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Qlib Workflow for Model Training, Backtesting, and Portfolio Analysis

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Summary

This guide outlines an end-to-end quantitative research workflow using Qlib. It describes installing the library, preparing Chinese market data from public sources, and running an example LightGBM configuration with the qrun tool. That workflow combines dataset construction, model training, backtesting, and evaluation. A notebook is also suggested for examining portfolio results and prediction scores, and the guide notes that researchers can integrate custom models alongside example models such as LightGBM and MLP.

The example reports excess returns and risk statistics both before and after costs, including annualized return, information ratio, and maximum drawdown. These figures illustrate how the workflow presents results; they are not evidence that the model will generalize or remain profitable. The example uses a particular public dataset and configuration, and the guide gives little detail on data quality, point-in-time correctness, transaction-cost assumptions, or validation design. Researchers should inspect those choices and test for leakage and robustness before interpreting the reported backtest as investable performance.

Key ideas

  • Qlib's qrun can coordinate dataset creation, model fitting, backtesting, and evaluation.
  • The guide demonstrates the workflow with public Chinese market data and a LightGBM example.
  • A notebook can be used to inspect portfolio behavior and prediction scores.
  • Qlib supports integration of custom forecasting models.
  • Example backtest metrics are configuration-specific and do not establish future performance.

Tags

Machine learningEquitiesBacktestingPortfolio construction
Full text
# quick



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

Shown in full with attribution under the source's licence. Licence: MIT

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.