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119 lines
4.4 KiB
ReStructuredText
119 lines
4.4 KiB
ReStructuredText
.. _model:
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============================================
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Forecast Model: Model Training & Prediction
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============================================
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Introduction
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===================
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``Forecast Model`` is designed to make the `prediction score` about stocks. Users can use the ``Forecast Model`` in an automatic workflow by ``qrun``, please refer to `Workflow: Workflow Management <workflow.html>`_.
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Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Forecast Model`` can be used as an independent module also.
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Base Class & Interface
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======================
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``Qlib`` provides a base class `qlib.model.base.Model <../reference/api.html#module-qlib.model.base>`_ from which all models should inherit.
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The base class provides the following interfaces:
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.. autoclass:: qlib.model.base.Model
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:members:
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``Qlib`` also provides a base class `qlib.model.base.ModelFT <../reference/api.html#qlib.model.base.ModelFT>`_, which includes the method for finetuning the model.
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For other interfaces such as `finetune`, please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.
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Example
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==================
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``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are treated as the baselines of ``Forecast Model``. The following steps show how to run`` LightGBM`` as an independent module.
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- Initialize ``Qlib`` with `qlib.init` first, please refer to `Initialization <../start/initialization.html>`_.
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- Run the following code to get the `prediction score` `pred_score`
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.. code-block:: Python
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from qlib.contrib.model.gbdt import LGBModel
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from qlib.contrib.data.handler import Alpha158
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from qlib.utils import init_instance_by_config, flatten_dict
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
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market = "csi300"
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benchmark = "SH000300"
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data_handler_config = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": market,
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}
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task = {
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"model": {
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"class": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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"kwargs": {
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"loss": "mse",
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"colsample_bytree": 0.8879,
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"learning_rate": 0.0421,
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"subsample": 0.8789,
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"lambda_l1": 205.6999,
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"lambda_l2": 580.9768,
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"max_depth": 8,
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"num_leaves": 210,
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"num_threads": 20,
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},
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},
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"dataset": {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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"handler": {
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"class": "Alpha158",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": data_handler_config,
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},
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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},
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},
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}
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# model initiaiton
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model = init_instance_by_config(task["model"])
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dataset = init_instance_by_config(task["dataset"])
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# start exp
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with R.start(experiment_name="workflow"):
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# train
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R.log_params(**flatten_dict(task))
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model.fit(dataset)
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# prediction
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recorder = R.get_recorder()
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sr = SignalRecord(model, dataset, recorder)
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sr.generate()
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.. note::
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`Alpha158` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
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`SignalRecord` is the `Record Template` in ``Qlib``, please refer to `Workflow <recorder.html#record-template>`_.
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Also, the above example has been given in ``examples/train_backtest_analyze.ipynb``.
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Custom Model
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===================
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Qlib supports custom models. If users are interested in customizing their own models and integrating the models into ``Qlib``, please refer to `Custom Model Integration <../start/integration.html>`_.
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API
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===================
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Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.
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