diff --git a/examples/workflow_by_code_alstm.py b/examples/workflow_by_code_alstm.py deleted file mode 100644 index 8fd9e3565..000000000 --- a/examples/workflow_by_code_alstm.py +++ /dev/null @@ -1,138 +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": "ALSTM", - "module_path": "qlib.contrib.model.pytorch_alstm", - "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", - "rnn_type": "GRU", - }, - }, - "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) - - 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) diff --git a/examples/workflow_by_code_gats.py b/examples/workflow_by_code_gats.py deleted file mode 100644 index 20f3ae552..000000000 --- a/examples/workflow_by_code_gats.py +++ /dev/null @@ -1,140 +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": "GAT", - "module_path": "qlib.contrib.model.pytorch_gats", - "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", - "with_pretrain": True, - "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 = 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) diff --git a/examples/workflow_by_code_gru.py b/examples/workflow_by_code_gru.py deleted file mode 100644 index dece520d1..000000000 --- a/examples/workflow_by_code_gru.py +++ /dev/null @@ -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_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.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": "GRU", - "module_path": "qlib.contrib.model.pytorch_gru", - "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": "loss", - "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) diff --git a/examples/workflow_by_code_hats.py b/examples/workflow_by_code_hats.py deleted file mode 100644 index 64bc860b4..000000000 --- a/examples/workflow_by_code_hats.py +++ /dev/null @@ -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) diff --git a/examples/workflow_by_code_lstm.py b/examples/workflow_by_code_lstm.py deleted file mode 100644 index ee50c9aff..000000000 --- a/examples/workflow_by_code_lstm.py +++ /dev/null @@ -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) diff --git a/examples/workflow_by_code_sfm.py b/examples/workflow_by_code_sfm.py deleted file mode 100644 index 5bd91ded8..000000000 --- a/examples/workflow_by_code_sfm.py +++ /dev/null @@ -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) diff --git a/examples/workflow_by_code_tabnet.py b/examples/workflow_by_code_tabnet.py deleted file mode 100644 index 3778b9d59..000000000 --- a/examples/workflow_by_code_tabnet.py +++ /dev/null @@ -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)