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* Intermediate version * Fix yaml template & Successfully run rolling * Be compatible with benchmark * Get same results with previous linear model * Black formatting * Update black * Update the placeholder mechanism * Update CI * Update CI * Upgrade Black * Fix CI and simplify code * Fix CI * Move the data processing caching mechanism into utils. * Adjusting DDG-DA * Organize import
86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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"""
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Qlib provides two kinds of interfaces.
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(1) Users could define the Quant research workflow by a simple configuration.
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(2) Qlib is designed in a modularized way and supports creating research workflow by code just like building blocks.
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The interface of (1) is `qrun XXX.yaml`. The interface of (2) is script like this, which nearly does the same thing as `qrun XXX.yaml`
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"""
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import qlib
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from qlib.constant import REG_CN
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from qlib.utils import init_instance_by_config, flatten_dict
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord, PortAnaRecord, SigAnaRecord
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from qlib.tests.data import GetData
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from qlib.tests.config import CSI300_BENCH, CSI300_GBDT_TASK
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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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GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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model = init_instance_by_config(CSI300_GBDT_TASK["model"])
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dataset = init_instance_by_config(CSI300_GBDT_TASK["dataset"])
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port_analysis_config = {
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"executor": {
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"class": "SimulatorExecutor",
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": "day",
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"generate_portfolio_metrics": True,
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},
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},
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"strategy": {
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"class": "TopkDropoutStrategy",
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"module_path": "qlib.contrib.strategy.signal_strategy",
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"kwargs": {
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"signal": (model, dataset),
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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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"backtest": {
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"start_time": "2017-01-01",
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"end_time": "2020-08-01",
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"account": 100000000,
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"benchmark": CSI300_BENCH,
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"exchange_kwargs": {
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"freq": "day",
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"limit_threshold": 0.095,
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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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}
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# NOTE: This line is optional
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# It demonstrates that the dataset can be used standalone.
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example_df = dataset.prepare("train")
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print(example_df.head())
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# start exp
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with R.start(experiment_name="workflow"):
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R.log_params(**flatten_dict(CSI300_GBDT_TASK))
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model.fit(dataset)
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R.save_objects(**{"params.pkl": model})
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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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# Signal Analysis
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sar = SigAnaRecord(recorder)
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sar.generate()
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# backtest. If users want to use backtest based on their own prediction,
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# please refer to https://qlib.readthedocs.io/en/latest/component/recorder.html#record-template.
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par = PortAnaRecord(recorder, port_analysis_config, "day")
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par.generate()
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