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Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs

Quantitative Financial Modelling Framework

A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.

Asynchronous, event-driven algorithmic trading in Python and C++

GPU-accelerated Factors analysis library and Backtester

Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]

Python live trade execution library with zipline interface.

A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine

Open Source Algorithmic Trading Engine

Python interface to IEX and IEX cloud APIs

Python framework for quantitative financial analysis and trading algorithms on decentralised exchanges

Technical analysis and other functions to construct technical trading rules with R

Vectorized backtester and trading engine for QuantRocket

Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests

Start developing and backtesting your own automated trading strategies

The fastest way from backtest to live trading.

Feature Engineering and Feature Importance in Machine Learning for Financial Markets

Self-hosted Python strategy research and Alpaca paper trading with broker reconciliation, evidence gates, and live execution disabled.

High performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

Self-tuning multi-agent AI trading system. 8-source signal fusion, Bull/Bear/Judge debate on Claude Opus 4.7, Kelly + ATR position sizing. Python · Kalshi + Polymarket adapters.

A Python SDK for FinancialData.Net API - Real-time & Historical Stock Market Data

An open-source toolkit for quantitative analysis of crypto & stock markets, featuring an advanced market screener, portfolio backtester, and companion tools for the Gunbot trading bot.

RL stock selection for China A-share — bundled polars-native factor library (105 Alpha101 + 191 GTJA Alpha191 = 296 factors), board-aware price limits, GPU train + ONNX CPU infer, MIT-licensed.

AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.

Open-source AI trading signal engine with a public, inspectable outcome ledger. Our own signals lose money on average after modeled costs — we publish every row anyway. Multi-timeframe TA, backtester, paper trading, Telegram/Discord alerts. Self-hosted, Next.js + PostgreSQL, MIT.

Julia Incremental Technical Analysis Indicators (inspired by talipp)

Market-making strategy that placed #2 in Paradigm's Prediction Market Challenge. 110 iterations, 8 hours.

This repository contains the source code and content for the website algotradinglib.com, focused on algorithmic trading and financial market analysis.

AI-powered trading research platform. Test any idea on stocks, futures, and crypto with event studies, backtesting, and statistical validation. MCP server with 8 tools. pip install varrd.