Awesome Quant

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Portfolio optimization with deep learning.

Exchange calendars to use with pandas for trading applications

Python wrapper for an unofficial Yahoo Finance API

Collection of algorithms for online portfolio selection

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

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

Python live trade execution library with zipline interface.

Calendars for various securities exchanges.

Python SDK for IEX Cloud

Performance analysis of predictive (alpha) stock factors

SABR model Python implementation

Top training materials in quantitative finance

Portfolio and risk analytics in Python

A Python library for evaluating option trading strategies.

A Python package for aggregating and normalizing historical data from popular and free financial APIs.

Agent-driven alpha factory — LLM autonomously designs, backtests, and submits factors to WorldQuant BRAIN

No description.

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

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

A fixed income library for pricing bonds and bond futures, and derivatives such as interest rate swaps (IRS), cross-currency swaps (XCS) and FX swaps. Contains tools for full curveset construction with market standard optimisers and automatic differentiation (AD) and risk sensitivity calculations including delta and cross-gamma.

Entropy Pooling views and stress testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.

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.

Pipeline Extension for Live Trading

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.

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

portfolio construction and quantitative analysis

Python client for tardis.dev - historical tick-level cryptocurrency market data replay API.

Code repository for Pricing and Trading Interest Rate Derivatives

Python interface to Brazilian Central Bank web services

Common financial risk and performance metrics. Used by zipline and pyfolio.

Get meaningful OHLCV datasets

python module for currencies

Business days calculations and utilities

Analysis of financial instruments

Python API for accessing Lake high frequency tick trades & order book data

a cashflow engine wrapper for structured finance professionals

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.

tessa – simple, hassle-free access to price information of financial assets

Quant trading framework by OctoBot. Write, backtest & automate Python trading strategies like TradingView Pine Script. Work in progress.

Risk tools for commodities trading and finance

Time series and portfolio analytics for quantitative finance.

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.

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.

Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.

Distributed QuantLib

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

High-Performance Automatic Differentiation for Python