155 lines
4.9 KiB
Python
155 lines
4.9 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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# Refer to:
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.ipynb
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# - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
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# python exps/trading/workflow_tt.py --gpu 1 --market csi300
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#####################################################
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import sys, argparse
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from procedures.q_exps import update_gpu
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from procedures.q_exps import update_market
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from procedures.q_exps import run_exp
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import qlib
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from qlib.config import C
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from qlib.config import REG_CN
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from qlib.utils import init_instance_by_config
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from qlib.workflow import R
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def main(xargs):
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dataset_config = {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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"handler": {
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"class": "Alpha360",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": xargs.market,
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"infer_processors": [
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{
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"class": "RobustZScoreNorm",
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"kwargs": {"fields_group": "feature", "clip_outlier": True},
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},
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{"class": "Fillna", "kwargs": {"fields_group": "feature"}},
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],
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"learn_processors": [
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{"class": "DropnaLabel"},
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{"class": "CSRankNorm", "kwargs": {"fields_group": "label"}},
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],
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"label": ["Ref($close, -2) / Ref($close, -1) - 1"],
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},
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},
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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},
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}
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model_config = {
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"class": "QuantTransformer",
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"module_path": "trade_models",
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"kwargs": {
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"net_config": None,
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"opt_config": None,
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"GPU": "0",
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"metric": "loss",
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},
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}
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port_analysis_config = {
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"strategy": {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.strategy",
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"kwargs": {
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"topk": 50,
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"n_drop": 5,
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},
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},
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"backtest": {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": "SH000300",
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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},
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}
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record_config = [
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{
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"class": "SignalRecord",
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"module_path": "qlib.workflow.record_temp",
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"kwargs": dict(),
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},
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{
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"class": "SigAnaRecord",
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"module_path": "qlib.workflow.record_temp",
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"kwargs": dict(ana_long_short=False, ann_scaler=252),
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},
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{
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"class": "PortAnaRecord",
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"module_path": "qlib.workflow.record_temp",
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"kwargs": dict(config=port_analysis_config),
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},
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]
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provider_uri = "~/.qlib/qlib_data/cn_data"
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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save_dir = "{:}-{:}".format(xargs.save_dir, xargs.market)
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dataset = init_instance_by_config(dataset_config)
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for irun in range(xargs.times):
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xmodel_config = model_config.copy()
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xmodel_config = update_gpu(xmodel_config, xargs.gpu)
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task_config = dict(
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model=xmodel_config, dataset=dataset_config, record=record_config
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)
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run_exp(
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task_config,
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dataset,
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xargs.name,
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"recorder-{:02d}-{:02d}".format(irun, xargs.times),
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save_dir,
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Vanilla Transformable Transformer")
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./outputs/vtt-runs",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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"--name", type=str, default="Transformer", help="The experiment name."
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)
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parser.add_argument("--times", type=int, default=10, help="The repeated run times.")
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parser.add_argument(
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"--gpu", type=int, default=0, help="The GPU ID used for train / test."
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)
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parser.add_argument(
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"--market", type=str, default="all", help="The market indicator."
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)
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args = parser.parse_args()
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main(args)
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