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.

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Algorithmic trading using machine learning.

Notebooks based on financial machine learning.

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Quantitative analysis, strategies and backtests

Some notebooks with powerful trading strategies.

Stock indicator technical analysis library package for .NET. Send in historical price quotes and get back desired technical indicators. Nothing more. It can be used in any market analysis software using standard OHLCV price quotes for equities, commodities, forex, cryptocurrencies, and others. We had private trading algorithms, machine learning, and charting systems in mind when originally creating this community library. Current indicators include: Accumulation/Distribution Line (ADL), Aroon Oscillator, Arnaud Legoux Moving Average (ALMA), Average Directional Index (ADX), Average True Range (ATR), Awesome Oscillator (AO), Balance of Power (BOP), Beta Coefficient, Bollinger Bands®, Chaikin Money Flow (CMF), Chaikin Oscillator, Chandelier Exit, Choppiness Index (CHOP), Commodity Channel Index (CCI), ConnorsRSI, Correlation Coefficient, Donchian Channels, Double Exponential Moving Average (DEMA), Elder-ray Index, Exponential Moving Average (EMA), Force Index, Fractal Chaos Bands (FCB), Gator Oscillator, Heikin-Ashi, Hull Moving Average (HMA), Ichimoku Cloud, Kaufman's Adaptive Moving Average (KAMA), KDJ Index, Keltner Channels, Momentum Oscillator, Money Flow Index (MFI), MESA Adaptive Moving Averages (MAMA), Moving Average Convergence/Divergence (MACD), Moving Average Envelopes, On-balance Volume (OBV), Parabolic SAR (stop and reverse), Percentage Volume Oscillator (PVO), Pivot Points and Rolling Pivot Points, Price Channels, Price (Comparative) Relative Strength (PRS), Price Momentum Oscillator (PMO), Rate of Change (ROC), Relative Strength Index (RSI), R-Squared (Coefficient of Determination), Simple Moving Average (SMA), Slope and Linear Regression, Smoothed Moving Average (SMMA), Standard Deviation, Stoller Average Range Channel (STARC) Bands, Stochastic Oscillator, Stochastic RSI, SuperTrend, Tillson T3 Moving Average, Triple Exponential Moving Average (TEMA), Triple EMA Oscillator (TRIX), True Strength Index (TSI), Ulcer Index, Ultimate Oscillator, Volume Simple Moving Average, Volume Weighted Average Price (VWAP), Vortex Indicator (VI), Weighted Moving Average (WMA), Williams %R, Williams Alligator, Williams Fractal, and Zig Zag.

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

A financial trading method using machine learning.

Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex

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

An open source library for portfolio optimisation

PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Cloud-native Financial Reinforcement Learning

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

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

super-monorepo for machine learning and algorithmic trading

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy