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@@ -11,8 +11,8 @@ Introduction
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The components in `Qlib Framework <../introduction/introduction.html#framework>`_ are designed in a loosely-coupled way. Users could build their own Quant research workflow with these components like `Example <https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py>`_.
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Besides, ``Qlib`` provides more user-friendly interfaces named ``qrun`` to automatically run the whole workflow defined by configuration. A concrete execution of the whole workflow is called an `experiment`.
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With ``qrun``, user can easily run an `experiment`, which includes the following steps:
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Besides, ``Qlib`` provides more user-friendly interfaces named ``qrun`` to automatically run the whole workflow defined by configuration. Running the whole workflow is called an `execution`.
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With ``qrun``, user can easily start an `execution`, which includes the following steps:
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- Data
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- Loading
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@@ -25,7 +25,7 @@ With ``qrun``, user can easily run an `experiment`, which includes the following
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- Forecast signal analysis
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- Backtest
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For each `experiment`, ``Qlib`` has a complete system to tracking all the information as well as artifacts generated during training, inference and evaluation phase. For more information about how Qlib handles `experiment`, please refer to the related document: `Recorder: Experiment Management <../component/recorder.html>`_.
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For each `execution`, ``Qlib`` has a complete system to tracking all the information as well as artifacts generated during training, inference and evaluation phase. For more information about how ``Qlib`` handles this, please refer to the related document: `Recorder: Experiment Management <../component/recorder.html>`_.
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Complete Example
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===================
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@@ -35,8 +35,9 @@ Below is a typical config file of ``qrun``.
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.. code-block:: YAML
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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qlib_init:
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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market: &market csi300
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benchmark: &benchmark SH000300
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data_handler_config: &data_handler_config
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@@ -100,12 +101,16 @@ After saving the config into `configuration.yaml`, users could start the workflo
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.. code-block:: bash
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qrun -c configuration.yaml
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qrun configuration.yaml
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.. note::
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`qrun` will be placed in your $PATH directory when installing ``Qlib``.
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.. note::
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The symbol `&` in `yaml` file stands for an anchor of a field, which is useful when another fields include this parameter as part of the value. Taking the configuration file above as an example, users can directly change the value of `market` and `benchmark` without traversing the entire configuration file.
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Configuration File
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===================
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@@ -114,17 +119,15 @@ Let's get into details of ``qrun`` in this section.
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Before using ``qrun``, users need to prepare a configuration file. The following content shows how to prepare each part of the configuration file.
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Qlib Data Section
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Qlib Init Section
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--------------------
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At first, the configuration file needs to contain several basic parameters about the data, which will be used for qlib initialization, data handling and backtest.
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At first, the configuration file needs to contain several basic parameters which will be used for qlib initialization.
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.. code-block:: YAML
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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market: &market csi300
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benchmark: &benchmark SH000300
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The meaning of each field is as follows:
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@@ -139,34 +142,14 @@ The meaning of each field is as follows:
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The value of `region` should be aligned with the data stored in `provider_uri`.
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- `market`
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Type: str. Index name, the default value is `csi500`.
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- `benchmark`
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Type: str, list or pandas.Series. Stock index symbol, the default value is `SH000905`.
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Task Section
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--------------------
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.. note::
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* If `benchmark` is str, it will use the daily change as the 'bench'.
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* If `benchmark` is list, it will use the daily average change of the stock pool in the list as the 'bench'.
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* If `benchmark` is pandas.Series, whose `index` is trading date and the value T is the change from T-1 to T, it will be directly used as the 'bench'. An example is as following:
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.. code-block:: python
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print(D.features(D.instruments('csi500'), ['$close/Ref($close, 1)-1'])['$close/Ref($close, 1)-1'].head())
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2017-01-04 0.011693
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2017-01-05 0.000721
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2017-01-06 -0.004322
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2017-01-09 0.006874
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2017-01-10 -0.003350
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.. note::
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The symbol `&` in `yaml` file stands for an anchor of a field, which is useful when another fields include this parameter as part of the value. Taking the configuration file above as an example, users can directly change the value of `market` and `benchmark` without traversing the entire configuration file.
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The `task` field in the configuration corresponds to a `task`, which contains the parameters of three different subsections: `Model`, `Dataset` and `Record`.
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Model Section
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--------------------
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~~~~~~~~~~~~~~~~~~~~
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In the `task` field, the `model` section describes the parameters of the model to be used for training and inference. For more information about the base ``Model`` class, please refer to `Qlib Model <../component/model.html>`_.
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@@ -202,7 +185,7 @@ The meaning of each field is as follows:
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``Qlib`` provides a util named: ``init_instance_by_config`` to initialize any class inside ``Qlib`` with the configuration includes the fields: `class`, `module_path` and `kwargs`.
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Dataset Section
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--------------------
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~~~~~~~~~~~~~~~~~~~~
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The `dataset` field describes the parameters for the ``Dataset`` module in ``Qlib`` as well those for the module ``DataHandler``. For more information about the ``Dataset`` module, please refer to `Qlib Model <../component/data.html#dataset>`_.
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@@ -237,9 +220,9 @@ Here is the configuration for the ``Dataset`` module which will take care of dat
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test: [2017-01-01, 2020-08-01]
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Record Section
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--------------------
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~~~~~~~~~~~~~~~~~~~~
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The `record` field is about the parameters the ``Record`` module in ``Qlib``. ``Record`` is responsible for generating certain analysis and evaluation results such as `prediction`, `information Coefficient (IC)` and `backtest`.
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The `record` field is about the parameters the ``Record`` module in ``Qlib``. ``Record`` is responsible for tracking training process and results such as `information Coefficient (IC)` and `backtest` in a standard format.
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The following script is the configuration of `backtest` and the `strategy` used in `backtest`:
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