mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 14:01:28 +08:00
148 lines
4.5 KiB
Python
148 lines
4.5 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# Licensed under the MIT License.
|
|
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import qlib
|
|
import pandas as pd
|
|
from qlib.config import REG_CN
|
|
from qlib.contrib.model.gbdt import LGBModel
|
|
from qlib.contrib.data.handler import Alpha158
|
|
from qlib.contrib.strategy.strategy import TopkDropoutStrategy
|
|
from qlib.contrib.evaluate import (
|
|
backtest as normal_backtest,
|
|
risk_analysis,
|
|
)
|
|
from qlib.utils import exists_qlib_data
|
|
|
|
# from qlib.model.learner import train_model
|
|
from qlib.utils import init_instance_by_config
|
|
|
|
|
|
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}")
|
|
sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
|
|
from get_data import GetData
|
|
|
|
GetData().qlib_data_cn(target_dir=provider_uri)
|
|
|
|
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,
|
|
}
|
|
|
|
TRAINER_CONFIG = {
|
|
"train_start_time": "2008-01-01",
|
|
"train_end_time": "2014-12-31",
|
|
"validate_start_time": "2015-01-01",
|
|
"validate_end_time": "2016-12-31",
|
|
"test_start_time": "2017-01-01",
|
|
"test_end_time": "2020-08-01",
|
|
}
|
|
|
|
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",
|
|
),
|
|
},
|
|
},
|
|
}
|
|
# You shoud record the data in specific sequence
|
|
# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
|
|
}
|
|
|
|
# model = train_model(task)
|
|
model = init_instance_by_config(task["model"])
|
|
dataset = init_instance_by_config(task["dataset"])
|
|
|
|
model.fit(dataset)
|
|
|
|
pred_score = model.predict(dataset)
|
|
|
|
# save pred_score to file
|
|
pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
|
|
pred_score_path.parent.mkdir(exist_ok=True, parents=True)
|
|
pred_score.to_pickle(pred_score_path)
|
|
|
|
###################################
|
|
# backtest
|
|
###################################
|
|
STRATEGY_CONFIG = {
|
|
"topk": 50,
|
|
"n_drop": 5,
|
|
}
|
|
BACKTEST_CONFIG = {
|
|
"verbose": False,
|
|
"limit_threshold": 0.095,
|
|
"account": 100000000,
|
|
"benchmark": BENCHMARK,
|
|
"deal_price": "close",
|
|
"open_cost": 0.0005,
|
|
"close_cost": 0.0015,
|
|
"min_cost": 5,
|
|
}
|
|
|
|
# use default strategy
|
|
# custom Strategy, refer to: TODO: Strategy API url
|
|
strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
|
|
report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
|
|
|
|
###################################
|
|
# analyze
|
|
# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
|
|
###################################
|
|
analysis = dict()
|
|
analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
|
|
analysis["excess_return_with_cost"] = risk_analysis(
|
|
report_normal["return"] - report_normal["bench"] - report_normal["cost"]
|
|
)
|
|
analysis_df = pd.concat(analysis) # type: pd.DataFrame
|
|
print(analysis_df)
|