# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

"""
This example shows how OnlineTool works when we need update prediction.
There are two parts including first_train and update_online_pred.
Firstly, we will finish the training and set the trained models to the `online` models.
Next, we will finish updating online predictions.
"""
import fire
import qlib
from qlib.config import REG_CN
from qlib.model.trainer import task_train
from qlib.workflow.online.utils import OnlineToolR

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": "csi100",
}

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",
    },
}


class UpdatePredExample:
    def __init__(
        self, provider_uri="~/.qlib/qlib_data/cn_data", region=REG_CN, experiment_name="online_srv", task_config=task
    ):
        qlib.init(provider_uri=provider_uri, region=region)
        self.experiment_name = experiment_name
        self.online_tool = OnlineToolR(self.experiment_name)
        self.task_config = task_config

    def first_train(self):
        rec = task_train(self.task_config, experiment_name=self.experiment_name)
        self.online_tool.reset_online_tag(rec)  # set to online model

    def update_online_pred(self):
        self.online_tool.update_online_pred()

    def main(self):
        self.first_train()
        self.update_online_pred()


if __name__ == "__main__":
    ## to train a model and set it to online model, use the command below
    # python update_online_pred.py first_train
    ## to update online predictions once a day, use the command below
    # python update_online_pred.py update_online_pred
    ## to see the whole process with your own parameters, use the command below
    # python update_online_pred.py main --experiment_name="your_exp_name"
    fire.Fire(UpdatePredExample)