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35794846ff
* Refine several todos * CI issues * Remove Dropna limitation of `quote_df` in Exchange (#1334) * Remove Dropna limitation of `quote_df` of Exchange * Impreove docstring * Fix type error when expression is specified (#1335) * Refine fill_missing_data() * Remove several TODO comments * Add back env for interpreters * Change Literal import * Resolve PR comments * Move to SAOEState * Add Trainer.get_policy_state_dict() * Mypy issue Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
124 lines
4.2 KiB
Python
124 lines
4.2 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import unittest
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from qlib.backtest import backtest
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from qlib.tests import TestAutoData
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import pandas as pd
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from pathlib import Path
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from qlib.data import D
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import numpy as np
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DIRNAME = Path(__file__).absolute().resolve().parent
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class FileStrTest(TestAutoData):
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# Assumption to ensure the correctness of the test
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# - No price adjustment in these several trading days.
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TEST_INST = "SH600519"
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EXAMPLE_FILE = DIRNAME / "order_example.csv"
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def _gen_orders(self, dealt_num_for_1000) -> pd.DataFrame:
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headers = [
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"datetime",
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"instrument",
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"amount",
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"direction",
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]
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orders = [
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# test cash limit for buying
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["20200103", self.TEST_INST, "1000", "buy"],
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# test min_cost for buying
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["20200106", self.TEST_INST, "1", "buy"],
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# test held stock limit for selling
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["20200107", self.TEST_INST, "1000", "sell"],
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# test cash limit for buying
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["20200108", self.TEST_INST, "1000", "buy"],
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# test min_cost for selling
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["20200109", self.TEST_INST, "1", "sell"],
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# test selling all stocks
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["20200110", self.TEST_INST, str(dealt_num_for_1000), "sell"],
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]
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return pd.DataFrame(orders, columns=headers).set_index(["datetime", "instrument"])
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def test_file_str(self):
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# 0) basic settings
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account_money = 150000
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# 1) get information
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df = D.features([self.TEST_INST], ["$close", "$factor"], start_time="20200103", end_time="20200103")
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price = df["$close"].item()
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factor = df["$factor"].item()
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price_unit = price / factor * 100
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dealt_num_for_1000 = (account_money // price_unit) * (100 / factor)
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print(price, factor, price_unit, dealt_num_for_1000)
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# 2) generate orders
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orders = self._gen_orders(dealt_num_for_1000)
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orders.to_csv(self.EXAMPLE_FILE)
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print(orders)
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# 3) run the strategy
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strategy_config = {
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"class": "FileOrderStrategy",
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"module_path": "qlib.contrib.strategy.rule_strategy",
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"kwargs": {"file": self.EXAMPLE_FILE},
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}
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freq = "day"
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start_time = "2020-01-01"
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end_time = "2020-01-16"
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codes = [self.TEST_INST]
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backtest_config = {
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"start_time": start_time,
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"end_time": end_time,
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"account": account_money,
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"benchmark": None, # benchmark is not required here for trading
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"exchange_kwargs": {
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"freq": freq,
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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": 500,
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"codes": codes,
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"trade_unit": 100,
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},
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# "pos_type": "InfPosition" # Position with infinitive position
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}
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executor_config = {
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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": freq,
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"generate_portfolio_metrics": False,
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"verbose": True,
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"indicator_config": {
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"show_indicator": False,
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},
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},
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}
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report_dict, indicator_dict = backtest(
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executor=executor_config,
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strategy=strategy_config,
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**backtest_config,
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)
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# ffr valid
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ffr_dict = indicator_dict["1day"][0]["ffr"].to_dict()
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ffr_dict = {str(date).split()[0]: ffr_dict[date] for date in ffr_dict}
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assert np.isclose(ffr_dict["2020-01-03"], dealt_num_for_1000 / 1000)
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assert np.isclose(ffr_dict["2020-01-06"], 0)
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assert np.isclose(ffr_dict["2020-01-07"], dealt_num_for_1000 / 1000)
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assert np.isclose(ffr_dict["2020-01-08"], dealt_num_for_1000 / 1000)
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assert np.isclose(ffr_dict["2020-01-09"], 0)
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assert np.isclose(ffr_dict["2020-01-10"], 1)
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self.EXAMPLE_FILE.unlink()
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if __name__ == "__main__":
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unittest.main()
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