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* init commit * change the version number * rich the docs&fix cache docs * update index readme * Modify cache class name * Modify sharpe to information_ratio * Modify Group- to Group * add the description of graphical results & fix the backtest docs * fix docs in details * update docs * Update introduction.rst * Update README.md * Update introduction.rst * Update introduction.rst * Update introduction.rst * Update installation.rst * Update installation.rst * Update initialization.rst * Update getdata.rst * Update integration.rst * Update initialization.rst * Update getdata.rst * Update estimator.rst Modify some typos. * Update README.md Modify the typos. * Update initialization.rst * Update data.rst * Update report.rst * Update estimator.rst * Update cumulative_return.py * Update model.rst * Update rank_label.py * Update cumulative_return.py * Update strategy.rst * Update getdata.rst * Update backtest.rst * Update integration.rst * Update getdata.rst * Update introduction.rst * Update introduction.rst * Update README.md * Update report.rst * Update integration.rst Fix typos * Update installation.rst Fix typos * Update getdata.rst * Update initialization.rst Fix typos. * add quick start docs&fix detials * fix estimator docs & fix strategy docs * fix the cahce in data.rst * update documents * Fix Corr && Rsquare * fix data retrival example to csi300 & fix a data bug * fix filter bug * Fix data collector * Modift model args * add the log & fix README.md\quick.rst * add enviroment depend & add intoduction of qlib-server online mode * fix image center fomat & set log_only of docs is True * fix README.md format * update data preparation & readme logo image * get_data support version * Modify analysis names * Modify analysis graph * update report.rst & data.rst * commmit estimator for merge * minimal requirements * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update READEME.md * Update READEME.md * update estimator * Fix doc urls * fix get_data.py docstring * update test_get_data.py * Upate docs * Upate docs * Upate docs Co-authored-by: bxdd <bxddream@gmail.com> Co-authored-by: zhupr <zhu.pengrong@foxmail.com> Co-authored-by: Wendi Li <wendili.academic@qq.com> Co-authored-by: Dingsu Wang <dingsu.wang@gmail.com> Co-authored-by: bxdd <45119470+bxdd@users.noreply.github.com> Co-authored-by: cslwqxx <cslwqxx@users.noreply.github.com>
94 lines
3.9 KiB
ReStructuredText
94 lines
3.9 KiB
ReStructuredText
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===============================
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Quick Start
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===============================
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Introduction
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==============
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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``.
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- 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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==================
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Users can easily intsall ``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
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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
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python setup.py install
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To kown more about `installation`, please refer to `Qlib Installation <../start/installation.html>`_.
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Prepare Data
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==============
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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_cn --target_dir ~/.qlib/qlib_data/cn_data
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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 kown more about `prepare data`, please refer to `Data Preparation <../component/data.html>`_.
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Auto Quant Research Workflow
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====================================
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``Qlib`` provides a tool named ``Estimator`` 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:
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- Run ``Estimator`` with `estimator_config.yaml` as following.
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.. code-block::
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cd examples # Avoid running program under the directory contains `qlib`
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estimator -c estimator/estimator_config.yaml
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- Estimator result
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The result of ``Estimator`` is as follows, which is also the result of ``Interday Trading``. Please refer to please refer to `Interdat Trading <../component/backtest.html>`_. for more details about the result.
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.. code-block:: python
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risk
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excess_return_without_cost mean 0.000605
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std 0.005481
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annualized_return 0.152373
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information_ratio 1.751319
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max_drawdown -0.059055
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excess_return_with_cost mean 0.000410
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std 0.005478
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annualized_return 0.103265
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information_ratio 1.187411
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max_drawdown -0.075024
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To know more about `Estimator`, please refer to `Estimator <../component/estimator.html>`_.
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- Graphical Reports Analysis:
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- Run ``examples/estimator/analyze_from_estimator.ipynb`` with jupyter notebook
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Users can have portfolio analysis or prediction score (model prediction) analysis by run ``examples/estimator/analyze_from_estimator.ipynb``.
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- Graphical Reports
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Users can get graphical reports about the analysis, please refer to `Aanalysis: Evaluation & Results Analysis <../component/report.html>`_ for more details.
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Custom Model Integration
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===============================================
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``Qlib`` provides ``lightGBM`` and ``Dnn`` model as the baseline of ``Interday 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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