesse](https://github.com/jesse-ai/jesse)   | Python | - Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.
- [* 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
- [DeepAlpha](https://github.com/stefanoviana/deepalpha) - `Python` | AI crypto trading bot for Bybit with 70.9% walk-forward validated accuracy. LightGBM + XGBoost ensemble with 72 ML features.
- [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
- [exc](https://github.com/Nouzan/exc)   | Rust | - The abstraction layer of exchanges.
- [MyCryptoBot](https://github.com/diogomatoschaves/MyCryptoBot)   | Python, Js | - Automated, open source crypto trading and backtesting platform
- [godzilla.dev](https://godzilla.dev)   | C++, Python | - Open-source framework for crypto quant trading, funding rate arbitrage and ultra-low-latency market making. [GitHub](https://github.com/godzilla-foundation/godzilla-community)
- [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.
- [* 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.
- [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
- [ml-quant-trading](https://github.com/initial-d/ml-quant-trading)   | `Python`, `PyTorch` | - Research stack for multi-factor ML experiments with 213 mask-aware factors, bias correction, portfolio optimization, and vectorized backtesting.
- [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
- [Finance](https://github.com/shashankvemuri/Finance) | `Python` | - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data
-[ThetaGang](https://github.com/brndnmtthws/thetagang) - ThetaGang is an IBKR bot for collecting money
- [volest](https://github.com/jasonstrimpel/volatility-trading) | `Python` | - A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
- [torchquantum](https://github.com/nymath/torchquantum) | `Cython`, `C`, `Python` | - TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.
- [OpenAlpha](https://github.com/caoruicn/openalpha) | `C++` | - An open source equity statistical arbitrage backtest simulator, use the same API as WorldQuant's WebSim
- [stock](https://github.com/xcycharles/stock) | `Python` | - 一些因子挖掘的代码 A 股
- [Genetic-Alpha](https://github.com/Morgansy/Genetic-Alpha) - A genetic programming algorithm used for generating alpha factors in the multi-factor investment strategy
- [alpha_examples](https://github.com/wukan1986/alpha_examples) - An expression based alpha demo using Polars
- [R2 Bitcoin Arbitrager](https://github.com/bitrinjani/r2) | `TypeScript` | - R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript.
## Basic libraries
## Basic Components
### Fundamental libraries
- [Cvxpy](https://github.com/cvxpy/cvxpy) | `Python`, `C++` | - A Python-embedded modeling language for convex optimization problems.
- [Numpy](https://github.com/numpy/numpy) | `Python`, `C` | - The fundamental package for scientific computing with Python
- [Scipy](https://github.com/scipy/scipy) | `Python`, `C` | - Fundamental algorithms for scientific computing in Python
- [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
-[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
- [PyMC](https://github.com/pymc-devs/pymc) | `Python` | - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
- [Cvxpy](https://github.com/cvxpy/cvxpy)   | Python, C++ | - A Python-embedded modeling language for convex optimization problems.
- [jax](https://github.com/google/jax)   | `Python` | - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- [Numpy](https://github.com/numpy/numpy)   | Python, C | - The fundamental package for scientific computing with Python
- [trade-frame](https://github.com/rburkholder/trade-frame)   | C++ | - C++ 17 based library (with sample applications) for testing equities, futures, currencies, etfs & options based automated trading ideas using DTN IQFeed real time data feed and Interactive Brokers (IB TWS API) for trade execution. Some support for Alpaca & Phemex. Notifications via Telegram
-Modelling
- [Scipy](https://github.com/scipy/scipy)   | Python, C | - Fundamental algorithms for scientific computing in Python
- [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
- [DEAP](https://github.com/DEAP/deap)   |Python| - Distributed Evolutionary Algorithms in Python
- DataFrame
- [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.
- [FireDucks](https://fireducks-dev.github.io/)  - Compiler Accelerated DataFrame Library for Python with fully-compatible pandas API
- Machine Learning
- [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
- [JAX](https://github.com/google/jax)   - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- [Pytorch](https://github.com/pytorch/pytorch)   | Python | - Tensors and Dynamic neural networks in Python with strong GPU acceleration
- [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
- 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
### Computation Graph
### Computation
- [Dask](https://github.com/dask/dask) | `Python` | - Parallel computing with task scheduling in Python with a Pandas like API
- [Ray](https://github.com/ray-project/ray) | `Python`, `C++` | - An open source framework that provides a simple, universal API for building distributed applications.
- [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 usefulin a number of applications
- [GraphKit](https://github.com/yahoo/graphkit) | `Python` | - A lightweight Python module for creating and running ordered graphs of computations.
- [Tributary](https://github.com/timkpaine/tributary) | `Python` | - Streaming reactive and dataflow graphs in Python
- [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
- [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
- [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
### 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
- [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
- [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.
- [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
- [ffn](https://github.com/pmorissette/ffn) | `Python` | - A financial function library for Python
- [honest-signals](https://github.com/MarvinRey7879/honest-signals) | `Python` | - Scores detected chart patterns against the pattern-free baseline for the same market and timeframe, reporting lift with cluster-robust confidence intervals instead of a hit rate against 50%
- [quantstats](https://github.com/ranaroussi/quantstats) | `Python` | - Portfolio analytics for quants, written in Python
### Indicators
@@ -205,6 +328,11 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
- [ta-rust](https://github.com/greyblake/ta-rs) | `Rust` | - Technical analysis library for Rust language
- [finta](https://github.com/peerchemist/finta) | `Python` | - Common financial technical indicators implemented in Pandas
- [pandas-ta](https://github.com/twopirllc/pandas-ta) | `Python` | - Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.
- [kand](https://github.com/rust-ta/kand) | `Rust` & `Python` | - A blazingly fast technical analysis library in Rust and Python.
- [chart-patterns](https://github.com/focus1691/chart-patterns) | `TypeScript` | - Technical analysis library for chart patterns, price action, and volume-based pattern detection.
- [ChartScout](https://chartscout.io) - Real-time crypto chart pattern detection and alerts
- [Wickra](https://github.com/wickra-lib/wickra) | `Rust` & `Python` & `JavaScript` & `C++` & `C#` & `Go` & `Java` & `R` | - Streaming-first technical-analysis library with 514 O(1)-per-tick indicators across 24 families; bit-exact batch and streaming from one Rust core.
### Pricing
@@ -213,55 +341,130 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) | `Julia` | - Quantlib implementation in pure Julia.
- [FinancePy](https://github.com/domokane/FinancePy) | `Python` | - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
- [tf-quant-finance](https://github.com/google/tf-quant-finance) - High-performance TensorFlow library for quantitative finance from Google
- [vollib](https://github.com/vollib/vollib) | `Python` | - Fundamentally a swig/python wrapper around Peter Jaeckel's lets_be_rational. lets_be_rational focuses exclusively on Black76, while Vollib extends this to add support for Black-Scholes and Black-Scholes-Merton.
### Risk
- [pyfolio](https://github.com/quantopian/pyfolio) | `Python` | - Portfolio and risk analytics in Python
- [curistat](https://github.com/moxiespirit/MyClone/tree/main/volatility_platform) | `Python` | - Futures volatility forecasting for ES/NQ. Daily CVN rating (1-10), regime detection (CRC composite), directional signals, economic event impact analytics. Includes MCP server for AI agent integration.
- [System R](https://agents.systemr.ai) | `Python` | - Risk intelligence API for trading agents. Pre-trade gate with position sizing (G-formula/Kelly), drawdown analysis, Monte Carlo simulation, regime detection. REST API and MCP server.
- [QuantDojo Tools Hub](https://quantdojo.ai/tools/) – Free, no-login web calculators for position sizing, Kelly criterion, risk/reward, drawdown recovery, Sharpe ratio, and trading expectancy.
### Optimization
- [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
- [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib) | `Python` | - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
- [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
- [statsmodels](http://statsmodels.sourceforge.net) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
- [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.
- [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.
- [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.
- [hurst-calculator](https://github.com/Osamwonyi18/hurst-calculator) - Rescaled Range (R/S) analysis for estimating the Hurst exponent of a time series. Classifies series as mean-reverting, random walk, or trending. NumPy-only, single-file Python.
## Visualization
- [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.
- [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
- [KLinePic](https://klinepic.com/) | `TypeScript` | - Converts broker and exchange trade records into annotated candlestick review charts with buy/sell markers, batch PNG/ZIP export, and an Agent API. [Examples](https://github.com/sher1096/klinepic-agent-api-examples)
- [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
## Databases
- [Arctic (Man Group)](https://github.com/man-group/arctic) | `Python` | - High performance datastore for time series and tick data
- [ArcticDB (Man Group)](https://github.com/man-group/ArcticDB) | `C++`, `Python` | - ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
- [DuckDB](https://github.com/duckdb/duckdb)  | `C++`, `Python` | - ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
- [pylance](https://github.com/lancedb/lance)  | `Rust` | - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow
- [Arctic (Man Group)](https://github.com/man-group/arctic)  | `Python` | - High performance datastore for time series and tick data
- [PyStore](https://github.com/ranaroussi/pystore) | `Python` | - Fast data store for Pandas time-series data
- [Marketstore](https://github.com/alpacahq/marketstore) | `Go` | - DataFrame Server for Financial Timeseries Data
- [Tectonicdb](https://github.com/0b01/tectonicdb) | `Rust` | - Tectonicdb is a fast, highly compressed standalone database and streaming protocol for order book ticks.
- [Redis](https://github.com/redis/redis) | `C` | - Redis is an in-memory database that persists on disk.
- [kdb](https://github.com/KxSystems) | `q` | - Companion files to kdb+ and q
## Data Source
- [FilingFirehose](https://github.com/jaablon/filingfirehose-python) - SEC EDGAR JSON API with body-text-classified 8-Ks (catches buried items: 7.3% of Item 8.01 filings flagged), 13D/G with 21+ activist filers tagged, S-3/424B5 ATM detection. Free 72h tier, paid full archive from $29/mo. REST + MCP + Python SDK + GitHub Action.
- [AltData Atlas](https://altdataatlas.com) - Open directory of alternative data providers for systematic and fundamental investors.
- [* OpenBB Terminal](https://github.com/OpenBB-finance/OpenBBTerminal) | `Python` | - Investment Research for Everyone, Anywhere.
- [FinanceDatabase](https://github.com/JerBouma/FinanceDatabase)  - This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
- [FinanceToolkit](https://github.com/JerBouma/FinanceToolkit)  - Toolkit with 200+ financial metrics including 80+ financial ratios, 30+ technical indicators, 20+ risk and performance metrics and 50+ macro indicators which pulls from Financial Modeling Prep, Yahoo Finance, OECD, GMBD and more.
- [AkShare](https://github.com/akfamily/akshare)  |`Python`| - AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
- [findatapy](https://github.com/cuemacro/findatapy) |`Python`| - findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using a unified high level interface.
- [FXMacroData](https://github.com/fxmacrodata/fxmacrodata) |`Python`| - Real-time forex macroeconomic data API serving central bank announcements, policy rates, inflation, employment, and GDP for 18 currencies. Includes MCP server and OAuth support.
- [yfinance](https://github.com/ranaroussi/yfinance) |`Python`| - yfinance offers a threaded and Pythonic way to download market data from Yahoo!Ⓡ finance.
- [pandas-datareader](https://github.com/pydata/pandas-datareader) |`Python`| - Up to date remote data access for pandas, works for multiple versions of pandas.
- [Wallstreet](https://github.com/mcdallas/wallstreet) |`Python`| - Wallstreet: Real time Stock and Option tools
- [TuShare](https://github.com/waditu/tushare) |`Python`| - TuShare is a utility for crawling historical data of China stocks
- [Investpy](https://github.com/alvarobartt/investpy) - Financial Data Extraction from Investing.com with Python
- [AkShare](https://github.com/akfamily/akshare) |`Python`| - AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
- [awesome-data](https://github.com/akfamily/awesome-data) - Awesome-data shows most interesting data-source around the financial world
- [Fundamental Analysis Data](https://github.com/JerBouma/FundamentalAnalysis) | `Python` | - Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.
- [Financial Data](https://financialdata.net/) - Stock Market and Financial Data API
- [StockAInsights](https://stockainsights.com) - Institutional-grade financial statements API with AI extraction from SEC filings — not XBRL. Covers domestic and foreign filers (20-F, 6-K, 40-F), normalized quarterly and annual data.
- [Insider Alerts](https://insideralerts.io/) - SEC Form 4 insider buying and selling alert platform with watchlists, searchable filings, and Telegram notifications for public insider activity.
- [goMacro.ai](https://gomacro.ai) - AI-powered economic calendar with institutional-grade macro insights and scenario planning for NFP, CPI, PPI and other data releases.
- [Chart Library](https://github.com/grahammccain/chart-library-mcp) |`Python`| - Historical chart pattern similarity search engine. 24M+ pre-computed embeddings (pgvector) across 15K+ symbols and 10 years of minute-bar data. Returns forward returns (1/3/5/10 day) for pattern-based strategy development. Also available as MCP server.
- [Helium MCP](https://heliumtrades.com/mcp-page/) | `REST API`, `MCP` | - Free REST + MCP API providing ML-derived options pricing (per-symbol fair value, prob_ITM, Greeks), real-time market data, and AI-ranked trading strategies. Also includes 31-dim news bias scoring across 3.2M+ articles for sentiment overlays. 50 free queries/IP, no signup. [Source](https://github.com/connerlambden/helium-mcp)
- [The Stall](https://github.com/thebrierfox/the-stall) | `MCP`, `x402` | - Pay-per-call MCP server with 191 financial market data capabilities: US stocks, ETFs, equity fundamentals, analyst ratings, earnings surprises, insider trades, hedge fund holdings, options chains, treasury yields, macro indicators, DeFi, crypto, and prediction markets. Pays in USDC on Base via x402; no API keys required. [Server](https://the-stall.intuitek.ai)
- [AlphaAI](https://alphai.io/developers) | `REST API`, `MCP` | - Relevance-scored, ticker-linked financial news (GDELT + SEC EDGAR): every article gets a 1-10 relevance score, a category, and per-ticker impact at ingest, and SEC Form 4 insider filings become scored structured events. Free tier: 20 req/min and 100/day, no card. [MCP](https://alphai.io/mcp)
- [BDE Score™](https://github.com/hbhqq9/bde-score)   | `Python`, `MCP` | - AI-powered multi-market stock analysis with transparent multi-factor scoring for 73 stocks across US, HK, and A-share markets. EU AI Act Art.50 compliant. MCP server + REST API.
### Alternative
- [Adanos Market Sentiment API](https://api.adanos.org/docs/) | `REST API` | - Cross-platform market sentiment API for equities using Reddit, X/Twitter, and Polymarket signals; returns trending tickers, buzz scores, and sentiment snapshots for quant workflows.
- [13F Insight](https://13finsight.com/?utm_source=github&utm_medium=referral&utm_campaign=seo_outreach_20260422&utm_content=wangzhe3224_awesome-systematic-trading) - AI-powered platform for tracking institutional investor 13F holdings; covers 5,000+ managers with position change alerts, concentration analysis, and quarterly filing summaries. Free tier available.
- [SEC EDGAR Filing API](https://github.com/janlukasschroeder/sec-api-python)
- [edgartools](https://github.com/dgunning/edgartools) |`Python`| - SEC EDGAR data for quant strategies — fundamentals, institutional holdings (13F), insider transactions, and corporate events (8-K). Includes MCP server for AI workflows.
- [AlphaSMO](https://github.com/alphasmo/alphasmo-tools) | `CLI`, `MCP`, `REST API` | - SEC 13F institutional holdings, Form 4 insider trading, and a "smart money convergence" signal (tickers where institutions and insiders are both buying). Free anonymous tier, no signup required.
- [CongressionalStockBrain](https://congressionalstockbrain.com) - AI-powered STOCK Act disclosure tracker that converts U.S. lawmaker trade filings into machine-scored signals for retail investors. Alternative data source for equity quant strategies. Free tier available.
- [Helium MCP](https://github.com/connerlambden/helium-mcp)   | `REST API`, `MCP` | - Real-time stock, ETF, and crypto data with AI bull/bear cases and price forecasts; ML options fair value (probability ITM, full Greeks); top-ranked options strategies (short vol, long vol); and news sentiment from 5,000+ sources. MCP server with REST endpoints. Free tier: 50 queries, no auth. [Website](https://heliumtrades.com/mcp-page/)
### Crypto
- [Cryptofeed](https://github.com/bmoscon/cryptofeed) |`Python`| - Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio
- [Cryptofeed](https://github.com/bmoscon/cryptofeed) |`Python`| - Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio
- [Orderflow](https://github.com/focus1691/orderflow) | `TypeScript`, `NestJS`, `TimescaleDB` | - Builds real-time Footprint Candles from WebSocket trade data across crypto exchanges.
- [Agent Gateway](https://agent-gateway-kappa.vercel.app) | `REST API` | - Free unified REST API providing real-time prices for 500+ crypto tokens via Hyperliquid. No API key required for price data. OpenAPI spec at `/openapi.json`.
- [tessera-api](https://github.com/tesseralytics/python-client) | `Python` | - Official client for Tessera: order-flow-enriched OHLCV, funding-rate, and positioning datasets built from raw Hyperliquid trades, read straight into Polars or DuckDB over a REST API. [Website](https://tesseralytics.dev)
- [CoinPaprika](https://api.coinpaprika.com) | `REST API` | - Free crypto market data API with prices, volume, market cap, OHLCV, and exchange data for 7,000+ coins. No API key required, no rate limits.
- [DexPaprika](https://api.dexpaprika.com) | `REST API` | - Free DEX/DeFi data API covering pools, tokens, OHLCV, and trade history across all chains and DEXes. No API key, no rate limits.
- [Sharpe](https://www.sharpe.ai/docs/free-api) | `REST API`, `MCP` | - Crypto market intelligence API and MCP server covering funding rates, options, arbitrage, narratives, exchange listings, and news. Public endpoints work without a required API key. [Website](https://www.sharpe.ai)
- [Coinugget](https://coinugget.com) - Real-time RSI signals, price action & volume spikes dashboard for crypto traders. Free, no sign-up required.
- [Microverse Systems](https://microversesystems.com) - Real-time L2 order books from 21 exchanges. Free WebSocket API, historical replay, sub-ms latency.
- [Market Posture Daily](https://marketpd.com) - Daily trend, regime and relative-strength data for ~90 crypto assets and US stocks/ETFs (price vs the 50/200-day trend, momentum, correlation) plus a cointegration pair screener. Free terminal + JSON API.
- [BitBank](https://bitbank.nz) | `REST API` | - AI-powered crypto forecasting and predictions API with machine learning models for price movement analysis.
- [AgentServices](https://github.com/vbkotecha/aiservices-api) | `REST API`, `MCP`, `x402` | - Crypto & market data API platform with 54 services, 97 endpoints, and 37 MCP tools. On-chain payments via x402 (USDC on Base). Covers prices, OHLCV, on-chain metrics, DeFi data, and technical indicators. [Server](https://agentservices.to)
- [WealthVille](https://wealthville.net/api/v1) | `REST API`, `MCP` | - DeFi liquidity-pool scoring for LP/yield strategies. Covers ~68,800 Solana pools (Meteora DLMM, Orca Whirlpool, Raydium AMM/CLMM/CPMM) and 575 EVM pools across Ethereum, Arbitrum, Base, Optimism, Polygon and BSC. Returns a 0-100 pool score plus an ENTER/HOLD/EXIT/REDUCE/AVOID verdict with per-protocol calibrated confidence, and publishes a miss-inclusive 30-day track record so the signal can be evaluated before it is used. No API key required. Endpoints: `/pools/top`, `/evm/pools`, `/signals/feed`, `/track-record`. [Website](https://wealthville.net/developers)
### Prediction Markets
- [Parsec](https://github.com/parsecular/parsec-mcp) | `Rust`, `TypeScript`, `Python` | - Prediction market data, execution, and live streams across all major exchanges. [Website](https://parsecapi.com)
- [ProfitPlay Agent Arena](https://github.com/jarvismaximum-hue/profitplay-starter) | `Python`, `JavaScript` | - Open prediction market arena where AI agents compete in real-time BTC/ETH/SOL prediction games. Python and Node.js SDKs, 9 live markets, REST + WebSocket APIs. [(Demo)](https://profitplay-1066795472378.us-east1.run.app)
- [pykalshi](https://github.com/ArshKA/kalshi-client)   | `Python` | - Feature-rich Python client for Kalshi prediction markets with WebSocket streaming, automatic retries, rate limiting, pandas integration, Jupyter rendering, and local orderbook management.
- [TurbineFi](https://turbinefi.com) | `Web` | - Build, backtest, and deploy automated trading strategies for prediction markets including Kalshi and Polymarket.
- [TBD Predict](https://github.com/ego-protocol/tbd-vote-cli) | `TypeScript` | - Solana-based prediction market for human opinions with an agent CLI (`@tbd-vote/cli`) and AGENTS.md spec for AI agents to authenticate, list opinion campaigns, and place bets via JSON-friendly commands. [Website](https://www.tbd.vote)
- [PolyMind](https://polyminds.netlify.app/) | - Real-time Polymarket trading alerts with multi-AI analysis (Groq, Claude, Gemini). Monitors 200+ markets every 15 seconds across 12 signal types: whale bets, volume spikes, price reversals, coordinated wallets, and more. Free tier available.
## Broker APIs
@@ -269,16 +472,23 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
- [PENDAX](https://github.com/CompendiumFi/PENDAX-SDK) | `JavaScript` | - A free Javascript library allowing simplified interaction with trading and data commands on a growing list of cryptocurrency exchanges like FTX, OKX, ByBit, & more.
- [ccxt](https://github.com/ccxt/ccxt) | `Python`, `JavaScript` | - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
- [Coinnect](https://github.com/hugues31/coinnect) | `Rust` | - Coinnect is a Rust library aiming to provide a complete access to main crypto currencies exchanges via REST API.
- [async_rithmic](https://github.com/rundef/async_rithmic) | `Python` | - Python async framework for Rithmic Protocol Buffer API
- [pmxt](https://github.com/pmxt-dev/pmxt) | `Python`, `JavaScript` | - A JavaScript / Python prediction markets trading API with support for major exchanges. (The ccxt for prediction markets)
- [PolyClawster](https://github.com/al1enjesus/polyclawster) - AI agent skill for trading on Polymarket prediction markets. Non-custodial, whale signal detection, public leaderboard. Works as OpenClaw skill or Telegram Mini App.
- [NanoStack](https://api.nano-labs.io) | `Rust`, `REST API` | - Permissionless cross-chain execution fabric. 86 chains, 8-15 bps fee, no API key required. Direct pool routing, conservation-enforced settlement, sub-millisecond dispatch.
- [Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage (2021)](https://www.amazon.co.uk/Quantitative-Portfolio-Management-Statistical-Arbitrage/dp/1119821320/ref=asc_df_1119821320/?tag=googshopuk-21&linkCode=df0&hvadid=534858257189&hvpos=&hvnetw=g&hvrand=3040398248892159445&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9044954&hvtargid=pla-919734400242&psc=1&th=1&psc=1)
- [Algorithmic Trading with Python (2020) by Chris Conlan](https://github.com/chrisconlan/algorithmic-trading-with-python)
- [Python for Algorithmic Trading (2020) by Dr. Yves J. Hilpisch](https://github.com/yhilpisch/py4at)
@@ -302,15 +519,18 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
- [Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python](https://github.com/stefan-jansen/machine-learning-for-trading)
- [Advances in Financial Machine Learning](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)
- [Machine Learning for Asset Managers](https://github.com/emoen/Machine-Learning-for-Asset-Managers)
- [Proof Engineering: The Algorithmic Trading Platform](https://medium.com/prooftrading/proof-engineering-the-algorithmic-trading-platform-b9c2f195433d)
- [KeepRule](https://keeprule.com) - Free investment principles knowledge base with 500+ rules from Buffett, Munger, Graham and more, organized by investing scenarios.
### Tutorials
- [Algorithmic Trading for Cryptocurrencies in Python](https://github.com/tudorelu/tudorials/tree/master/trading) - A simple yet practical experiment tutorial for cryto trading.
- [Solo Crypto Quant Starter Kit](https://github.com/cryptomotifs/cipher-starter) - A 150-page playbook for building a Solana-native signal engine + autonomous trading bot solo on $0/mo infrastructure. Covers universe selection, top-K signal filtering, eighth-Kelly sizing, MEV sandwich defenses, three-tier wallet architecture, Canadian NI 31-103 compliance, and Oracle Cloud Always Free deploy. [Landing page](https://cryptomotifs.github.io/cipher-starter/).
### Courses
@@ -324,14 +544,9 @@ Note: the one marked as `Live Trading` has reasonable live trading support for a
- [awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading) - List of awesome resources for machine learning-based algorithmic trading
- [Congressional Stock Brain](https://congressionalstockbrain.com) - Free AI-powered platform that ingests every U.S. STOCK Act disclosure and scores each trade by significance: committee relevance, timing vs. legislative activity, trade size vs. historical baseline, and disclosure delay. Covers 800+ lawmakers and 50,000+ disclosed trades. No login required.
It takes time to produce videos, articles, and maintains the repositories.
Feel free to support me :) thanks.
- [PolyMind](https://polyminds.netlify.app/) - Real-time Polymarket trading alerts with multi-AI analysis (Groq, Claude, Gemini). Monitors 200+ markets every 15 seconds across 12 signal types: whale bets, volume spikes, price reversals, coordinated wallets, and more. Free tier available.
- [EventTrader](https://cymetica.com?utm_source=github&utm_medium=pr&utm_campaign=trading-outreach) — CLOB exchange with 10 AI trading agents running live. Automated market making, epoch keeping, smart order routing. Pre-launch TGE token predictions on Base L2. REST + WebSocket API. [API](https://cymetica.com/api/docs) | [Platform](https://cymetica.com?utm_source=github&utm_medium=pr&utm_campaign=trading-outreach)
-[ToolsNova](https://toolsnova.net) - Free browser-based trading calculators for forex and gold traders. Includes XAU/USD pip calculator, margin calculator, position size calculator and 100+ other tools. No signup required.
- [Jesse](https://github.com/jesse-ai/jesse) | `Python` | - Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.
- [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
- [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
- [DeepAlpha](https://github.com/stefanoviana/deepalpha) | `Python` | - AI crypto trading bot with 3-model ML ensemble (XGBoost, HMM, Transformer), 70.9% walk-forward accuracy, 12 exchanges. Cloud platform with dashboard, backtesting, and TradingView webhooks.
- [Golang Crypto Trading Bot](https://github.com/saniales/golang-crypto-trading-bot/tree/develop) | `Go` | - A golang implementation of a console-based trading bot for cryptocurrency exchanges
- [BBGO](https://github.com/c9s/bbgo) | `Go` | - The modern cryptocurrency trading bot written in Go.
- [Blankly](https://github.com/blankly-finance/blankly) | `Python` | - 🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.
- [Peregrine](https://github.com/wardbradt/peregrine) | `Python` | - Detects arbitrage opportunities across 131 cryptocurrency exchanges in 50 countries
- [K](https://github.com/ctubio/Krypto-trading-bot) | `C++` | - Self-hosted crypto trading bot (automated high frequency market making) written in C++
- [gocryptotrader](https://github.com/thrasher-corp/gocryptotrader) | `Go` | - A cryptocurrency trading bot supporting multiple exchanges written in Golang.
- [polymarket-whales](https://github.com/al1enjesus/polymarket-whales) - Real-time whale trade tracker for Polymarket prediction markets. Fires terminal + Telegram alerts when large orders hit the book. Python, MIT, zero config.
- [PolyMind](https://polyminds.netlify.app/) | `JavaScript` | - Real-time Polymarket trading alerts with multi-AI analysis (Groq, Claude, Gemini). Track whale bets, volume spikes, coordinated wallets, and 12 signal types. Free tier available.
- [gocryptotrader](https://github.com/thrasher-corp/gocryptotrader) | `Go` | - A cryptocurrency trading bot supporting multiple exchanges written in Golang.
- [Solana SDK Tools](https://github.com/DebuggingMax/solana-sdk-tools) | `TypeScript` | - Token scanner and trading toolkit for Solana DEXs (Pump.fun, Raydium) with anti-rug analysis, copy trading, and paper trading mode
- [Signalview](https://www.signalview.xyz) | `Platform` | - Non-custodial AI agents executing backtested signals on Hyperliquid perps; every signal ships a public 18-month backtest scored −100..+100; scoped agent keys cannot withdraw. Free to run.
- [TrendRider Strategy](https://github.com/darkvolg/trendrider-strategy) | `Python` | - Open-source Freqtrade strategy for Bybit with a novel cascading early-loss exit ladder (-1.5% at 2h, BE at 4h, +0.5% at 8h, +1% at 16h, forced 24h). Backtest delta vs flat 24h timeout: +69% net profit, -77% max drawdown. Multi-timeframe entries on BTC/ETH/SOL + 15 altcoins. Public live dry-run dashboard at [trendrider.net/live](https://trendrider.net/live). MIT.
- [INDICIA DESK](https://indiciadesk.com/en/vol) | `Platform` | - BTC/ETH options analytics on Deribit plus whale behaviour on Hyperliquid perps. Automatically classifies multi-leg whale option structures from the trade flow (butterfly, condor, risk reversal, straddle, calendar and five more families), keeps per-strike implied volatility with history, gamma exposure and the market-maker breakeven corridor. Ships an open daily track record with the misses included, and a public backtest of the "max pain magnet" theory across hundreds of expiries. Free tier available.
- [perpsignal](https://github.com/mokshyaprotocol/signalview) | `Python` | - Signal engine and backtester for perpetual futures. Write a strategy as a compact expression or JSON definition, evaluate it against OHLCV, and backtest into Sharpe/return/drawdown/win-rate with fees, funding, stops/targets and leverage modelled. No wallet, key or live-trading code — pure research library, safe to run anywhere. Apache-2.0, `pip install perpsignal`.
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