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https://github.com/microsoft/qlib.git
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Add initial workflow_by_config
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@@ -46,15 +46,6 @@ if __name__ == "__main__":
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"instruments": MARKET,
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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",
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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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task = {
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"model": {
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"model": {
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"class": "LGBModel",
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"class": "LGBModel",
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@@ -82,14 +73,8 @@ if __name__ == "__main__":
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},
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},
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"segments": {
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"train": ("2008-01-01", "2014-12-31"),
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"valid": (
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"valid": ("2015-01-01", "2016-12-31"),
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"2015-01-01",
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"test": ("2017-01-01", "2020-08-01"),
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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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},
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},
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},
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@@ -99,8 +84,12 @@ if __name__ == "__main__":
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port_analysis_config = {
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port_analysis_config = {
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"strategy": {
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"strategy": {
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"topk": 50,
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"class": "TopkDropoutStrategy",
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"n_drop": 5,
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"module_path": "qlib.contrib.strategy.strategy",
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"kwargs": {
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"topk": 50,
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"n_drop": 5,
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}
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},
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},
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"backtest": {
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"backtest": {
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"verbose": False,
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"verbose": False,
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49
examples/workflow_by_config.py
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49
examples/workflow_by_config.py
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@@ -0,0 +1,49 @@
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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 fire
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import yaml
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import pandas as pd
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from qlib.config import REG_CN
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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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# worflow handler function
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def workflow(config_path):
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with open(config_path) as fp:
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config = yaml.load(fp, Loader=yaml.FullLoader)
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provider_uri = config.get("PROVIDER_URI")
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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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# model initiaiton
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model = init_instance_by_config(config.get("TASK")["model"])
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dataset = init_instance_by_config(config.get("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, config.get("PORT_ANALYSIS_CONFIG"))
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par.generate()
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if __name__ == "__main__":
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fire.Fire(workflow)
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52
examples/workflow_config.yaml
Normal file
52
examples/workflow_config.yaml
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@@ -0,0 +1,52 @@
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PROVIDER_URI: "~/.qlib/qlib_data/cn_data"
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MARKET: &market csi300
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BENCHMARK: &benchmark SH000300
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DATA_HANDLER_CONFIG: &data_handerler_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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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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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_handerler_config
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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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record: [SignalRecord, PortAnaRecord]
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PORT_ANALYSIS_CONFIG:
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy.strategy
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kwargs:
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topk: 50
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n_drop: 5
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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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