=========== 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>_.