:newspaper: What's NEW!   :sparkling_heart:

Recent released features

Introducing : LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D

We are excited to announce the release of RD-Agent📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.

RD-Agent is now available on GitHub, and we welcome your star🌟!

To learn more, please visit our ♾️Demo page. Here, you will find demo videos in both English and Chinese to help you better understand the scenario and usage of RD-Agent.

Features released before 2021 are not listed here.

Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning.

An increasing number of SOTA Quant research works/papers in diverse paradigms are being released in Qlib to collaboratively solve key challenges in quantitative investment. For example, 1) using supervised learning to mine the market's complex non-linear patterns from rich and heterogeneous financial data, 2) modeling the dynamic nature of the financial market using adaptive concept drift technology, and 3) using reinforcement learning to model continuous investment decisions and assist investors in optimizing their trading strategies.

It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. For more details, please refer to our paper "Qlib: An AI-oriented Quantitative Investment Platform".

Plans

New features under development(order by estimated release time). Your feedbacks about the features are very important.

Framework of Qlib

The high-level framework of Qlib can be found above(users can find the detailed framework of Qlib's design when getting into nitty gritty). The components are designed as loose-coupled modules, and each component could be used stand-alone.

Qlib provides a strong infrastructure to support Quant research. Data is always an important part. A strong learning framework is designed to support diverse learning paradigms (e.g. reinforcement learning, supervised learning) and patterns at different levels(e.g. market dynamic modeling). By modeling the market, trading strategies will generate trade decisions that will be executed. Multiple trading strategies and executors in different levels or granularities can be nested to be optimized and run together. At last, a comprehensive analysis will be provided and the model can be served online in a low cost.

Quick Start

This quick start guide tries to demonstrate

1. It's very easy to build a complete Quant research workflow and try your ideas with Qlib.
2. Though with public data and simple models, machine learning technologies work very well in practical Quant investment.

Here is a quick demo shows how to install Qlib, and run LightGBM with qrun. But, please make sure you have already prepared the data following the instruction.

Installation

Note:

1. Conda is suggested for managing your Python environment. In some cases, using Python outside of a conda environment may result in missing header files, causing the installation failure of certain packages.
2. Please pay attention that installing cython in Python 3.6 will raise some error when installing Qlib from source. If users use Python 3.6 on their machines, it is recommended to upgrade Python to version 3.7 or use conda's Python to install Qlib from source.
3. For Python 3.9, Qlib supports running workflows such as training models, doing backtest and plot most of the related figures (those included in notebook). However, plotting for the model performance is not supported for now and we will fix this when the dependent packages are upgraded in the future.
4. QlibRequires tables package, hdf5 in tables does not support python3.9.

Install with pip

Users can easily install Qlib by pip according to the following command.

  pip install pyqlib

Note: pip will install the latest stable qlib. However, the main branch of qlib is in active development. If you want to test the latest scripts or functions in the main branch. Please install qlib with the methods below.

Install from source

Also, users can install the latest dev version Qlib by the source code according to the following steps:

• Before installing Qlib from source, users need to install some dependencies:

  pip install numpy
  pip install --upgrade  cython

• Clone the repository and install Qlib as follows.

  git clone https://github.com/microsoft/qlib.git && cd qlib
  pip install .  # `pip install -e .[dev]` is recommended for development. check details in docs/developer/code_standard_and_dev_guide.rst

  Note: You can install Qlib with python setup.py install as well. But it is not the recommended approach. It will skip pip and cause obscure problems. For example, only the command pip install . can overwrite the stable version installed by pip install pyqlib, while the command python setup.py install can't.

Tips: If you fail to install Qlib or run the examples in your environment, comparing your steps and the CI workflow may help you find the problem.

Tips for Mac: If you are using Mac with M1, you might encounter issues in building the wheel for LightGBM, which is due to missing dependencies from OpenMP. To solve the problem, install openmp first with brew install libomp and then run pip install . to build it successfully.

Data Preparation

❗ Due to more restrict data security policy. The offical dataset is disabled temporarily. You can try this data source contributed by the community. Here is an example to download the data updated on 20240809.

wget https://github.com/chenditc/investment_data/releases/download/2024-08-09/qlib_bin.tar.gz
mkdir -p ~/.qlib/qlib_data/cn_data
tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1
rm -f qlib_bin.tar.gz

The official dataset below will resume in short future.

Load and prepare data by running the following code:

Get with module

  # get 1d data
  python -m qlib.run.get_data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

  # get 1min data
  python -m qlib.run.get_data qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min

Get from source

  # get 1d data
  python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

  # get 1min data
  python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data_1min --region cn --interval 1min

This dataset is created by public data collected by crawler scripts, which have been released in the same repository. Users could create the same dataset with it. Description of dataset

Please pay ATTENTION that the data is collected from Yahoo Finance, and the data might not be perfect. We recommend users to prepare their own data if they have a high-quality dataset. For more information, users can refer to the related document.

Automatic update of daily frequency data (from yahoo finance)

This step is Optional if users only want to try their models and strategies on history data.

It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.

NOTE: Users can't incrementally update data based on the offline data provided by Qlib(some fields are removed to reduce the data size). Users should use yahoo collector to download Yahoo data from scratch and then incrementally update it.

For more information, please refer to: yahoo collector

• Automatic update of data to the "qlib" directory each trading day(Linux)

  • use crontab: crontab -e

  • set up timed tasks:

    * * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>

    • script path: scripts/data_collector/yahoo/collector.py

• Manual update of data

    python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>

  • trading_date: start of trading day
  • end_date: end of trading day(not included)

Auto Quant Research Workflow

Qlib provides a tool named qrun to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). You can start an auto quant research workflow and have a graphical reports analysis according to the following steps:

1. Quant Research Workflow: Run qrun with lightgbm workflow config (workflow_config_lightgbm_Alpha158.yaml as following.

     cd examples  # Avoid running program under the directory contains `qlib`
     qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

   If users want to use qrun under debug mode, please use the following command:

   python -m pdb qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

   The result of qrun is as follows, please refer to Intraday Trading for more details about the result.

   
   'The following are analysis results of the excess return without cost.'
                          risk
   mean               0.000708
   std                0.005626
   annualized_return  0.178316
   information_ratio  1.996555
   max_drawdown      -0.081806
   'The following are analysis results of the excess return with cost.'
                          risk
   mean               0.000512
   std                0.005626
   annualized_return  0.128982
   information_ratio  1.444287
   max_drawdown      -0.091078

   Here are detailed documents for qrun and workflow.

2. Graphical Reports Analysis: Run examples/workflow_by_code.ipynb with jupyter notebook to get graphical reports

   • Forecasting signal (model prediction) analysis

     • Cumulative Return of groups
     • Return distribution
     • Information Coefficient (IC)
     • Auto Correlation of forecasting signal (model prediction)

   • Portfolio analysis

     • Backtest return