Search awesome repositories

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

Quantitative systematic trading strategy development and backtesting in Julia

A python library for computing technical analysis indicators on streaming data.

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.

Use Python like a spreadsheet!

Kelly Criterion calculation

Quantitative factor research skills for AI coding assistants

Time series implementation for the Julia language focused on efficiency and flexibility

Zigma is an algorithmic trading framework built with the Zig programming language, leveraging an actor-based concurrency model. It aims to provide an efficient, low-latency system for algorithmic trading through components handling market data, strategy execution, order management, risk, and data persistence.

A zero-alloc, compile-time hardened FIX engine built for sub-100ns execution.

Data Analysis Studies on Value Investing

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

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

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.

QuantLib with AAD

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.

PriceHub: Unified Python Package for Collecting OHLC Prices from Binance, Bybit, OKX, Coinbase, Kraken APIs into a DataFrame

Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.

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

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.

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.

Fast Risks with QuantLib in Python

📚 MesoSim's Strategy Library

Zipline Extensions for QuantRocket

Rust crate of validated, decimal-precise trading building blocks: price types, L2 order book, tick-to-OHLCV aggregation, hundreds of streaming indicators, position ledger, risk rules, Black-Scholes Greeks and a walk-forward backtester. On crates.io.

C++20 research implementation of special-relativistic geometry on OHLCV market data: Lorentz factors, spacetime-interval regime labels, Christoffel symbols and geodesic-deviation signals, with SIMD kernels, Python validation scripts and a LaTeX paper.