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synced 2026-07-11 06:46:56 +08:00
Cash Update (#559)
* fix negative cash * update order test * fix bug * update file_order_test
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wangwenxi-handsome
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735153a50d
@@ -16,6 +16,8 @@ class FileStrTest(TestAutoData):
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EXAMPLE_FILE = DIRNAME / "order_example.csv"
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DEAL_NUM_FOR_1000 = 123.47105436976445
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def _gen_orders(self) -> pd.DataFrame:
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headers = [
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"datetime",
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@@ -24,11 +26,18 @@ class FileStrTest(TestAutoData):
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"direction",
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]
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orders = [
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["20200102", self.TEST_INST, "1000", "sell"],
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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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["20200103", self.TEST_INST, "1", "buy"],
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# test held stock limit for selling
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["20200106", self.TEST_INST, "1000", "sell"],
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["20200106", self.TEST_INST, "1000", "buy"],
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["20200106", self.TEST_INST, "949.7773413058803", "sell"],
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# test cash limit for buying
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["20200107", self.TEST_INST, "1000", "buy"],
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# test min_cost for selling
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["20200108", self.TEST_INST, "1", "sell"],
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# test selling all stocks
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["20200110", self.TEST_INST, str(self.DEAL_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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@@ -54,7 +63,7 @@ class FileStrTest(TestAutoData):
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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": 100000000,
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"account": 30000,
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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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@@ -62,9 +71,9 @@ class FileStrTest(TestAutoData):
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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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"min_cost": 500,
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"codes": codes,
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"trade_unit": 100,
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"trade_unit": 2,
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},
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# "pos_type": "InfPosition" # Position with infinitive position
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}
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@@ -80,7 +89,16 @@ class FileStrTest(TestAutoData):
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},
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},
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}
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backtest(executor=executor_config, strategy=strategy_config, **backtest_config)
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report_dict, indicator_dict = backtest(executor=executor_config, strategy=strategy_config, **backtest_config)
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# ffr valid
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ffr_dict = indicator_dict["1day"]["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 ffr_dict["2020-01-03"] == 0
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assert ffr_dict["2020-01-06"] == self.DEAL_NUM_FOR_1000 / 1000
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assert ffr_dict["2020-01-07"] == self.DEAL_NUM_FOR_1000 / 1000
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assert ffr_dict["2020-01-08"] == 0
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assert ffr_dict["2020-01-10"] == 1
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self.EXAMPLE_FILE.unlink()
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@@ -1,119 +0,0 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import unittest
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import qlib
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from qlib.backtest import backtest, order
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from qlib.tests import TestAutoData
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from qlib.backtest.order import TradeDecisionWO, TradeRangeByTime
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import pandas as pd
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from pathlib import Path
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class FileStrTest(TestAutoData):
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TEST_INST = "SH600519"
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def init_qlib(self):
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provider_uri_day = "/nfs_data1/stock_data/huaxia_1d_qlib"
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provider_uri_1min = "/nfs_data1/stock_data/huaxia_1min_qlib"
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provider_uri_map = {"1min": provider_uri_1min, "day": provider_uri_day}
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client_config = {
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"calendar_provider": {
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"class": "LocalCalendarProvider",
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"module_path": "qlib.data.data",
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"kwargs": {
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"backend": {
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"class": "FileCalendarStorage",
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"module_path": "qlib.data.storage.file_storage",
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"kwargs": {"provider_uri_map": provider_uri_map},
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}
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},
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},
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"feature_provider": {
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"class": "LocalFeatureProvider",
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"module_path": "qlib.data.data",
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"kwargs": {
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"backend": {
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"class": "FileFeatureStorage",
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"module_path": "qlib.data.storage.file_storage",
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"kwargs": {"provider_uri_map": provider_uri_map},
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}
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},
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},
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}
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qlib.init(provider_uri=provider_uri_day, **client_config, expression_cache=None, dataset_cache=None)
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def test_file_str(self):
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freq = "1min"
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inst = ["SH600000", "SH600011"]
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start_time = "2020-01-01"
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end_time = "2020-01-15 15:00"
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strategy_config = {
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"class": "RandomOrderStrategy",
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"module_path": "qlib.contrib.strategy.rule_strategy",
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"kwargs": {
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"trade_range": TradeRangeByTime("9:30", "15:00"),
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"sample_ratio": 1.0,
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"volume_ratio": 0.01,
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"market": inst,
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},
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}
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position_dict = {
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"cash": 100000000,
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"SH600000": {"amount": 100},
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"SH600011": {"amount": 101},
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}
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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": position_dict,
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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": 5,
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"codes": inst,
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},
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"pos_type": "Position", # Position with infinitive position
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}
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executor_config = {
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"class": "NestedExecutor",
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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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"inner_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": freq,
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"generate_report": False,
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"verbose": 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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"inner_strategy": {
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"class": "TWAPStrategy",
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"module_path": "qlib.contrib.strategy.rule_strategy",
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},
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"track_data": True,
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"generate_report": True,
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"indicator_config": {
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"show_indicator": True,
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},
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},
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}
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self.init_qlib()
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backtest(executor=executor_config, strategy=strategy_config, **backtest_config)
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if __name__ == "__main__":
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unittest.main()
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