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| =========== |
| Quick Start |
| =========== |
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| Introduction |
| ============ |
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| This ``Quick Start`` guide tries to demonstrate |
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| - It's very easy to build a complete Quant research workflow and try users' ideas with ``Qlib``. |
| - Though with public data and simple models, machine learning technologies work very well in practical Quant investment. |
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| Installation |
| ============ |
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| Users can easily install ``Qlib`` according to the following steps: |
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| - Before installing ``Qlib`` from source, users need to install some dependencies: |
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| .. code-block:: |
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| pip install numpy |
| pip install --upgrade cython |
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| - Clone the repository and install ``Qlib`` |
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| .. code-block:: |
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| git clone https://github.com/microsoft/qlib.git && cd qlib |
| python setup.py install |
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| To known more about `installation`, please refer to `Qlib Installation <../start/installation.html>`_. |
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| Prepare Data |
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| Load and prepare data by running the following code: |
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| .. code-block:: |
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| python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn |
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| This dataset is created by public data collected by crawler scripts in ``scripts/data_collector/``, which have been released in the same repository. Users could create the same dataset with it. |
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| To known more about `prepare data`, please refer to `Data Preparation <../component/data.html#data-preparation>`_. |
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| Auto Quant Research Workflow |
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| ``Qlib`` provides a tool named ``qrun`` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). Users can start an auto quant research workflow and have a graphical reports analysis according to the following steps: |
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| - Quant Research Workflow: |
| - Run ``qrun`` with a config file of the LightGBM model `workflow_config_lightgbm.yaml` as following. |
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| cd examples # Avoid running program under the directory contains `qlib` |
| qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml |
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| - Workflow result |
| The result of ``qrun`` is as follows, which is also the typical result of ``Forecast model(alpha)``. Please refer to `Intraday Trading <../component/backtest.html>`_. for more details about the result. |
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| .. code-block:: python |
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| risk |
| excess_return_without_cost mean 0.000605 |
| std 0.005481 |
| annualized_return 0.152373 |
| information_ratio 1.751319 |
| max_drawdown -0.059055 |
| excess_return_with_cost mean 0.000410 |
| std 0.005478 |
| annualized_return 0.103265 |
| information_ratio 1.187411 |
| max_drawdown -0.075024 |
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| To know more about `workflow` and `qrun`, please refer to `Workflow: Workflow Management <../component/workflow.html>`_. |
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| - Graphical Reports Analysis: |
| - Run ``examples/workflow_by_code.ipynb`` with jupyter notebook |
| Users can have portfolio analysis or prediction score (model prediction) analysis by run ``examples/workflow_by_code.ipynb``. |
| - Graphical Reports |
| Users can get graphical reports about the analysis, please refer to `Analysis: Evaluation & Results Analysis <../component/report.html>`_ for more details. |
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| Custom Model Integration |
| ======================== |
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| ``Qlib`` provides a batch of models (such as ``lightGBM`` and ``MLP`` models) as examples of ``Forecast Model``. In addition to the default model, users can integrate their own custom models into ``Qlib``. If users are interested in the custom model, please refer to `Custom Model Integration <../start/integration.html>`_. |
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