Recent released features
| Feature | Status |
|---|---|
| Release Qlib v0.8.0 | Released on Dec 8, 2021 |
| ADD model | Released on Nov 22, 2021 |
| ADARNN model | Released on Nov 14, 2021 |
| TCN model | Released on Nov 4, 2021 |
| Nested Decision Framework | Released on Oct 1, 2021. Example and Doc |
| Temporal Routing Adaptor (TRA) | Released on July 30, 2021 |
| Transformer & Localformer | Released on July 22, 2021 |
| Release Qlib v0.7.0 | Released on July 12, 2021 |
| TCTS Model | Released on July 1, 2021 |
| Online serving and automatic model rolling | ⭐ Released on May 17, 2021 |
| DoubleEnsemble Model | Released on Mar 2, 2021 |
| High-frequency data processing example | Released on Feb 5, 2021 |
| High-frequency trading example | Part of code released on Jan 28, 2021 |
| High-frequency data(1min) | Released on Jan 27, 2021 |
| Tabnet Model | Released on Jan 22, 2021 |
Features released before 2021 are not listed here.
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, users 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".
- Plans
- Framework of Qlib
- Quick Start
- Quant Model(Paper) Zoo
- Quant Dataset Zoo
- More About Qlib
- Offline Mode and Online Mode
- Related Reports
- Contact Us
- Contributing
New features under development(order by estimated release time). Your feedbacks about the features are very important.
| Feature | Status |
|---|---|
| Planning-based portfolio optimization | Under review: microsoft#280 |
| Fund data supporting and analysis | Under review: microsoft#292 |
| Point-in-Time database | Under review: microsoft#343 |
| Meta-Learning-based data selection | Initial opensource version under development |
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 a high-performance infrastructure for users to manage and retrieve raw data. Trainer provides a flexible interface to control the training process of models, which enable algorithms to control 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 Decision Generator will generate the target trading decisions(i.e. portfolio, orders) to be executed by Execution Env (i.e. the trading market). There may be multiple levels of Trading Agent and Execution Env (e.g. an order executor trading agent and intraday order execution environment could behave like an interday trading environment and nested in daily portfolio management trading agent and interday trading environment ) |
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.
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.
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.
This table demonstrates the supported Python version of Qlib:
| install with pip | install from source | plot | |
|---|---|---|---|
| Python 3.7 | ✔️ | ✔️ | ✔️ |
| Python 3.8 | ✔️ | ✔️ | ✔️ |
| Python 3.9 | ❌ | ✔️ | ❌ |
Note:
- Conda is suggested for managing your Python environment.
- Please pay attention that installing cython in Python 3.6 will raise some error when installing
Qlibfrom 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 installQlibfrom source. - For Python 3.9,
Qlibsupports 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.
Users can easily install Qlib by pip according to the following command.
pip install pyqlibNote: 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.
Also, users can install the latest dev version Qlib by the source code according to the following steps:
-
Before installing
Qlibfrom source, users need to install some dependencies:pip install numpy pip install --upgrade cython
-
Clone the repository and install
Qlibas follows.- If you haven't installed qlib by the command
pip install pyqlibbefore:git clone https://github.com/microsoft/qlib.git && cd qlib python setup.py install
- If you have already installed the stable version by the command
pip install pyqlib:git clone https://github.com/microsoft/qlib.git && cd qlib pip install .
Note: Only the command
pip install .can overwrite the stable version installed bypip install pyqlib, while the commandpython setup.py installcan't. - If you haven't installed qlib by the command
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.
Load and prepare data by running the following code:
# 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.
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.
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)
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
qrunwith 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
qrununder 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
qrunis 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
qrunand workflow. -
Graphical Reports Analysis: Run
examples/workflow_by_code.ipynbwithjupyter notebookto get graphical reports-
Forecasting signal (model prediction) analysis
- Cumulative Return of groups
- Return distribution
- Information Coefficient (IC)
- Auto Correlation of forecasting signal (model prediction)
- Cumulative Return of groups
-
Portfolio analysis
- Backtest return
- Backtest return
-
Explanation of above results
-
The automatic workflow may not suit 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.
Here is a list of models built on Qlib.
- GBDT based on XGBoost (Tianqi Chen, et al. KDD 2016)
- GBDT based on LightGBM (Guolin Ke, et al. NIPS 2017)
- GBDT based on Catboost (Liudmila Prokhorenkova, et al. NIPS 2018)
- MLP based on pytorch
- LSTM based on pytorch (Sepp Hochreiter, et al. Neural computation 1997)
- GRU based on pytorch (Kyunghyun Cho, et al. 2014)
- ALSTM based on pytorch (Yao Qin, et al. IJCAI 2017)
- GATs based on pytorch (Petar Velickovic, et al. 2017)
- SFM based on pytorch (Liheng Zhang, et al. KDD 2017)
- TFT based on tensorflow (Bryan Lim, et al. International Journal of Forecasting 2019)
- TabNet based on pytorch (Sercan O. Arik, et al. AAAI 2019)
- DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. ICDM 2020)
- TCTS based on pytorch (Xueqing Wu, et al. ICML 2021)
- Transformer based on pytorch (Ashish Vaswani, et al. NeurIPS 2017)
- Localformer based on pytorch (Juyong Jiang, et al.)
- TRA based on pytorch (Hengxu, Dong, et al. KDD 2021)
- TCN based on pytorch (Shaojie Bai, et al. 2018)
- ADARNN based on pytorch (YunTao Du, et al. 2021)
- ADD based on pytorch (Hongshun Tang, et al.2020)
Your PR of new Quant models is highly welcomed.
The performance of each model on the Alpha158 and Alpha360 dataset can be found here.
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:
-
Users can use the tool
qrunmentioned above to run a model's workflow based from a config file. -
Users can create a
workflow_by_codepython script based on the one listed in theexamplesfolder. -
Users can use the script
run_all_model.pylisted in theexamplesfolder to run a model. Here is an example of the specific shell command to be used:python run_all_model.py run --models=lightgbm, where the--modelsarguments 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.- NOTE: Each baseline has different environment dependencies, please make sure that your python version aligns with the requirements(e.g. TFT only supports Python 3.6~3.7 due to the limitation of
tensorflow==1.15.0)
- NOTE: Each baseline has different environment dependencies, please make sure that your python version aligns with the requirements(e.g. TFT only supports Python 3.6~3.7 due to the limitation of
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 parallel 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 run 10It also provides the API to run specific models at once. For more use cases, please refer to the file's docstrings.
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.
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 htmlYou 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.
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.
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 |
+(-)Eindicates with (out)ExpressionCache+(-)Dindicates with (out)DatasetCache
Most general-purpose databases take too much time to load 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.
- If you have any issues, please create issue here or send messages in gitter.
- If you want to make contributions to
Qlib, please create pull requests. - For other reasons, you are welcome to contact us by email([email protected]).
- We are recruiting new members(both FTEs and interns), your resumes are welcome!
Join IM discussion groups:
| Gitter |
|---|
 |
This project welcomes contributions and suggestions.
Here are some
code standards for submiting a pull request.
Making contributions is not a hard thing. Solving an issue(maybe just answering a question raised in issues list or gitter), fixing/issuing a bug, improving the documents and even fixing a typo are important contributions to Qlib.
For example, if you want to contribute to Qlib's document/code, you can follow the steps in the figure below.
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 [email protected] with any additional questions or comments.