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Delete workflow_by_code_tft.py

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Wendi Li
2020-11-25 20:34:52 +08:00
committed by you-n-g
parent 5581cc42bf
commit 510be10a87
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examples/benchmarks/TFT/workflow_by_code_tft.py
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# 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
from tft import TFTModel
if __name__ == "__main__":
# use default data
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
if not exists_qlib_data(provider_uri):
print(f"Qlib data is not found in {provider_uri}")
sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
from get_data import GetData
GetData().qlib_data_cn(target_dir=provider_uri)
qlib.init(provider_uri=provider_uri, region=REG_CN)
MARKET = "csi300"
BENCHMARK = "SH000300"
###################################
# train model
###################################
DATA_HANDLER_CONFIG = {
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": MARKET,
}
TRAINER_CONFIG = {
"train_start_time": "2008-01-01",
"train_end_time": "2014-12-31",
"validate_start_time": "2015-01-01",
"validate_end_time": "2016-12-31",
"test_start_time": "2017-01-01",
"test_end_time": "2020-08-01",
}
task = {
"dataset": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": DATA_HANDLER_CONFIG,
},
"segments": {
"train": ("2008-01-01", "2014-12-31"),
"valid": (
"2015-01-01",
"2016-12-31",
),
"test": (
"2017-01-01",
"2020-08-01",
),
},
},
}
# You shoud record the data in specific sequence
# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
}
model = TFTModel()
dataset = init_instance_by_config(task["dataset"])
model.fit(dataset)
pred_score, label_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)