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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
committed by GitHub
parent e62684eddf
commit 1dededa33f
29 changed files with 400 additions and 411 deletions
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10
docs/hidden/client.rst
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@@ -1,12 +1,12 @@
.. _client:
Qlib Client-Server Framework
===================
============================
.. currentmodule:: qlib
Introduction
-----------
------------
Client-Server is designed to solve following problems
- Manage the data in a centralized way. Users don't have to manage data of different versions.
@@ -159,13 +159,11 @@ Limitations
2. The rolling operation expression with parameter `0` can not be updated rightly under mechanism of the client-server framework.
API
********************
***
The client is based on `python-socketio<https://python-socketio.readthedocs.io>`_ which is a framework that supports WebSocket client for Python language. The client can only propose requests and receive results, which do not include any calculating procedure.
Class
--------------------
-----
.. automodule:: qlib.data.client

20
docs/hidden/online.rst
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@@ -1,11 +1,11 @@
.. _online:
Online
===================
======
.. currentmodule:: qlib
Introduction
-------------------
------------
Welcome to use Online, this module simulates what will be like if we do the real trading use our model and strategy.
@@ -31,11 +31,11 @@ The file structure can be viewed at fileStruct_.
Example
-------------------
-------
Let's take an example,
.. note:: Make sure you have the latest version of `qlib` installed.
.. note:: Make sure you have the latest version of `qlib` installed.
If you want to use the models and data provided by `qlib`, you only need to do as follows.
@@ -93,7 +93,7 @@ If Your account was saved in "./user_data/", you can see the performance of your
Here 'SH000905' represents csi500 and 'SH000300' represents csi300
Manage your account
--------------------
-------------------
Any account processed by `online` should be saved in a folder. you can use commands
defined to manage your accounts.
@@ -161,7 +161,7 @@ be called at each trading date.
>> online update -date 2019-10-16 -path ./user_data/
API
------------------
---
All those operations are based on defined in `qlib.contrib.online.operator`
@@ -170,7 +170,7 @@ All those operations are based on defined in `qlib.contrib.online.operator`
.. _fileStruct:
File structure
------------------
--------------
'user_data' indicates the root of folder.
Name that bold indicates it’s a folder, otherwise it’s a document.
@@ -214,7 +214,7 @@ Configuration file
The configure file used in `online` should contain the model and strategy information.
About the model
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~
First, your configuration file needs to have a field about the model,
this field and its contents determine the model we used when generating score at predict date.
@@ -243,7 +243,7 @@ contains 2 methods used in `online` module.
About the strategy
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~
Your need define the strategy used to generate the order list at predict date.
@@ -259,7 +259,7 @@ Followings are two examples for a TopkAmountStrategy
n_drop: 10
Generated files
------------------
---------------
The 'online_generate' command will create the order list at {folder_path}/{user_id}/temp/,
the name of that is orderlist_{YYYY-MM-DD}.json, YYYY-MM-DD is the date that those orders to be executed.

41
docs/hidden/tuner.rst
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@@ -1,11 +1,11 @@
.. _tuner:
Tuner
===================
=====
.. currentmodule:: qlib
Introduction
-------------------
------------
Welcome to use Tuner, this document is based on that you can use Estimator proficiently and correctly.
@@ -41,19 +41,19 @@ We write a simple configuration example as following,
tuner_class: QLibTuner
qlib_client:
auto_mount: False
logging_level: INFO
logging_level: INFO
optimization_criteria:
report_type: model
report_factor: model_score
optim_type: max
tuner_pipeline:
-
model:
-
model:
class: SomeModel
space: SomeModelSpace
trainer:
trainer:
class: RollingTrainer
strategy:
strategy:
class: TopkAmountStrategy
space: TopkAmountStrategySpace
max_evals: 2
@@ -166,13 +166,13 @@ Also, there are some optional fields. The meaning of each field is as follows:
The class of tuner, str type, must be an already implemented model, such as `QLibTuner` in `qlib`, or a custom tuner, but it must be a subclass of `qlib.contrib.tuner.Tuner`, the default value is `QLibTuner`.
- `tuner_module_path`
The module path, str type, absolute url is also supported, indicates the path of the implementation of tuner. The default value is `qlib.contrib.tuner.tuner`
The module path, str type, absolute url is also supported, indicates the path of the implementation of tuner. The default value is `qlib.contrib.tuner.tuner`
About the optimization criteria
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You need to designate a factor to optimize, for tuner need a factor to decide which case is better than other cases.
Usually, we use the result of `estimator`, such as backtest results and the score of model.
Usually, we use the result of `estimator`, such as backtest results and the score of model.
This part needs contain these fields:
@@ -203,13 +203,13 @@ The tuner pipeline contains different tuners, and the `tuner` program will proce
.. code-block:: YAML
tuner_pipeline:
-
model:
-
model:
class: SomeModel
space: SomeModelSpace
trainer:
trainer:
class: RollingTrainer
strategy:
strategy:
class: TopkAmountStrategy
space: TopkAmountStrategySpace
max_evals: 2
@@ -249,25 +249,25 @@ You need to use the same dataset to evaluate your different `estimator` experime
test_start_date: 2016-07-01
test_end_date: 2018-04-30
- `rolling_period`
- `rolling_period`
The rolling period, integer type, indicates how many time steps need rolling when rolling the data. The default value is `60`. If you use `RollingTrainer`, this config will be used, or it will be ignored.
- `train_start_date`
Training start time, str type.
- `train_end_date`
- `train_end_date`
Training end time, str type.
- `validate_start_date`
- `validate_start_date`
Validation start time, str type.
- `validate_end_date`
- `validate_end_date`
Validation end time, str type.
- `test_start_date`
- `test_start_date`
Test start time, str type.
- `test_end_date`
- `test_end_date`
Test end time, str type. If `test_end_date` is `-1` or greater than the last date of the data, the last date of the data will be used as `test_end_date`.
About the data and backtest
@@ -315,11 +315,10 @@ About the data and backtest
Experiment Result
-----------------
All the results are stored in experiment file directly, you can check them directly in the corresponding files.
All the results are stored in experiment file directly, you can check them directly in the corresponding files.
What we save are as following:
- Global optimal parameters
- Local optimal parameters of each tuner
- Config file of this `tuner` experiment
- Every `estimator` experiments result in the process