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Improve the style of documentation (#1132)

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This commit improves the documentation (rst files) only in the
following three ways:

* Aligned section headers with their underline/overline punctuation characters

* Deleted all trailling whitespaces in rst files

* Deleted a few trailling newlines at the end of the rst files

Co-authored-by: Bingyao Liu <Bingyao.Liu@sofund.com>
This commit is contained in:
YaOzI
2022-07-07 19:42:27 +08:00
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parent e62684eddf
commit 1dededa33f
29 changed files with 400 additions and 411 deletions
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22
docs/start/getdata.rst
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@@ -1,18 +1,18 @@
.. _getdata:
=============================
==============
Data Retrieval
=============================
==============
.. currentmodule:: qlib
Introduction
====================
============
Users can get stock data with ``Qlib``. The following examples demonstrate the basic user interface.
Examples
====================
========
``QLib`` Initialization:
@@ -30,7 +30,7 @@ If users followed steps in `initialization <initialization.html>`_ and downloade
Load trading calendar with given time range and frequency:
.. code-block:: python
>> from qlib.data import D
>> D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')[:2]
[Timestamp('2010-01-04 00:00:00'), Timestamp('2010-01-05 00:00:00')]
@@ -46,7 +46,7 @@ Parse a given market name into a stock pool config:
Load instruments of certain stock pool in the given time range:
.. code-block:: python
>> from qlib.data import D
>> instruments = D.instruments(market='csi300')
>> D.list_instruments(instruments=instruments, start_time='2010-01-01', end_time='2017-12-31', as_list=True)[:6]
@@ -79,14 +79,14 @@ For more details about filter, please refer `Filter API <../component/data.html>
Load features of certain instruments in a given time range:
.. code-block:: python
>> from qlib.data import D
>> instruments = ['SH600000']
>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
$close $volume Ref($close, 1) Mean($close, 3) $high-$low
instrument datetime
instrument datetime
SH600000 2010-01-04 86.778313 16162960.0 88.825928 88.061483 2.907631
2010-01-05 87.433578 28117442.0 86.778313 87.679273 3.235252
2010-01-06 85.713585 23632884.0 87.433578 86.641825 1.720009
@@ -108,7 +108,7 @@ Load features of certain stock pool in a given time range:
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
$close $volume Ref($close, 1) Mean($close, 3) $high-$low
instrument datetime
instrument datetime
SH600655 2010-01-04 2699.567383 158193.328125 2619.070312 2626.097738 124.580566
2010-01-08 2612.359619 77501.406250 2584.567627 2623.220133 83.373047
2010-01-11 2712.982422 160852.390625 2612.359619 2636.636556 146.621582
@@ -147,5 +147,5 @@ Here is an exmaple which does the same thing as above examples.
API
====================
===
To know more about how to use the Data, go to API Reference: `Data API <../reference/api.html#data>`_

24
docs/start/initialization.rst
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@@ -1,23 +1,23 @@
.. _initialization:
====================
===================
Qlib Initialization
====================
===================
.. currentmodule:: qlib
Initialization
=========================
==============
Please follow the steps below to initialize ``Qlib``.
Download and prepare the Data: execute the following command to download stock data. Please pay `attention` that the data is collected from `Yahoo Finance <https://finance.yahoo.com/lookup>`_ and the data might not be perfect. We recommend users to prepare their own data if they have high-quality datasets. Please refer to `Data <../component/data.html#converting-csv-format-into-qlib-format>`_ for more information about customized dataset.
.. code-block:: bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
Please refer to `Data Preparation <../component/data.html#data-preparation>`_ for more information about `get_data.py`,
@@ -30,7 +30,7 @@ Initialize Qlib before calling other APIs: run following code in python.
from qlib.constant import REG_CN
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
qlib.init(provider_uri=provider_uri, region=REG_CN)
.. note::
Do not import qlib package in the repository directory of ``Qlib``, otherwise, errors may occur.
@@ -56,16 +56,16 @@ The following are several important parameters of `qlib.init` (`Qlib` has a lot
- `redis_port`
Type: int, optional parameter(default: 6379), port of `redis`
.. note::
.. note::
The value of `region` should be aligned with the data stored in `provider_uri`. Currently, ``scripts/get_data.py`` only provides China stock market data. If users want to use the US stock market data, they should prepare their own US-stock data in `provider_uri` and switch to US-stock mode.
.. note::
If Qlib fails to connect redis via `redis_host` and `redis_port`, cache mechanism will not be used! Please refer to `Cache <../component/data.html#cache>`_ for details.
- `exp_manager`
Type: dict, optional parameter, the setting of `experiment manager` to be used in qlib. Users can specify an experiment manager class, as well as the tracking URI for all the experiments. However, please be aware that we only support input of a dictionary in the following style for `exp_manager`. For more information about `exp_manager`, users can refer to `Recorder: Experiment Management <../component/recorder.html>`_.
.. code-block:: Python
# For example, if you want to set your tracking_uri to a <specific folder>, you can initialize qlib below
@@ -78,7 +78,7 @@ The following are several important parameters of `qlib.init` (`Qlib` has a lot
}
})
- `mongo`
Type: dict, optional parameter, the setting of `MongoDB <https://www.mongodb.com/>`_ which will be used in some features such as `Task Management <../advanced/task_management.html>`_, with high performance and clustered processing.
Type: dict, optional parameter, the setting of `MongoDB <https://www.mongodb.com/>`_ which will be used in some features such as `Task Management <../advanced/task_management.html>`_, with high performance and clustered processing.
Users need to follow the steps in `installation <https://www.mongodb.com/try/download/community>`_ to install MongoDB firstly and then access it via a URI.
Users can access mongodb with credential by setting "task_url" to a string like `"mongodb://%s:%s@%s" % (user, pwd, host + ":" + port)`.

11
docs/start/installation.rst
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@@ -1,8 +1,8 @@
.. _installation:
====================
============
Installation
====================
============
.. currentmodule:: qlib
@@ -24,7 +24,7 @@ Also, Users can install ``Qlib`` by the source code according to the following s
- Enter the root directory of ``Qlib``, in which the file ``setup.py`` exists.
- Then, please execute the following command to install the environment dependencies and install ``Qlib``:
.. code-block:: bash
$ pip install numpy
@@ -34,7 +34,7 @@ Also, Users can install ``Qlib`` by the source code according to the following s
.. note::
It's recommended to use anaconda/miniconda to setup the environment. ``Qlib`` needs lightgbm and pytorch packages, use pip to install them.
Use the following code to make sure the installation successful:
@@ -44,6 +44,3 @@ Use the following code to make sure the installation successful:
>>> import qlib
>>> qlib.__version__
<LATEST VERSION>
=====================

22
docs/start/integration.rst
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@@ -1,9 +1,9 @@
=========================================
========================
Custom Model Integration
=========================================
========================
Introduction
===================
============
``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are examples of ``Forecast Model``. In addition to the default models ``Qlib`` provide, users can integrate their own custom models into ``Qlib``.
@@ -14,7 +14,7 @@ Users can integrate their own custom models according to the following steps.
- Test the custom model.
Custom Model Class
===========================
==================
The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#module-qlib.model.base>`_ and override the methods in it.
- Override the `__init__` method
@@ -36,7 +36,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
- The parameters could include some `optional` parameters with default values, such as `num_boost_round = 1000` for `GBDT`.
- Code Example: In the following example, `num_boost_round = 1000` is an optional parameter.
.. code-block:: Python
def fit(self, dataset: DatasetH, num_boost_round = 1000, **kwargs):
# prepare dataset for lgb training and evaluation
@@ -101,14 +101,14 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
)
Configuration File
=======================
==================
The configuration file is described in detail in the `Workflow <../component/workflow.html#complete-example>`_ document. In order to integrate the custom model into ``Qlib``, users need to modify the "model" field in the configuration file. The configuration describes which models to use and how we can initialize it.
- Example: The following example describes the `model` field of configuration file about the custom lightgbm model mentioned above, where `module_path` is the module path, `class` is the class name, and `args` is the hyperparameter passed into the __init__ method. All parameters in the field is passed to `self._params` by `\*\*kwargs` in `__init__` except `loss = mse`.
- Example: The following example describes the `model` field of configuration file about the custom lightgbm model mentioned above, where `module_path` is the module path, `class` is the class name, and `args` is the hyperparameter passed into the __init__ method. All parameters in the field is passed to `self._params` by `\*\*kwargs` in `__init__` except `loss = mse`.
.. code-block:: YAML
model:
class: LGBModel
module_path: qlib.contrib.model.gbdt
@@ -126,7 +126,7 @@ The configuration file is described in detail in the `Workflow <../component/wor
Users could find configuration file of the baselines of the ``Model`` in ``examples/benchmarks``. All the configurations of different models are listed under the corresponding model folder.
Model Testing
=====================
=============
Assuming that the configuration file is ``examples/benchmarks/LightGBM/workflow_config_lightgbm.yaml``, users can run the following command to test the custom model:
.. code-block:: bash
@@ -136,10 +136,10 @@ Assuming that the configuration file is ``examples/benchmarks/LightGBM/workflow_
.. note:: ``qrun`` is a built-in command of ``Qlib``.
Also, ``Model`` can also be tested as a single module. An example has been given in ``examples/workflow_by_code.ipynb``.
Also, ``Model`` can also be tested as a single module. An example has been given in ``examples/workflow_by_code.ipynb``.
Reference
=====================
=========
To know more about ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <../component/model.html>`_ and `Model API <../reference/api.html#module-qlib.model.base>`_.