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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
Production-grade Rust-native trading engine with deterministic event-driven architecture
Translatedstock. Get stock data, calculate stock indicators, chip distribution, identify stock patterns, comprehensive stock selection, stock selection strategies, stock verification and backtesting, automatic stock trading, supports PC and mobile devices.
TranslatedQUANTAXIS: a purely local quantitative solution for stocks/futures/options that supports task scheduling and distributed deployment, covering data/backtesting/simulation/trading/visualization/multiple accounts
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
11 Claude skills for day trading: scan thousands of charts, track breaking news, time every entry and exit, and replay every outcome before you risk a dollar.
modular quant framework.
A Java library for technical analysis.
High-performance technical analysis indicators and backtesting framework in Go. Zero dependencies. ✨ See how! 👀
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
Scalable, event-driven, deep-learning-friendly backtesting library
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
Trady is a handy library for computing technical indicators, and it targets to be an automated trading system that provides stock data feeding, indicator computing, strategy building and automatic trading. It is built based on .NET Standard 2.0.
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot