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133 lines
3.8 KiB
Python
133 lines
3.8 KiB
Python
# 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.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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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_cn(target_dir=provider_uri)
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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": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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"kwargs": {
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"loss": "mse",
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"colsample_bytree": 0.8879,
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"learning_rate": 0.0421,
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"subsample": 0.8789,
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"lambda_l1": 205.6999,
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"lambda_l2": 580.9768,
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"max_depth": 8,
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"num_leaves": 210,
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"num_threads": 20,
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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": (
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"2015-01-01",
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"2016-12-31",
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),
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"test": (
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"2017-01-01",
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"2020-08-01",
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),
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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", "PortAnaRecord"],
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}
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port_analysis_config = {
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"strategy": {
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"topk": 50,
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"n_drop": 5,
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},
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"backtest": {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": 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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}
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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
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with R.start("workflow"):
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model.fit(dataset)
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# prediction
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recorder = R.get_recorder()
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sr = SignalRecord(model, dataset, recorder)
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sr.generate()
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# backtest
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par = PortAnaRecord(recorder, port_analysis_config)
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par.generate()
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