mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 14:01:28 +08:00
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# 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()
|