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Delete workflow code for testing baseline.
This commit is contained in:
lwwang1995
@@ -1,138 +0,0 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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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.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
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from qlib.utils import init_instance_by_config
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if __name__ == "__main__":
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# use default 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(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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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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TRAINER_CONFIG = {
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"train_start_time": "2008-01-01",
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"train_end_time": "2014-12-31",
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"validate_start_time": "2015-01-01",
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"validate_end_time": "2016-12-31",
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"test_start_time": "2017-01-01",
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"test_end_time": "2020-08-01",
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}
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task = {
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"model": {
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"class": "ALSTM",
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"module_path": "qlib.contrib.model.pytorch_alstm",
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"kwargs": {
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"d_feat": 6,
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"hidden_size": 64,
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"num_layers": 2,
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"dropout": 0.0,
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"n_epochs": 200,
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"lr": 1e-3,
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"early_stop": 20,
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"batch_size": 800,
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"metric": "IC",
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"loss": "mse",
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"seed": 0,
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"GPU": "0",
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"rnn_type": "GRU",
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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": "ALPHA360_Denoise",
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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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# You shoud record the data in specific sequence
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# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
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}
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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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model.fit(dataset)
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pred_score = model.predict(dataset)
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# save pred_score to file
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pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
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pred_score_path.parent.mkdir(exist_ok=True, parents=True)
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pred_score.to_pickle(pred_score_path)
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###################################
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# backtest
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###################################
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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}
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BACKTEST_CONFIG = {
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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": BENCHMARK,
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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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# use default strategy
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# custom Strategy, refer to: TODO: Strategy API url
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
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###################################
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# analyze
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# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
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###################################
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analysis = dict()
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analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"]
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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print(analysis_df)
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@@ -1,140 +0,0 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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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.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
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from qlib.utils import init_instance_by_config
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if __name__ == "__main__":
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# use default 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(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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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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TRAINER_CONFIG = {
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"train_start_time": "2008-01-01",
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"train_end_time": "2014-12-31",
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"validate_start_time": "2015-01-01",
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"validate_end_time": "2016-12-31",
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"test_start_time": "2017-01-01",
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"test_end_time": "2020-08-01",
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}
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task = {
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"model": {
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"class": "GAT",
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"module_path": "qlib.contrib.model.pytorch_gats",
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"kwargs": {
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"d_feat": 6,
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"hidden_size": 64,
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"num_layers": 2,
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"dropout": 0.7,
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"n_epochs": 200,
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"lr": 1e-4,
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"early_stop": 20,
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"metric": "loss",
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"loss": "mse",
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"base_model": "LSTM",
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"with_pretrain": True,
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"seed": 0,
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"GPU": "0",
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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": "ALPHA360_Denoise",
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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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# You shoud record the data in specific sequence
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# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
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}
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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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model.fit(dataset)
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pred_score = model.predict(dataset)
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# save pred_score to file
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pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
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pred_score_path.parent.mkdir(exist_ok=True, parents=True)
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pred_score.to_pickle(pred_score_path)
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###################################
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# backtest
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###################################
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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}
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BACKTEST_CONFIG = {
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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": BENCHMARK,
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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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# use default strategy
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# custom Strategy, refer to: TODO: Strategy API url
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
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###################################
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# analyze
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# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
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###################################
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analysis = dict()
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analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"]
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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print(analysis_df)
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@@ -1,144 +0,0 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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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.pytorch_gru import GRU
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from qlib.contrib.data.handler import ALPHA360_Denoise
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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
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# from qlib.model.learner import train_model
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from qlib.utils import init_instance_by_config
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import pickle
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if __name__ == "__main__":
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# use default 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(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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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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###################################
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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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|
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TRAINER_CONFIG = {
|
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"train_start_time": "2008-01-01",
|
||||
"train_end_time": "2014-12-31",
|
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"validate_start_time": "2015-01-01",
|
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"validate_end_time": "2016-12-31",
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"test_start_time": "2017-01-01",
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"test_end_time": "2020-08-01",
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}
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task = {
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"model": {
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"class": "GRU",
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"module_path": "qlib.contrib.model.pytorch_gru",
|
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"kwargs": {
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"d_feat": 6,
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"hidden_size": 64,
|
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"num_layers": 2,
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"dropout": 0.0,
|
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"n_epochs": 200,
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"lr": 1e-3,
|
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"early_stop": 20,
|
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"batch_size": 800,
|
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"metric": "loss",
|
||||
"loss": "mse",
|
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"seed": 0,
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"GPU": 0,
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},
|
||||
},
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"dataset": {
|
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"class": "DatasetH",
|
||||
"module_path": "qlib.data.dataset",
|
||||
"kwargs": {
|
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"handler": {
|
||||
"class": "ALPHA360_Denoise",
|
||||
"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)
|
||||
@@ -1,136 +0,0 @@
|
||||
# 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.strategy.strategy import TopkDropoutStrategy
|
||||
from qlib.contrib.evaluate import (
|
||||
backtest as normal_backtest,
|
||||
risk_analysis,
|
||||
)
|
||||
from qlib.utils import exists_qlib_data
|
||||
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(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,
|
||||
}
|
||||
|
||||
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": "HATS",
|
||||
"module_path": "qlib.contrib.model.pytorch_hats",
|
||||
"kwargs": {
|
||||
"d_feat": 6,
|
||||
"hidden_size": 64,
|
||||
"num_layers": 2,
|
||||
"dropout": 0.7,
|
||||
"n_epochs": 200,
|
||||
"lr": 1e-4,
|
||||
"early_stop": 20,
|
||||
"metric": "loss",
|
||||
"loss": "mse",
|
||||
"base_model": "LSTM",
|
||||
"seed": 0,
|
||||
"GPU": "2",
|
||||
},
|
||||
},
|
||||
"dataset": {
|
||||
"class": "DatasetH",
|
||||
"module_path": "qlib.data.dataset",
|
||||
"kwargs": {
|
||||
"handler": {
|
||||
"class": "ALPHA360_Denoise",
|
||||
"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 = init_instance_by_config(task["model"])
|
||||
dataset = init_instance_by_config(task["dataset"])
|
||||
model.fit(dataset, save_path="benchmarks/HATS/model_hat.pkl")
|
||||
|
||||
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)
|
||||
@@ -1,144 +0,0 @@
|
||||
# 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.pytorch_lstm import LSTM
|
||||
from qlib.contrib.data.handler import ALPHA360_Denoise
|
||||
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
|
||||
|
||||
import pickle
|
||||
|
||||
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(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,
|
||||
}
|
||||
|
||||
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": "LSTM",
|
||||
"module_path": "qlib.contrib.model.pytorch_lstm",
|
||||
"kwargs": {
|
||||
"d_feat": 6,
|
||||
"hidden_size": 64,
|
||||
"num_layers": 2,
|
||||
"dropout": 0.0,
|
||||
"n_epochs": 200,
|
||||
"lr": 1e-3,
|
||||
"early_stop": 20,
|
||||
"batch_size": 800,
|
||||
"metric": "IC",
|
||||
"loss": "mse",
|
||||
"seed": 0,
|
||||
"GPU": 0,
|
||||
},
|
||||
},
|
||||
"dataset": {
|
||||
"class": "DatasetH",
|
||||
"module_path": "qlib.data.dataset",
|
||||
"kwargs": {
|
||||
"handler": {
|
||||
"class": "ALPHA360_Denoise",
|
||||
"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)
|
||||
@@ -1,158 +0,0 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import qlib
|
||||
import pandas as pd
|
||||
from qlib.config import REG_CN
|
||||
from qlib.contrib.model.pytorch_gru import GRU
|
||||
from qlib.contrib.data.handler import ALPHA360_Denoise
|
||||
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.utils import init_instance_by_config
|
||||
|
||||
import pickle
|
||||
|
||||
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(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,
|
||||
}
|
||||
|
||||
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": "SFM",
|
||||
"module_path": "qlib.contrib.model.pytorch_sfm",
|
||||
"kwargs": {
|
||||
"d_feat": 6,
|
||||
"hidden_size": 64,
|
||||
"output_dim": 32,
|
||||
"freq_dim": 25,
|
||||
"dropout_W": 0.5,
|
||||
"dropout_U": 0.5,
|
||||
"n_epochs": 15,
|
||||
"lr": 1e-3,
|
||||
"metric": "",
|
||||
"batch_size": 1600,
|
||||
"early_stop": 20,
|
||||
"eval_steps": 5,
|
||||
"loss": "mse",
|
||||
"lr_decay": 0.96,
|
||||
"lr_decay_steps": 100,
|
||||
"optimizer": "adam",
|
||||
"GPU": 3,
|
||||
"seed": 710,
|
||||
},
|
||||
},
|
||||
"dataset": {
|
||||
"class": "DatasetH",
|
||||
"module_path": "qlib.data.dataset",
|
||||
"kwargs": {
|
||||
"handler": {
|
||||
"class": "ALPHA360_Denoise",
|
||||
"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)
|
||||
@@ -1,142 +0,0 @@
|
||||
# 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.tabnet import TabNetModel
|
||||
from qlib.contrib.data.handler import ALPHA360_Denoise
|
||||
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
|
||||
|
||||
import pickle
|
||||
|
||||
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(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,
|
||||
}
|
||||
|
||||
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": "TabNetModel",
|
||||
"module_path": "qlib.contrib.model.tabnet",
|
||||
"kwargs": {
|
||||
"n_d": 8,
|
||||
"n_a": 8,
|
||||
"n_steps": 3,
|
||||
"gamma": 1.3,
|
||||
"n_independent": 2,
|
||||
"n_shared": 2,
|
||||
"seed": 0,
|
||||
"momentum": 0.02,
|
||||
"lambda_sparse": 1e-3,
|
||||
"optimizer_params": {"lr": 2e-3},
|
||||
},
|
||||
},
|
||||
"dataset": {
|
||||
"class": "DatasetH",
|
||||
"module_path": "qlib.data.dataset",
|
||||
"kwargs": {
|
||||
"handler": {
|
||||
"class": "ALPHA360_Denoise",
|
||||
"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)
|
||||
Reference in New Issue
Block a user