mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 14:01:28 +08:00
128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# Licensed under the MIT License.
|
|
|
|
import qlib
|
|
from qlib.config import REG_CN
|
|
from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
|
|
from qlib.workflow import R
|
|
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
|
|
from qlib.tests.data import GetData
|
|
|
|
if __name__ == "__main__":
|
|
|
|
# use default data
|
|
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
|
|
if not exists_qlib_data(provider_uri):
|
|
print(f"Qlib data is not found in {provider_uri}")
|
|
GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
|
|
|
|
qlib.init(provider_uri=provider_uri, region=REG_CN)
|
|
|
|
market = "csi300"
|
|
benchmark = "SH000300"
|
|
|
|
###################################
|
|
# train model
|
|
###################################
|
|
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"])
|
|
|
|
port_analysis_config = {
|
|
"executor": {
|
|
"class": "SimulatorExecutor",
|
|
"module_path": "qlib.backtest.executor",
|
|
"kwargs": {
|
|
"time_per_step": "day",
|
|
"generate_report": True,
|
|
},
|
|
},
|
|
"strategy": {
|
|
"class": "TopkDropoutStrategy",
|
|
"module_path": "qlib.contrib.strategy.model_strategy",
|
|
"kwargs": {
|
|
"model": model,
|
|
"dataset": dataset,
|
|
"topk": 50,
|
|
"n_drop": 5,
|
|
},
|
|
},
|
|
"backtest": {
|
|
"start_time": "2017-01-01",
|
|
"end_time": "2020-08-01",
|
|
"account": 100000000,
|
|
"benchmark": benchmark,
|
|
"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(task))
|
|
model.fit(dataset)
|
|
R.save_objects(**{"params.pkl": model})
|
|
|
|
# prediction
|
|
recorder = R.get_recorder()
|
|
sr = SignalRecord(model, dataset, recorder)
|
|
sr.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()
|