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
- Quant Model Zoo
- Quant Dataset Zoo
- More About Qlib
- Offline Mode and Online Mode
- 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
- It's very easy to build a complete Quant research workflow and try your ideas with Qlib.
- 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
Note: 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 useconda
's Python to installQlib
from source. -
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:
-
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. -
Graphical Reports Analysis: Run
examples/workflow_by_code.ipynb
withjupyter notebook
to get graphical reports
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
- TFT based on tensorflow
Your PR of new Quant models is highly welcomed.
The performance of each model on the Alpha158
and Alpha360
dataset can be found here.
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 theexamples
folder. -
User can use the script
run_all_model.py
listed in theexamples
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 support Linux for now. Other OS will be supported in the future. Besides, it doesn't support parrallel running the same model for multiple times as well, and this will be fixed in the future development too.)
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.
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 opencode@microsoft.com with any additional questions or comments.