baseline / Kronos /qlib /examples /workflow_by_code.py

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#  Copyright (c) Microsoft Corporation.
#  Licensed under the MIT License.
"""
Qlib provides two kinds of interfaces.
(1) Users could define the Quant research workflow by a simple configuration.
(2) Qlib is designed in a modularized way and supports creating research workflow by code just like building blocks.
The interface of (1) is `qrun XXX.yaml`.  The interface of (2) is script like this, which nearly does the same thing as `qrun XXX.yaml`
"""

import qlib
from qlib.constant import REG_CN
from qlib.utils import init_instance_by_config, flatten_dict
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord, SigAnaRecord
from qlib.tests.data import GetData
from qlib.tests.config import CSI300_BENCH, CSI300_GBDT_TASK

if __name__ == "__main__":
    # use default data
    provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
    GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
    qlib.init(provider_uri=provider_uri, region=REG_CN)

    model = init_instance_by_config(CSI300_GBDT_TASK["model"])
    dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])

    port_analysis_config = {
        "executor": {
            "class": "SimulatorExecutor",
            "module_path": "qlib.backtest.executor",
            "kwargs": {
                "time_per_step": "day",
                "generate_portfolio_metrics": True,
            },
        },
        "strategy": {
            "class": "TopkDropoutStrategy",
            "module_path": "qlib.contrib.strategy.signal_strategy",
            "kwargs": {
                "signal": (model, dataset),
                "topk": 50,
                "n_drop": 5,
            },
        },
        "backtest": {
            "start_time": "2017-01-01",
            "end_time": "2020-08-01",
            "account": 100000000,
            "benchmark": CSI300_BENCH,
            "exchange_kwargs": {
                "freq": "day",
                "limit_threshold": 0.095,
                "deal_price": "close",
                "open_cost": 0.0005,
                "close_cost": 0.0015,
                "min_cost": 5,
            },
        },
    }

    # NOTE: This line is optional
    # It demonstrates that the dataset can be used standalone.
    example_df = dataset.prepare("train")
    print(example_df.head())

    # start exp
    with R.start(experiment_name="workflow"):
        R.log_params(**flatten_dict(CSI300_GBDT_TASK))
        model.fit(dataset)
        R.save_objects(**{"params.pkl": model})

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

        # Signal Analysis
        sar = SigAnaRecord(recorder)
        sar.generate()

        # backtest. If users want to use backtest based on their own prediction,
        # please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
        par = PortAnaRecord(recorder, port_analysis_config, "day")
        par.generate()

	# Copyright (c) Microsoft Corporation.
	# Licensed under the MIT License.
	"""
	Qlib provides two kinds of interfaces.
	(1) Users could define the Quant research workflow by a simple configuration.
	(2) Qlib is designed in a modularized way and supports creating research workflow by code just like building blocks.

	The interface of (1) is `qrun XXX.yaml`. The interface of (2) is script like this, which nearly does the same thing as `qrun XXX.yaml`
	"""

	import qlib
	from qlib.constant import REG_CN
	from qlib.utils import init_instance_by_config, flatten_dict
	from qlib.workflow import R
	from qlib.workflow.record_temp import SignalRecord, PortAnaRecord, SigAnaRecord
	from qlib.tests.data import GetData
	from qlib.tests.config import CSI300_BENCH, CSI300_GBDT_TASK

	if __name__ == "__main__":
	# use default data
	provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
	GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
	qlib.init(provider_uri=provider_uri, region=REG_CN)

	model = init_instance_by_config(CSI300_GBDT_TASK["model"])
	dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])

	port_analysis_config = {
	"executor": {
	"class": "SimulatorExecutor",
	"module_path": "qlib.backtest.executor",
	"kwargs": {
	"time_per_step": "day",
	"generate_portfolio_metrics": True,
	},
	},
	"strategy": {
	"class": "TopkDropoutStrategy",
	"module_path": "qlib.contrib.strategy.signal_strategy",
	"kwargs": {
	"signal": (model, dataset),
	"topk": 50,
	"n_drop": 5,
	},
	},
	"backtest": {
	"start_time": "2017-01-01",
	"end_time": "2020-08-01",
	"account": 100000000,
	"benchmark": CSI300_BENCH,
	"exchange_kwargs": {
	"freq": "day",
	"limit_threshold": 0.095,
	"deal_price": "close",
	"open_cost": 0.0005,
	"close_cost": 0.0015,
	"min_cost": 5,
	},
	},
	}

	# NOTE: This line is optional
	# It demonstrates that the dataset can be used standalone.
	example_df = dataset.prepare("train")
	print(example_df.head())

	# start exp
	with R.start(experiment_name="workflow"):
	R.log_params(**flatten_dict(CSI300_GBDT_TASK))
	model.fit(dataset)
	R.save_objects(**{"params.pkl": model})

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

	# Signal Analysis
	sar = SigAnaRecord(recorder)
	sar.generate()

	# backtest. If users want to use backtest based on their own prediction,
	# please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
	par = PortAnaRecord(recorder, port_analysis_config, "day")
	par.generate()