Training and Using Forecast Models for Stock Ranking in Qlib

# model


.. _model:

===========================================
Forecast Model: Model Training & Prediction
===========================================

Introduction
============

``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>`_.

Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Forecast Model`` can be used as an independent module also.

Base Class & Interface
======================

``Qlib`` provides a base class `qlib.model.base.Model <../reference/api.html#module-qlib.model.base>`_ from which all models should inherit.

The base class provides the following interfaces:

.. autoclass:: qlib.model.base.Model
    :members:
    :noindex:

``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.

For other interfaces such as `finetune`, please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.

Example
=======

``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.

- Initialize ``Qlib`` with `qlib.init` first, please refer to `Initialization <../start/initialization.html>`_.
- Run the following code to get the `prediction score` `pred_score`
    .. code-block:: Python

        from qlib.contrib.model.gbdt import LGBModel
        from qlib.contrib.data.handler import Alpha158
        from qlib.utils import init_instance_by_config, flatten_dict
        from qlib.workflow import R
        from qlib.workflow.record_temp import SignalRecord, PortAnaRecord

        market = "csi300"
        benchmark = "SH000300"

        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,
        }

        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"),
                    },
                },
            },
        }

        # model initialization
        model = init_instance_by_config(task["model"])
        dataset = init_instance_by_config(task["dataset"])

        # start exp
        with R.start(experiment_name="workflow"):
            # train
            R.log_params(**flatten_dict(task))
            model.fit(dataset)

            # prediction
            recorder = R.get_recorder()
            sr = SignalRecord(model, dataset, recorder)
            sr.generate()

    .. note::

        `Alpha158` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
        `SignalRecord` is the `Record Template` in ``Qlib``, please refer to `Workflow <recorder.html#record-template>`_.

Also, the above example has been given in ``examples/train_backtest_analyze.ipynb``.
Technically, the meaning of the model prediction depends on the label setting designed by user.
By default, the meaning of the score is normally the rating of the instruments by the forecasting model. The higher the score, the more profit the instruments.

Reloading a saved model
=======================

Training and prediction with in-memory objects, as above, do not require a recorder
trust grant. Reloading a saved model or dataset may execute Python code, however.
For artifacts from a verified writer in an access-controlled store, explicitly use
``recorder.load_object("params.pkl", trusted=True)`` (or the corresponding ``R``
facade). Keep prediction and label data reads restricted by omitting ``trusted``.
Do not automatically retry a refused load with unrestricted deserialization.

Workflows that resume training can configure ``DelayTrainerR`` or
``DelayTrainerRM`` with ``trusted=True`` at construction, rather than
changing internal loads. See :ref:`artifact_loading_migration` for migration details,
version limitations and the separate trust requirements of local pickle files.


Custom Model
============

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>`_.

See :ref:`config_migration` when upgrading file-based models: each ``.py``
component needs its own top-level boolean ``trusted`` declaration, while
package imports remain available. The guide also covers trusted older
file-model pickles and registering custom built-in TRA backbones through
``qlib.contrib.model.pytorch_tra.MODEL_TYPES`` before construction, including
in workers.


API
===
Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.