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===========
Quick Start
===========
Introduction
============
This ``Quick Start`` guide tries to demonstrate
- 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.
Installation
============
Users can easily install ``Qlib`` according to the following steps:
- Before installing ``Qlib`` from source, users need to install some dependencies:
.. code-block::
pip install numpy
pip install --upgrade cython
- Clone the repository and install ``Qlib``
.. code-block::
git clone https://github.com/microsoft/qlib.git && cd qlib
python setup.py install
To known more about `installation`, please refer to `Qlib Installation <../start/installation.html>`_.
Prepare Data
============
Load and prepare data by running the following code:
.. code-block::
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 in ``scripts/data_collector/``, which have been released in the same repository. Users could create the same dataset with it.
To known more about `prepare data`, please refer to `Data Preparation <../component/data.html#data-preparation>`_.
Auto Quant Research Workflow
============================
``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:
- Quant Research Workflow:
- Run ``qrun`` with a config file of the LightGBM model `workflow_config_lightgbm.yaml` as following.
.. code-block::
cd examples # Avoid running program under the directory contains `qlib`
qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml
- 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.
.. code-block:: python
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
To know more about `workflow` and `qrun`, please refer to `Workflow: Workflow Management <../component/workflow.html>`_.
- 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.
Custom Model Integration
========================
``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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