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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>
125 lines
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125 lines
5.7 KiB
ReStructuredText
.. _getdata:
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=============================
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Data Retrieval
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=============================
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.. currentmodule:: qlib
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Introduction
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====================
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Users can get stock data with ``Qlib``. The following examples demonstrate the basic user interface.
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Examples
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====================
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``QLib`` Initialization:
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.. note:: In order to get the data, users need to initialize ``Qlib`` with `qlib.init` first. Please refer to `initialization <initialization.html>`_.
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If users followed steps in `initialization <initialization.html>`_ and downloaded the data, they should use the following code to initialize qlib
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.. code-block:: python
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>> import qlib
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>> qlib.init(provider_uri='~/.qlib/qlib_data/cn_data')
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Load trading calendar with given time range and frequency:
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.. code-block:: python
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>> from qlib.data import D
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>> D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')[:2]
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[Timestamp('2010-01-04 00:00:00'), Timestamp('2010-01-05 00:00:00')]
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Parse a given market name into a stock pool config:
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.. code-block:: python
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>> from qlib.data import D
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>> D.instruments(market='all')
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{'market': 'all', 'filter_pipe': []}
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Load instruments of certain stock pool in the given time range:
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.. code-block:: python
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>> from qlib.data import D
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>> instruments = D.instruments(market='csi300')
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>> D.list_instruments(instruments=instruments, start_time='2010-01-01', end_time='2017-12-31', as_list=True)[:6]
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['SH600036', 'SH600110', 'SH600087', 'SH600900', 'SH600089', 'SZ000912']
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Load dynamic instruments from a base market according to a name filter
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.. code-block:: python
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>> from qlib.data import D
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>> from qlib.data.filter import NameDFilter
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>> nameDFilter = NameDFilter(name_rule_re='SH[0-9]{4}55')
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>> instruments = D.instruments(market='csi300', filter_pipe=[nameDFilter])
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>> D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)
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['SH600655', 'SH601555']
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Load dynamic instruments from a base market according to an expression filter
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.. code-block:: python
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>> from qlib.data import D
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>> from qlib.data.filter import ExpressionDFilter
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>> expressionDFilter = ExpressionDFilter(rule_expression='$close>2000')
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>> instruments = D.instruments(market='csi300', filter_pipe=[expressionDFilter])
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>> D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)
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['SZ000651', 'SZ000002', 'SH600655', 'SH600570']
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For more details about filter, please refer `Filter API <../component/data.html>`_.
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Load features of certain instruments in a given time range:
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.. code-block:: python
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>> from qlib.data import D
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>> instruments = ['SH600000']
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>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
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>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
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$close $volume Ref($close, 1) Mean($close, 3) $high-$low
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instrument datetime
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SH600000 2010-01-04 86.778313 16162960.0 88.825928 88.061483 2.907631
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2010-01-05 87.433578 28117442.0 86.778313 87.679273 3.235252
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2010-01-06 85.713585 23632884.0 87.433578 86.641825 1.720009
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2010-01-07 83.788803 20813402.0 85.713585 85.645322 3.030487
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2010-01-08 84.730675 16044853.0 83.788803 84.744354 2.047623
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Load features of certain stock pool in a given time range:
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.. note:: With cache enabled, the qlib data server will cache data all the time for the requested stock pool and fields, it may take longer to process the request for the first time than that without cache. But after the first time, requests with the same stock pool and fields will hit the cache and be processed faster even the requested time period changes.
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.. code-block:: python
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>> from qlib.data import D
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>> from qlib.data.filter import NameDFilter, ExpressionDFilter
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>> nameDFilter = NameDFilter(name_rule_re='SH[0-9]{4}55')
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>> expressionDFilter = ExpressionDFilter(rule_expression='$close>Ref($close,1)')
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>> instruments = D.instruments(market='csi300', filter_pipe=[nameDFilter, expressionDFilter])
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>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
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>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
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$close $volume Ref($close, 1) Mean($close, 3) $high-$low
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instrument datetime
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SH600655 2010-01-04 2699.567383 158193.328125 2619.070312 2626.097738 124.580566
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2010-01-08 2612.359619 77501.406250 2584.567627 2623.220133 83.373047
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2010-01-11 2712.982422 160852.390625 2612.359619 2636.636556 146.621582
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2010-01-12 2788.688232 164587.937500 2712.982422 2704.676758 128.413818
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2010-01-13 2790.604004 145460.453125 2788.688232 2764.091553 128.413818
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For more details about features, please refer `Feature API <../component/data.html>`_.
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.. note:: When calling `D.features()` at the client, use parameter `disk_cache=0` to skip dataset cache, use `disk_cache=1` to generate and use dataset cache. In addition, when calling at the server, users can use `disk_cache=2` to update the dataset cache.
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API
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====================
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To know more about how to use the Data, go to API Reference: `Data API <../reference/api.html#data>`_
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