Update Q models
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D-X-Y
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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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_test.py
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#####################################################
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import sys, site
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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: sys.path.insert(0, str(lib_dir))
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import qlib
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import pandas as pd
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from qlib.config import REG_CN
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from qlib.contrib.model.gbdt import LGBModel
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from qlib.contrib.data.handler import Alpha158
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.evaluate import (
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backtest as normal_backtest,
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risk_analysis,
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)
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from qlib.utils import exists_qlib_data, init_instance_by_config
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
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from qlib.utils import flatten_dict
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# use default data
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# NOTE: need to download data from remote: python scripts/get_data.py qlib_data_cn --target_dir ~/.qlib/qlib_data/cn_data
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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if not exists_qlib_data(provider_uri):
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print(f"Qlib data is not found in {provider_uri}")
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sys.path.append(str(scripts_dir))
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from get_data import GetData
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GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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market = "csi300"
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benchmark = "SH000300"
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###################################
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# train model
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###################################
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data_handler_config = {
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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": market,
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}
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task = {
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"model": {
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"class": "QuantTransformer",
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"module_path": "trade_models",
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"kwargs": {
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"loss": "mse",
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"GPU": "0",
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"metric": "loss",
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},
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},
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"dataset": {
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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": "Alpha158",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": data_handler_config,
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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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}
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# model initiaiton
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model = init_instance_by_config(task["model"])
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dataset = init_instance_by_config(task["dataset"])
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# start exp to train model
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with R.start(experiment_name="train_model"):
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R.log_params(**flatten_dict(task))
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model.fit(dataset)
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R.save_objects(trained_model=model)
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rid = R.get_recorder().id
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100
exps/trading/workflow_tt.py
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100
exps/trading/workflow_tt.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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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
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#####################################################
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import sys, site, 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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import qlib
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from qlib.config import C
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import pandas as pd
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from qlib.config import REG_CN
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from qlib.contrib.model.gbdt import LGBModel
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from qlib.contrib.data.handler import Alpha158
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.evaluate import (
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backtest as normal_backtest,
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risk_analysis,
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)
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from qlib.utils import exists_qlib_data, init_instance_by_config
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
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from qlib.utils import flatten_dict
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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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{"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier": True}},
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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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"loss": "mse",
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"GPU": "0",
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"metric": "loss",
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},
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}
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task = {"model": model_config, "dataset": dataset_config}
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model = init_instance_by_config(model_config)
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dataset = init_instance_by_config(dataset_config)
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# start exp to train model
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with R.start(experiment_name="train_tt_model"):
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R.log_params(**flatten_dict(task))
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model.fit(dataset)
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R.save_objects(trained_model=model)
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rid = R.get_recorder().id
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Vanilla Transformable Transformer")
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parser.add_argument("--save_dir", type=str, default="./outputs/tt-ml-runs", help="The checkpoint directory.")
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parser.add_argument("--market", type=str, default="csi300", help="The market indicator.")
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args = parser.parse_args()
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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exp_manager = C.exp_manager
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exp_manager["kwargs"]["uri"] = "file:{:}".format(Path(args.save_dir).resolve())
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qlib.init(provider_uri=provider_uri, region=REG_CN, exp_manager=exp_manager)
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main(args)
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