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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>
48 lines
2.4 KiB
ReStructuredText
48 lines
2.4 KiB
ReStructuredText
===============================
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``Qlib``: Quantitative Platform
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===============================
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Introduction
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===================
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.. image:: ../_static/img/logo/white_bg_rec+word.png
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:align: center
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``Qlib`` is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
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With ``Qlib``, users can easily try their ideas to create better Quant investment strategies.
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Framework
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===================
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.. image:: ../_static/img/framework.png
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:align: center
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At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone.
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====================== ==============================================================================
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Name Description
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====================== ==============================================================================
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`Data layer` `DataServer` focuses on providing high-performance infrastructure for users to
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manage and retrieve raw data. `DataEnhancement` will preprocess the data and
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provide the best dataset to be fed into the models.
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`Interday Model` `Interday model` focuses on producing prediction scores (aka. `alpha`). Models
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are trained by `Model Creator` and managed by `Model Manager`. Users could
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choose one or multiple models for prediction. Multiple models could be combined
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with `Ensemble` module.
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`Interday Strategy` `Portfolio Generator` will take prediction scores as input and output the
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orders based on the current position to achieve the target portfolio.
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`Intraday Trading` `Order Executor` is responsible for executing orders output by
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`Interday Strategy` and returning the executed results.
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`Analysis` Users could get a detailed analysis report of forecasting signals and portfolios
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in this part.
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====================== ==============================================================================
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- The modules with hand-drawn style are under development and will be released in the future.
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- The modules with dashed borders are highly user-customizable and extendible.
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