pyqlib 0.6.0

A Quantitative-research Platform

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.

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.

With Qlib, user can easily try ideas to create better Quant investment strategies.

For more details, please refer to our paper "Qlib: An AI-oriented Quantitative Investment Platform".

• Framework of Qlib
• Quick Start
  • Installation
  • Data Preparation
  • Auto Quant Research Workflow
  • Building Customized Quant Research Workflow by Code
• Quant Model Zoo
  • Run a single model
  • Run multiple models
• Quant Dataset Zoo
• More About Qlib
• Offline Mode and Online Mode
  • Performance of Qlib Data Server
• Contributing

Framework of Qlib

At the module level, Qlib is a platform that consists of the above components. The components are designed as loose-coupled modules and each component could be used stand-alone.

Name	Description
Infrastructure layer	Infrastructure layer provides underlying support for Quant research. DataServer provides high-performance infrastructure for users to manage and retrieve raw data. Trainer provides flexible interface to control the training process of models which enable algorithms controlling the training process.
Workflow layer	Workflow layer covers the whole workflow of quantitative investment. Information Extractor extracts data for models. Forecast Model focuses on producing all kinds of forecast signals (e.g. alpha, risk) for other modules. With these signals Portfolio Generator will generate the target portfolio and produce orders to be executed by Order Executor.
Interface layer	Interface layer tries to present a user-friendly interface for the underlying system. Analyser module will provide users detailed analysis reports of forecasting signals, portfolios and execution results

• The modules with hand-drawn style are under development and will be released in the future.
• The modules with dashed borders are highly user-customizable and extendible.

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.

Installation

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

  pip install pyqlib

Also, users can install 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:

  git clone https://github.com/microsoft/qlib.git && cd qlib
  python setup.py install
  

Data Preparation

Load and prepare data by running the following code:

python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

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.

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 high-quality dataset. For more information, users can refer to the related document.

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.yaml) as following.

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

   The result of qrun is as follows, please refer to 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

Building Customized Quant Research Workflow by Code

The automatic workflow may not suite the research workflow of all Quant researchers. To support a flexible Quant research workflow, Qlib also provides a modularized interface to allow researchers to build their own workflow by code. Here is a demo for customized Quant research workflow by code.

Quant Model Zoo

Here is a list of models built on Qlib.

• GBDT based on LightGBM
• GBDT based on Catboost
• GBDT based on XGBoost
• MLP based on pytorch
• GRU based on pytorch
• LSTM based on pytorcn
• ALSTM based on pytorcn
• GATs based on pytorch
• SFM based on pytorch

Your PR of new Quant models is highly welcomed.

Run a single model

All the models listed above are runnable with Qlib. Users can find the config files we provide and some details about the model through the benchmarks folder. More information can be retrieved at the model files listed above.

Qlib provides three different ways to run a single model, users can pick the one that fits their cases best:

• User can use the tool qrun mentioned above to run a model's workflow based from a config file.

• User can create a workflow_by_code python script based on the one listed in the examples folder.

• User can use the script run_all_model.py listed in the examples folder to run a model. Here is an example of the specific shell command to be used: python run_all_model.py --models=lightgbm, where the --models arguments can take any number of models listed above(the available models can be found in benchmarks). For more use cases, please refer to the file's docstrings.

Run multiple models

Qlib also provides a script run_all_model.py which can run multiple models for several iterations. (Note: the script only supprots Linux now. Other OS will be supported in the future.)

The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as IC and backtest results will be generated and stored. (Note: the script will erase your previous experiment records created by running itself.)

Here is an example of running all the models for 10 iterations:

python run_all_model.py 10

It also provides the API to run specific models at once. For more use cases, please refer to the file's docstrings.

Quant Dataset Zoo

Dataset plays a very important role in Quant. Here is a list of the datasets built on Qlib.

Dataset	US Market	China Market
Alpha360	√	√
Alpha158	√	√

Here is a tutorial to build dataset with Qlib. Your PR to build new Quant dataset is highly welcomed.

More About Qlib

The detailed documents are organized in docs. Sphinx and the readthedocs theme is required to build the documentation in html formats.

cd docs/
conda install sphinx sphinx_rtd_theme -y
# Otherwise, you can install them with pip
# pip install sphinx sphinx_rtd_theme
make html

You can also view the latest document online directly.

Qlib is in active and continuing development. Our plan is in the roadmap, which is managed as a github project.

Offline Mode and Online Mode

The data server of Qlib can either deployed as Offline mode or Online mode. The default mode is offline mode.

Under Offline mode, the data will be deployed locally.

Under Online mode, the data will be deployed as a shared data service. The data and their cache will be shared by all the clients. The data retrieval performance is expected to be improved due to a higher rate of cache hits. It will consume less disk space, too. The documents of the online mode can be found in Qlib-Server. The online mode can be deployed automatically with Azure CLI based scripts. The source code of online data server can be found in Qlib-Server repository.

Performance of Qlib Data Server

The performance of data processing is important to data-driven methods like AI technologies. As an AI-oriented platform, Qlib provides a solution for data storage and data processing. To demonstrate the performance of Qlib data server, we compare it with several other data storage solutions.

We evaluate the performance of several storage solutions by finishing the same task, which creates a dataset (14 features/factors) from the basic OHLCV daily data of a stock market (800 stocks each day from 2007 to 2020). The task involves data queries and processing.

	HDF5	MySQL	MongoDB	InfluxDB	Qlib -E -D	Qlib +E -D	Qlib +E +D
Total (1CPU) (seconds)	184.4±3.7	365.3±7.5	253.6±6.7	368.2±3.6	147.0±8.8	47.6±1.0	7.4±0.3
Total (64CPU) (seconds)					8.8±0.6	4.2±0.2

• +(-)E indicates with (out) ExpressionCache
• +(-)D indicates with (out) DatasetCache

Most general-purpose databases take too much time on loading data. After looking into the underlying implementation, we find that data go through too many layers of interfaces and unnecessary format transformations in general-purpose database solutions. Such overheads greatly slow down the data loading process. Qlib data are stored in a compact format, which is efficient to be combined into arrays for scientific computation.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the right to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.