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138 lines
4.1 KiB
Python
138 lines
4.1 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 pickle
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import numpy as np
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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 init_instance_by_config
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from qlib.data.dataset.handler import DataHandlerLP
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from qlib.data.ops import Operators
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from qlib.data.data import Cal
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from highfreq_ops import DayFirst, DayLast, FFillNan, Date, Select, IsNull
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def save_dataset(dataset, path: [Path, str]):
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"""
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save dataset to path
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Parameters
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----------
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path : [Path, str]
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path to save
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"""
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dataset.to_pickle(path=path)
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def load_dataset(path: [Path, str], init_type=DataHandlerLP.IT_LS):
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"""
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load dataset from path
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Parameters
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----------
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path : [Path, str]
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path to load
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init_type : str
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- if `init_type` == DataHandlerLP.IT_FIT_SEQ:
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the input of `DataHandlerLP.fit` will be the output of the previous processor
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- if `init_type` == DataHandlerLP.IT_FIT_IND:
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the input of `DataHandlerLP.fit` will be the original df
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- if `init_type` == DataHandlerLP.IT_LS:
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The state of the object has been load by pickle
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"""
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fd = open(path, 'rb')
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dataset = pickle.load(fd)
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dataset.init(init_type=init_type)
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fd.close()
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return dataset
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if __name__ == "__main__":
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# use default data
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provider_uri = "/mnt/v-xiabi/data/qlib/high_freq" # target_dir
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qlib.init(provider_uri=provider_uri, custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull], redis_port=233, region=REG_CN, auto_mount=False)
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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_CONFIG0 = {
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"start_time": "2017-01-01 00:00:00",
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"end_time": "2020-11-30 15:00:00",
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"freq": "1min",
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"fit_start_time": "2017-01-01 00:00:00",
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"fit_end_time": "2020-08-31 15:00:00",
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"instruments": "all",
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"infer_processors": [{"class": "HighFreqNorm", "module_path": "highfreq_processor", "kwargs": {}}],
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}
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DATA_HANDLER_CONFIG1 = {
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"start_time": "2017-01-01 00:00:00",
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"end_time": "2020-11-30 15:00:00",
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"freq": "1min",
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"instruments": "all",
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}
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task = {
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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": "HighFreqHandler",
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"module_path": "highfreq_handler",
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"kwargs": DATA_HANDLER_CONFIG0,
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},
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"segments": {
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"train": ("2017-01-01 00:00:00", "2020-08-31 15:00:00"),
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"test": (
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"2020-09-01 00:00:00",
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"2020-11-30 15:00:00",
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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', 'SigAnaRecord', 'PortAnaRecord'],
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"dataset_backtest": {
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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": "HighFreqBacktestHandler",
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"module_path": "highfreq_hander",
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"kwargs": DATA_HANDLER_CONFIG1,
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},
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"segments": {
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"train": ("2017-01-01 00:00:00", "2020-08-31 15:00:00"),
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"test": (
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"2020-09-01 00:00:00",
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"2020-11-30 15:00:00",
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),
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},
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},
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},
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}
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Cal.get_calender_day(freq="1min") # TO FIX: load the calendar day for cache
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dataset = init_instance_by_config(task["dataset"])
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dataset_backtest = init_instance_by_config(task["dataset_backtest"])
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