| .. _workflow: |
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| ============================= |
| Workflow: Workflow Management |
| ============================= |
| .. currentmodule:: qlib |
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| 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. Running the whole workflow is called an `execution`. |
| With ``qrun``, user can easily start an `execution`, which includes the following steps: |
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| - Data |
| - Loading |
| - Processing |
| - Slicing |
| - Model |
| - Training and inference |
| - Saving & loading |
| - Evaluation |
| - Forecast signal analysis |
| - Backtest |
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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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| Before getting into details, here is a complete example of ``qrun``, which defines the workflow in typical Quant research. |
| Below is a typical config file of ``qrun``. |
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| .. code-block:: YAML |
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| qlib_init: |
| provider_uri: "~/.qlib/qlib_data/cn_data" |
| region: cn |
| market: &market csi300 |
| benchmark: &benchmark SH000300 |
| data_handler_config: &data_handler_config |
| start_time: 2008-01-01 |
| end_time: 2020-08-01 |
| fit_start_time: 2008-01-01 |
| fit_end_time: 2014-12-31 |
| instruments: *market |
| port_analysis_config: &port_analysis_config |
| strategy: |
| class: TopkDropoutStrategy |
| module_path: qlib.contrib.strategy.strategy |
| kwargs: |
| topk: 50 |
| n_drop: 5 |
| signal: <PRED> |
| backtest: |
| start_time: 2017-01-01 |
| end_time: 2020-08-01 |
| account: 100000000 |
| benchmark: *benchmark |
| exchange_kwargs: |
| limit_threshold: 0.095 |
| deal_price: close |
| open_cost: 0.0005 |
| close_cost: 0.0015 |
| min_cost: 5 |
| task: |
| model: |
| class: LGBModel |
| module_path: qlib.contrib.model.gbdt |
| kwargs: |
| loss: mse |
| colsample_bytree: 0.8879 |
| learning_rate: 0.0421 |
| subsample: 0.8789 |
| lambda_l1: 205.6999 |
| lambda_l2: 580.9768 |
| max_depth: 8 |
| num_leaves: 210 |
| num_threads: 20 |
| dataset: |
| class: DatasetH |
| module_path: qlib.data.dataset |
| kwargs: |
| handler: |
| class: Alpha158 |
| module_path: qlib.contrib.data.handler |
| kwargs: *data_handler_config |
| segments: |
| train: [2008-01-01, 2014-12-31] |
| valid: [2015-01-01, 2016-12-31] |
| test: [2017-01-01, 2020-08-01] |
| record: |
| - class: SignalRecord |
| module_path: qlib.workflow.record_temp |
| kwargs: {} |
| - class: PortAnaRecord |
| module_path: qlib.workflow.record_temp |
| kwargs: |
| config: *port_analysis_config |
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| After saving the config into `configuration.yaml`, users could start the workflow and test their ideas with a single command below. |
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| .. code-block:: bash |
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| qrun configuration.yaml |
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| If users want to use ``qrun`` under debug mode, please use the following command: |
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| .. code-block:: bash |
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| python -m pdb qlib/cli/run.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.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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| Let's get into details of ``qrun`` in this section. |
| 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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| The design logic of the configuration file is very simple. It predefines fixed workflows and provide this yaml interface to users to define how to initialize each component. |
| It follow the design of `init_instance_by_config <https://github.com/microsoft/qlib/blob/2aee9e0145decc3e71def70909639b5e5a6f4b58/qlib/utils/__init__.py#L264>`_ . It defines the initialization of each component of Qlib, which typically include the class and the initialization arguments. |
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| For example, the following yaml and code are equivalent. |
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| .. code-block:: YAML |
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| model: |
| class: LGBModel |
| module_path: qlib.contrib.model.gbdt |
| kwargs: |
| loss: mse |
| colsample_bytree: 0.8879 |
| learning_rate: 0.0421 |
| subsample: 0.8789 |
| lambda_l1: 205.6999 |
| lambda_l2: 580.9768 |
| max_depth: 8 |
| num_leaves: 210 |
| num_threads: 20 |
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| .. code-block:: python |
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| from qlib.contrib.model.gbdt import LGBModel |
| kwargs = { |
| "loss": "mse" , |
| "colsample_bytree": 0.8879, |
| "learning_rate": 0.0421, |
| "subsample": 0.8789, |
| "lambda_l1": 205.6999, |
| "lambda_l2": 580.9768, |
| "max_depth": 8, |
| "num_leaves": 210, |
| "num_threads": 20, |
| } |
| LGBModel(kwargs) |
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| Qlib Init Section |
| ----------------- |
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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" |
| region: cn |
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| The meaning of each field is as follows: |
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| - `provider_uri` |
| Type: str. The URI of the Qlib data. For example, it could be the location where the data loaded by ``get_data.py`` are stored. |
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| - `region` |
| - If `region` == "us", ``Qlib`` will be initialized in US-stock mode. |
| - If `region` == "cn", ``Qlib`` will be initialized in China-stock mode. |
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| .. note:: |
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| The value of `region` should be aligned with the data stored in `provider_uri`. |
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| Task Section |
| ------------ |
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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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| 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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| .. code-block:: YAML |
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| model: |
| class: LGBModel |
| module_path: qlib.contrib.model.gbdt |
| kwargs: |
| loss: mse |
| colsample_bytree: 0.8879 |
| learning_rate: 0.0421 |
| subsample: 0.8789 |
| lambda_l1: 205.6999 |
| lambda_l2: 580.9768 |
| max_depth: 8 |
| num_leaves: 210 |
| num_threads: 20 |
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| The meaning of each field is as follows: |
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| - `class` |
| Type: str. The name for the model class. |
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| - `module_path` |
| Type: str. The path for the model in qlib. |
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| - `kwargs` |
| The keywords arguments for the model. Please refer to the specific model implementation for more information: `models <https://github.com/microsoft/qlib/blob/main/qlib/contrib/model>`_. |
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| .. note:: |
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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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| 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 Data <../component/data.html#dataset>`_. |
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| The keywords arguments configuration of the ``DataHandler`` is as follows: |
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| .. code-block:: YAML |
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| data_handler_config: &data_handler_config |
| start_time: 2008-01-01 |
| end_time: 2020-08-01 |
| fit_start_time: 2008-01-01 |
| fit_end_time: 2014-12-31 |
| instruments: *market |
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| Users can refer to the document of `DataHandler <../component/data.html#datahandler>`_ for more information about the meaning of each field in the configuration. |
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| Here is the configuration for the ``Dataset`` module which will take care of data preprocessing and slicing during the training and testing phase. |
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| .. code-block:: YAML |
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| dataset: |
| class: DatasetH |
| module_path: qlib.data.dataset |
| kwargs: |
| handler: |
| class: Alpha158 |
| module_path: qlib.contrib.data.handler |
| kwargs: *data_handler_config |
| segments: |
| train: [2008-01-01, 2014-12-31] |
| valid: [2015-01-01, 2016-12-31] |
| test: [2017-01-01, 2020-08-01] |
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| Record Section |
| ~~~~~~~~~~~~~~ |
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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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| .. code-block:: YAML |
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| port_analysis_config: &port_analysis_config |
| strategy: |
| class: TopkDropoutStrategy |
| module_path: qlib.contrib.strategy.strategy |
| kwargs: |
| topk: 50 |
| n_drop: 5 |
| signal: <PRED> |
| backtest: |
| limit_threshold: 0.095 |
| account: 100000000 |
| benchmark: *benchmark |
| deal_price: close |
| open_cost: 0.0005 |
| close_cost: 0.0015 |
| min_cost: 5 |
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| For more information about the meaning of each field in configuration of `strategy` and `backtest`, users can look up the documents: `Strategy <../component/strategy.html>`_ and `Backtest <../component/backtest.html>`_. |
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| Here is the configuration details of different `Record Template` such as ``SignalRecord`` and ``PortAnaRecord``: |
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| .. code-block:: YAML |
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| record: |
| - class: SignalRecord |
| module_path: qlib.workflow.record_temp |
| kwargs: {} |
| - class: PortAnaRecord |
| module_path: qlib.workflow.record_temp |
| kwargs: |
| config: *port_analysis_config |
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| For more information about the ``Record`` module in ``Qlib``, user can refer to the related document: `Record <../component/recorder.html#record-template>`_. |
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