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* MVP for Indian Stocks in qlib using yahooquery * cleaned with black * cleaned with black * add YahooNormalizeIN and YahooNormalizeIN1d * cleaned the code * added 1min for IN and also updated readme * update comments * fix comments * recorder support upload both raw file and directory * fix comments * Update README.md * Fix docs of QlibRecorder * sort index after loader (#538) make sure the fetch method is based on a index-sorted pd.DataFrame * refactor online serving rolling api * refactor TRA * format by black * fix horizon * fix TRA when use single head * clean up * improve pretrain * update README * fix tra when logdir is None * fix tra when logdir is None * Update strategy.py * Update README.md * Update README.md * Conda Suggestion * code standard docs * Update ensemble.py (#560) * Fix CI Bug (#575) Co-authored-by: yuxwang <anduinnn@foxmail.com> * Update gen.py (#576) * Fix multi-process loop calls (#574) * check lexsort in the 'lazy_sort_index' function (#566) * check lexsort * check lexsort * lexsort comment * lexsort comment * Delete .DS_Store * Update README.md * bug fix & use oracle transport pretrain * mend * Add `backend_freq_config` parameter, support multi-freq uri * Add sample_config to QlibDataLoader, support multi-freq * add multi-freq example * get_cls_kwargs renamed get_callable_kwargs * support multi-freq uri * Add inst_processors to D.features * Fix typo * Fix the index type of the multi-freq example * Fix duplicate mlflow directories in tests * Add DataPathManager to QlibConfig && modify inst_processors to supports list only * Modify the default value in the multi_freq example * Modify client-server mode and dataset-cache to disable inst_processor * Add wheel package to github CI * fix comment * Update FAQ.rst * Update README.md Fix wrong link * Update the docs of TaskManager (#586) * Update manage.py * update yaml * update run_all_model * Modify the Feature to be case sensitive (#589) * update README * remove verbose * fix spell bug * fix typos (#592) * Update Release Note * fix portfolio bug * Add calendar support for resample * add freq kwargs * test.yml: Remove redundant code (#595) * Supporting shared processor (#596) * Supporting shared processor * fix readonly reverse bug * remove pytests dependency * with fit bug * fix parameter error * fix comments * Fix undefined names in Python code (#599) * Update pytorch_tabnet.py $ `flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics` ``` ./qlib/qlib/contrib/model/pytorch_tabnet.py:567:38: F821 undefined name 'inp' self.independ.append(GLU(inp, out_dim, vbs=vbs)) ^ ./qlib/examples/model_rolling/task_manager_rolling.py:75:18: F821 undefined name 'task_train' run_task(task_train, self.task_pool, experiment_name=self.experiment_name) ^ 2 F821 undefined name 'task_train' 2 ``` * Fix undefined names in Python code * from qlib.model.trainer import task_train * update seed * fix some docstring * add comments * Fix SimpleDatasetCache * Update setup.py updated classifiers * Update setup.py change to matplotlib==3.3 * Update python-publish.yml added python 3.9 * updategrade version number * Update model list * fix the type of filter_pipe * fix comment * fix record_temp * update cvxpy version * Update code_standard.rst (#587) * Update code_standard.rst * Update docs/developer/code_standard.rst Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * Add file lock for MLflowExpManager (#619) * fix torch version * Share version number (#620) * Update initialization.rst (#622) * Update initialization.rst * Update docs/start/initialization.rst Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * Update docs/start/initialization.rst Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * fix bugs for running previous exmaple * fix deal amount bug * update change doc (#623) * Add files via upload * Update README.md * Update README.md * Update README.md * Delete change doc.gif * Add files via upload * Update README.md * Delete change doc.gif * Add files via upload * Delete change doc.gif * Add files via upload * Update README.md Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * update doc * simplify run all model * fix run all model bug * Fix Models (#483) * fix gat dataset * fix tft model * Update tft.py * Fix tft.py Co-authored-by: Pengrong Zhu <zhu.pengrong@foxmail.com> * type and skip empty exp * fix model yaml config * fix tft import bug * skip empty result * fix model and yaml bug * fix wrong generate parameter * Modify multi-freq example (#626) * modify the example of multi-freq * add Copyright * add a comment to average_ops.py * modify the example of multi-freq * add comment to multi_freq_handler.py * add the Ref expression description to multi_freq_handler.py * add expression description to multi_freq_handler.py * update images * fix workflow and update framework Co-authored-by: Gaurav <2796gaurav@gmail.com> Co-authored-by: 2796gaurav <17353992+2796gaurav@users.noreply.github.com> Co-authored-by: bxdd <bxd98@126.com> Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: Dong Zhou <Zhou.Dong@microsoft.com> Co-authored-by: ZhangTP1996 <ztp18@mails.tsinghua.edu.cn> Co-authored-by: demon143 <59681577+demon143@users.noreply.github.com> Co-authored-by: Wangwuyi123 <51237097+Wangwuyi123@users.noreply.github.com> Co-authored-by: yuxwang <anduinnn@foxmail.com> Co-authored-by: Pengrong Zhu <zhu.pengrong@foxmail.com> Co-authored-by: Mark Zhao <50850474+markzhao98@users.noreply.github.com> Co-authored-by: cslwqxx <cslwqxx@users.noreply.github.com> Co-authored-by: Dong Zhou <evanzd@users.noreply.github.com> Co-authored-by: SaintMalik <37118134+saintmalik@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Anurag Kumar <mailanu98@gmail.com> Co-authored-by: demon143 <785696300@qq.com>
48 lines
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48 lines
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===============================
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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.svg
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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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`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
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`DataServer` provides high-performance infrastructure for users to manage
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and retrieve raw data. `Trainer` provides flexible interface to control
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the training process of models which enable algorithms controlling the
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training process.
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`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
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`Information Extractor` extracts data for models. `Forecast Model` focuses
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on producing all kinds of forecast signals (e.g. _alpha_, risk) for other
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modules. With these signals `Portfolio Generator` will generate the target
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portfolio and produce orders to be executed by `Order Executor`.
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`Interface` layer `Interface` layer tries to present a user-friendly interface for the underlying
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system. `Analyser` module will provide users detailed analysis reports of
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forecasting signals, portfolios and execution results
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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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