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qlib/tests/backtest/test_file_strategy.py
wangwenxi-handsome 3760a18a8d Merge nested main (#597)
* MVP for Indian Stocks in qlib using yahooquery

* cleaned with black

* cleaned with black

* add YahooNormalizeIN and YahooNormalizeIN1d

* cleaned the code

* added 1min for IN and also updated readme

* update comments

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* recorder support upload both raw file and directory

* fix comments

* Update README.md

* Fix docs of QlibRecorder

* sort index after loader (#538)

make sure the fetch method is based on a index-sorted pd.DataFrame

* refactor online serving rolling api

* refactor TRA

* format by black

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* clean up

* improve pretrain

* update README

* fix tra when logdir is None

* fix tra when logdir is None

* Update strategy.py

* Update README.md

* Update README.md

* Conda Suggestion

* code standard docs

* Update ensemble.py (#560)

* Fix CI  Bug (#575)


Co-authored-by: yuxwang <anduinnn@foxmail.com>

* Update gen.py (#576)

* Fix multi-process loop calls (#574)

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* bug fix & use oracle transport pretrain

* mend

* Add `backend_freq_config` parameter, support multi-freq uri

* Add sample_config to QlibDataLoader, support multi-freq

* add multi-freq example

* get_cls_kwargs renamed get_callable_kwargs

* support multi-freq uri

* Add inst_processors to D.features

* Fix typo

* Fix the index type of the multi-freq example

* Fix duplicate mlflow directories in tests

* Add DataPathManager to QlibConfig && modify inst_processors to supports list only

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Fix wrong link

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* Supporting shared processor

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* Fix undefined names in Python code (#599)

* Update pytorch_tabnet.py

$ `flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics`
```
./qlib/qlib/contrib/model/pytorch_tabnet.py:567:38: F821 undefined name 'inp'
            self.independ.append(GLU(inp, out_dim, vbs=vbs))
                                     ^
./qlib/examples/model_rolling/task_manager_rolling.py:75:18: F821 undefined name 'task_train'
        run_task(task_train, self.task_pool, experiment_name=self.experiment_name)
                 ^
2     F821 undefined name 'task_train'
2
```

* Fix undefined names in Python code

* from qlib.model.trainer import task_train

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updated classifiers

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change to matplotlib==3.3

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* Add file lock for MLflowExpManager (#619)

* fix torch version

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* fix bugs for running previous exmaple

* fix deal amount bug

* update change doc (#623)

* Add files via upload

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* Update README.md

* Delete change doc.gif

* Add files via upload

* Update README.md

* Delete change doc.gif

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* update doc

* simplify run all model

* fix run all model bug

* Fix Models (#483)

* fix gat dataset

* fix tft model

* Update tft.py

* Fix tft.py

Co-authored-by: Pengrong Zhu <zhu.pengrong@foxmail.com>

* type and skip empty exp

* fix model yaml config

* fix tft import bug

* skip empty result

* fix model and yaml bug

* fix wrong generate parameter

* Modify multi-freq example (#626)

* modify the example of multi-freq

* add Copyright

* add a comment to average_ops.py

* modify the example of multi-freq

* add comment to multi_freq_handler.py

* add the Ref expression description to multi_freq_handler.py

* add expression description to multi_freq_handler.py

* update images

* fix workflow and update framework

Co-authored-by: Gaurav <2796gaurav@gmail.com>
Co-authored-by: 2796gaurav <17353992+2796gaurav@users.noreply.github.com>
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2021-10-01 02:15:30 +08:00

109 lines
3.5 KiB
Python

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import unittest
from qlib.backtest import backtest, decision
from qlib.tests import TestAutoData
import pandas as pd
from pathlib import Path
DIRNAME = Path(__file__).absolute().resolve().parent
class FileStrTest(TestAutoData):
TEST_INST = "SH600519"
EXAMPLE_FILE = DIRNAME / "order_example.csv"
DEAL_NUM_FOR_1000 = 123.47105436976445
def _gen_orders(self) -> pd.DataFrame:
headers = [
"datetime",
"instrument",
"amount",
"direction",
]
orders = [
# test cash limit for buying
["20200103", self.TEST_INST, "1000", "buy"],
# test min_cost for buying
["20200106", self.TEST_INST, "1", "buy"],
# test held stock limit for selling
["20200107", self.TEST_INST, "1000", "sell"],
# test cash limit for buying
["20200108", self.TEST_INST, "1000", "buy"],
# test min_cost for selling
["20200109", self.TEST_INST, "1", "sell"],
# test selling all stocks
["20200110", self.TEST_INST, str(self.DEAL_NUM_FOR_1000), "sell"],
]
return pd.DataFrame(orders, columns=headers).set_index(["datetime", "instrument"])
def test_file_str(self):
orders = self._gen_orders()
print(orders)
orders.to_csv(self.EXAMPLE_FILE)
orders = pd.read_csv(self.EXAMPLE_FILE, index_col=["datetime", "instrument"])
strategy_config = {
"class": "FileOrderStrategy",
"module_path": "qlib.contrib.strategy.rule_strategy",
"kwargs": {"file": self.EXAMPLE_FILE},
}
freq = "day"
start_time = "2020-01-01"
end_time = "2020-01-16"
codes = [self.TEST_INST]
backtest_config = {
"start_time": start_time,
"end_time": end_time,
"account": 30000,
"benchmark": None, # benchmark is not required here for trading
"exchange_kwargs": {
"freq": freq,
"limit_threshold": 0.095,
"deal_price": "close",
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 500,
"codes": codes,
"trade_unit": 2,
},
# "pos_type": "InfPosition" # Position with infinitive position
}
executor_config = {
"class": "SimulatorExecutor",
"module_path": "qlib.backtest.executor",
"kwargs": {
"time_per_step": freq,
"generate_portfolio_metrics": False,
"verbose": True,
"indicator_config": {
"show_indicator": False,
},
},
}
report_dict, indicator_dict = backtest(executor=executor_config, strategy=strategy_config, **backtest_config)
# ffr valid
ffr_dict = indicator_dict["1day"]["ffr"].to_dict()
ffr_dict = {str(date).split()[0]: ffr_dict[date] for date in ffr_dict}
assert ffr_dict["2020-01-03"] == self.DEAL_NUM_FOR_1000 / 1000
assert ffr_dict["2020-01-06"] == 0
assert ffr_dict["2020-01-07"] == self.DEAL_NUM_FOR_1000 / 1000
assert ffr_dict["2020-01-08"] == self.DEAL_NUM_FOR_1000 / 1000
assert ffr_dict["2020-01-09"] == 0
assert ffr_dict["2020-01-10"] == 1
self.EXAMPLE_FILE.unlink()
if __name__ == "__main__":
unittest.main()