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## :newspaper: **What's NEW!** &nbsp;   :sparkling_heart: 
Recent released features
| Feature | Status |
| --                      | ------    |
| Release Qlib v0.7.0 | [Released](https://github.com/microsoft/qlib/releases/tag/v0.7.0) on July 12, 2021 |
| TCTS Model | [Released](https://github.com/microsoft/qlib/pull/491) on July 1, 2021 |
| Online serving and automatic model rolling | :star: [Released](https://github.com/microsoft/qlib/pull/290) on May 17, 2021 | 
| DoubleEnsemble Model | [Released](https://github.com/microsoft/qlib/pull/286) on Mar 2, 2021 | 
| High-frequency data processing example | [Released](https://github.com/microsoft/qlib/pull/257) on Feb 5, 2021  |
| High-frequency trading example | [Part of code released](https://github.com/microsoft/qlib/pull/227) on Jan 28, 2021  | 
| High-frequency data(1min) | [Released](https://github.com/microsoft/qlib/pull/221) on Jan 27, 2021 |
| Tabnet Model | [Released](https://github.com/microsoft/qlib/pull/205) on Jan 22, 2021 | 

Features released before 2021 are not listed here.



<p align="center">
  <img src="http://fintech.msra.cn/images_v060/logo/1.png" />
</p>


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"](https://arxiv.org/abs/2009.11189).

- [**Plans**](#plans)
- [Framework of Qlib](#framework-of-qlib)
- [Quick Start](#quick-start)
  - [Installation](#installation)
  - [Data Preparation](#data-preparation)
  - [Auto Quant Research Workflow](#auto-quant-research-workflow)
  - [Building Customized Quant Research Workflow by Code](#building-customized-quant-research-workflow-by-code)
- [**Quant Model Zoo**](#quant-model-zoo)
  - [Run a single model](#run-a-single-model)
  - [Run multiple models](#run-multiple-models)
- [**Quant Dataset Zoo**](#quant-dataset-zoo)
- [More About Qlib](#more-about-qlib)
- [Offline Mode and Online Mode](#offline-mode-and-online-mode)
  - [Performance of Qlib Data Server](#performance-of-qlib-data-server)
- [Related Reports](#related-reports)
- [Contact Us](#contact-us)
- [Contributing](#contributing)


# Plans
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:  https://github.com/microsoft/qlib/pull/280 | 
| Fund data supporting and analysis  |  Under review: https://github.com/microsoft/qlib/pull/292 |
| Point-in-Time database | Under review: https://github.com/microsoft/qlib/pull/343 |
| High-frequency trading | Under review: https://github.com/microsoft/qlib/pull/408 | 
| Meta-Learning-based data selection | Initial opensource version under development |

# Framework of Qlib

<div style="align: center">
<img src="http://fintech.msra.cn/images_v060/framework.png?v=0.2" />
</div>


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 `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.

Here is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how to install ``Qlib``, and run LightGBM with ``qrun``. **But**, please make sure you have already prepared the data following the [instruction](#data-preparation).


## Installation

This table demonstrates the supported Python version of `Qlib`:
|               | install with pip           | install from source  | plot |
| ------------- |:---------------------:|:--------------------:|:----:|
| Python 3.6    | :heavy_check_mark:    | :heavy_check_mark: (only with `Anaconda`)                  | :heavy_check_mark: |
| Python 3.7    | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
| Python 3.8    | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
| Python 3.9    | :x:                   | :heavy_check_mark:   | :x: |

**Note**: 
1. 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.
2. 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](examples/workflow_by_code.ipynb)). 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.

### Install with pip
Users can easily install ``Qlib`` by pip according to the following command.

```bash
  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:

  ```bash
  pip install numpy
  pip install --upgrade  cython
  ```

* Clone the repository and install ``Qlib`` as follows.
  * If you haven't installed qlib by the command ``pip install pyqlib`` before:
    ```bash
    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``:
    ```bash
    git clone https://github.com/microsoft/qlib.git && cd qlib
    pip install .
    ```
  **Note**: **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](.github/workflows/test.yml) may help you find the problem.

## Data Preparation
Load and prepare data by running the following code:
  ```bash
  # 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](scripts/data_collector/), 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](https://finance.yahoo.com/lookup), 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](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*.

### Automatic update of daily frequency data(from yahoo finance)
  > 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 refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance)

  * 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)


<!-- 
- Run the initialization code and get stock data:

  ```python
  import qlib
  from qlib.data import D
  from qlib.config import REG_CN

  # Initialization
  mount_path = "~/.qlib/qlib_data/cn_data"  # target_dir
  qlib.init(mount_path=mount_path, region=REG_CN)

  # Get stock data by Qlib
  # Load trading calendar with the given time range and frequency
  print(D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')[:2])

  # Parse a given market name into a stockpool config
  instruments = D.instruments('csi500')
  print(D.list_instruments(instruments=instruments, start_time='2010-01-01', end_time='2017-12-31', as_list=True)[:6])

  # Load features of certain instruments in given time range
  instruments = ['SH600000']
  fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
  print(D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head())
  ```
 -->

## 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](examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml) as following.
    ```bash
      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:
    ```bash
    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](https://qlib.readthedocs.io/en/latest/component/backtest.html) for more details about the result. 

    ```bash

    '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](https://qlib.readthedocs.io/en/latest/component/workflow.html).

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
      ![Cumulative Return](http://fintech.msra.cn/images_v060/analysis/analysis_model_cumulative_return.png?v=0.1)
      - Return distribution
      ![long_short](http://fintech.msra.cn/images_v060/analysis/analysis_model_long_short.png?v=0.1)
      - Information Coefficient (IC)
      ![Information Coefficient](http://fintech.msra.cn/images_v060/analysis/analysis_model_IC.png?v=0.1)        
      ![Monthly IC](http://fintech.msra.cn/images_v060/analysis/analysis_model_monthly_IC.png?v=0.1)
      ![IC](http://fintech.msra.cn/images_v060/analysis/analysis_model_NDQ.png?v=0.1)
      - Auto Correlation of forecasting signal (model prediction)
      ![Auto Correlation](http://fintech.msra.cn/images_v060/analysis/analysis_model_auto_correlation.png?v=0.1)

    - Portfolio analysis
      - Backtest return
      ![Report](http://fintech.msra.cn/images_v060/analysis/report.png?v=0.1)
      <!-- 
      - Score IC
      ![Score IC](docs/_static/img/score_ic.png)
      - Cumulative Return
      ![Cumulative Return](docs/_static/img/cumulative_return.png)
      - Risk Analysis
      ![Risk Analysis](docs/_static/img/risk_analysis.png)
      - Rank Label
      ![Rank Label](docs/_static/img/rank_label.png)
      -->
   - [Explanation](https://qlib.readthedocs.io/en/latest/component/report.html) of above results

## Building Customized Quant Research Workflow by Code
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](examples/workflow_by_code.ipynb) is a demo for customized Quant research workflow by code.


# [Quant Model Zoo](examples/benchmarks)

Here is a list of models built on `Qlib`.
- [GBDT based on XGBoost (Tianqi Chen, et al. KDD 2016)](qlib/contrib/model/xgboost.py)
- [GBDT based on LightGBM (Guolin Ke, et al. NIPS 2017)](qlib/contrib/model/gbdt.py)
- [GBDT based on Catboost (Liudmila Prokhorenkova, et al. NIPS 2018)](qlib/contrib/model/catboost_model.py)
- [MLP based on pytorch](qlib/contrib/model/pytorch_nn.py)
- [LSTM based on pytorch (Sepp Hochreiter, et al. Neural omputation 1997)](qlib/contrib/model/pytorch_lstm.py)
- [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](qlib/contrib/model/pytorch_gru.py)
- [ALSTM based on pytorch (Yao Qin, et al. IJCAI 2017)](qlib/contrib/model/pytorch_alstm.py)
- [GATs based on pytorch (Petar Velickovic, et al. 2017)](qlib/contrib/model/pytorch_gats.py)
- [SFM based on pytorch (Liheng Zhang, et al. KDD 2017)](qlib/contrib/model/pytorch_sfm.py)
- [TFT based on tensorflow (Bryan Lim, et al. International Journal of Forecasting 2019)](examples/benchmarks/TFT/tft.py)
- [TabNet based on pytorch (Sercan O. Arik, et al. AAAI 2019)](qlib/contrib/model/pytorch_tabnet.py)
- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. ICDM 2020)](qlib/contrib/model/double_ensemble.py)
- [TCTS based on pytorch (Xueqing Wu, et al. ICML 2021)](qlib/contrib/model/pytorch_tcts.py)

Your PR of new Quant models is highly welcomed.

The performance of each model on the `Alpha158` and `Alpha360` dataset can be found [here](examples/benchmarks/README.md).

## 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](examples/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 `qrun` mentioned above to run a model's workflow based from a config file.
- Users can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder.

- Users can use the script [`run_all_model.py`](examples/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](examples/benchmarks/)). For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).

## Run multiple models
`Qlib` also provides a script [`run_all_model.py`](examples/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
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](examples/run_all_model.py). 


# 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](./qlib/contrib/data/handler.py) |  √        |  √           |
| [Alpha158](./qlib/contrib/data/handler.py) |  √        |  √           |

[Here](https://qlib.readthedocs.io/en/latest/advanced/alpha.html) 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](docs/).
[Sphinx](http://www.sphinx-doc.org) and the readthedocs theme is required to build the documentation in html formats. 
```bash
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](http://qlib.readthedocs.io/) online directly.

Qlib is in active and continuing development. Our plan is in the roadmap, which is managed as a [github project](https://github.com/microsoft/qlib/projects/1).



# 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](https://qlib-server.readthedocs.io/). The online mode can be deployed automatically with [Azure CLI based scripts](https://qlib-server.readthedocs.io/en/latest/build.html#one-click-deployment-in-azure). The source code of online data server can be found in [Qlib-Server repository](https://github.com/microsoft/qlib-server).

## 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 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.

# Related Reports
- [【华泰金工林晓明团队】图神经网络选股与Qlib实践——华泰人工智能系列之四十二](https://mp.weixin.qq.com/s/w5fDB6oAv9dO6vlhf1kmhA)
- [Guide To Qlib: Microsoft’s AI Investment Platform](https://analyticsindiamag.com/qlib/)
- [【华泰金工林晓明团队】微软AI量化投资平台Qlib体验——华泰人工智能系列之四十](https://mp.weixin.qq.com/s/Brcd7im4NibJOJzZfMn6tQ)
- [微软也搞AI量化平台？还是开源的！](https://mp.weixin.qq.com/s/47bP5YwxfTp2uTHjUBzJQQ)
- [微矿Qlib：业内首个AI量化投资开源平台](https://mp.weixin.qq.com/s/vsJv7lsgjEi-ALYUz4CvtQ)

# Contact Us
- If you have any issues, please create issue [here](https://github.com/microsoft/qlib/issues/new/choose) or send messages in [gitter](https://gitter.im/Microsoft/qlib).
- If you want to make contributions to `Qlib`, please [create pull requests](https://github.com/microsoft/qlib/compare). 
- For other reasons, you are welcome to contact us by email([qlib@microsoft.com](mailto:qlib@microsoft.com)).
  - We are recruiting new members(both FTEs and interns), your resumes are welcome!

Join IM discussion groups:
|[Gitter](https://gitter.im/Microsoft/qlib)|
|----|
|![image](http://fintech.msra.cn/images_v060/qrcode/gitter_qr.png)|

# 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. Here are some 
[code standards](docs/developer/code_standard.rst) when you submit a pull request.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
