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https://github.com/microsoft/qlib.git
synced 2026-07-01 10:01:19 +08:00
Add a example to collecting all the decisions
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@@ -9,11 +9,11 @@ from .account import Account
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if TYPE_CHECKING:
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from ..strategy.base import BaseStrategy
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from .executor import BaseExecutor
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from .order import BaseTradeDecision
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from .position import Position
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from .exchange import Exchange
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from .backtest import backtest_loop
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from .backtest import collect_data_loop
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from .order import Order
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from .utils import CommonInfrastructure, LevelInfrastructure, TradeCalendarManager
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from ..utils import init_instance_by_config
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from ..log import get_module_logger
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@@ -228,10 +228,13 @@ def backtest(
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Returns
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-------
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report_dict: Report
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report: Report
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it records the trading report information
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indicator_dict: Indicator
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It is organized in a dict format
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indicator: Indicator
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it computes the trading indicator
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It is organized in a dict format
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"""
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trade_strategy, trade_executor = get_strategy_executor(
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start_time,
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@@ -243,9 +246,9 @@ def backtest(
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exchange_kwargs,
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pos_type=pos_type,
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)
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report_dict, indicator_dict = backtest_loop(start_time, end_time, trade_strategy, trade_executor)
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report, indicator = backtest_loop(start_time, end_time, trade_strategy, trade_executor)
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return report_dict, indicator_dict
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return report, indicator
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def collect_data(
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@@ -257,6 +260,7 @@ def collect_data(
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account=1e9,
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exchange_kwargs={},
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pos_type: str = "Position",
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return_value: dict = None,
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):
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"""initialize the strategy and executor, then collect the trade decision data for rl training
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@@ -277,4 +281,41 @@ def collect_data(
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exchange_kwargs,
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pos_type=pos_type,
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)
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yield from collect_data_loop(start_time, end_time, trade_strategy, trade_executor)
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yield from collect_data_loop(start_time, end_time, trade_strategy, trade_executor, return_value=return_value)
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def format_decisions(
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decisions: List[BaseTradeDecision],
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) -> Tuple[str, List[Tuple[BaseTradeDecision, Union[Tuple, None]]]]:
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"""
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format the decisions collected by `qlib.backtest.collect_data`
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The decisions will be organized into a tree-like structure.
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Parameters
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----------
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decisions : List[BaseTradeDecision]
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decisions collected by `qlib.backtest.collect_data`
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Returns
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-------
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Tuple[str, List[Tuple[BaseTradeDecision, Union[Tuple, None]]]]:
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reformat the list of decisions into a more user-friendly format
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<decisions> := Tuple[<freq>, List[Tuple[<decision>, <sub decisions>]]]
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- <sub decisions> := `<decisions> in lower level` | None
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- <freq> := "day" | "30min" | "1min" | ...
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- <decision> := <instance of BaseTradeDecision>
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"""
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if len(decisions) == 0:
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return None
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cur_freq = decisions[0].strategy.trade_calendar.get_freq()
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res = (cur_freq, [])
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last_dec_idx = 0
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for i, dec in enumerate(decisions[1:], 1):
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if dec.strategy.trade_calendar.get_freq() == cur_freq:
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res[1].append((decisions[last_dec_idx], format_decisions(decisions[last_dec_idx + 1 : i])))
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last_dec_idx = i
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res[1].append((decisions[last_dec_idx], format_decisions(decisions[last_dec_idx + 1 :])))
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return res
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@@ -171,7 +171,7 @@ class BaseSingleMetric:
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@property
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def empty(self) -> bool:
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"""If metric is empyt, return True."""
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"""If metric is empty, return True."""
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raise NotImplementedError(f"Please implement the `empty` method")
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@@ -357,17 +357,17 @@ class PandasSingleMetric:
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def __gt__(self, other):
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if isinstance(other, (int, float)):
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return PandasSingleMetric(self.metric < other)
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return PandasSingleMetric(self.metric > other)
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elif isinstance(other, PandasSingleMetric):
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return PandasSingleMetric(self.metric < other.metric)
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return PandasSingleMetric(self.metric > other.metric)
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else:
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return NotImplemented
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def __lt__(self, other):
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if isinstance(other, (int, float)):
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return PandasSingleMetric(self.metric > other)
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return PandasSingleMetric(self.metric < other)
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elif isinstance(other, PandasSingleMetric):
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return PandasSingleMetric(self.metric > other.metric)
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return PandasSingleMetric(self.metric < other.metric)
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else:
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return NotImplemented
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@@ -8,17 +8,72 @@ class TestAutoData(unittest.TestCase):
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_setup_kwargs = {}
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provider_uri = "~/.qlib/qlib_data/cn_data_simple" # target_dir
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provider_uri_1day = "~/.qlib/qlib_data/cn_data" # target_dir
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provider_uri_1min = "~/.qlib/qlib_data/cn_data_1min"
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@classmethod
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def setUpClass(cls) -> None:
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def setUpClass(cls, enable_1d_type="simple", enable_1min=False) -> None:
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# use default data
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if enable_1d_type == "simple":
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provider_uri_day = cls.provider_uri
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name_day = "qlib_data_simple"
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elif enable_1d_type == "full":
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provider_uri_day = cls.provider_uri_1day
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name_day = "qlib_data"
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else:
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raise NotImplementedError(f"This type of input is not supported")
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GetData().qlib_data(
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name="qlib_data_simple",
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name=name_day,
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region=REG_CN,
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interval="1d",
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target_dir=cls.provider_uri,
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target_dir=provider_uri_day,
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delete_old=False,
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exists_skip=True,
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)
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init(provider_uri=cls.provider_uri, region=REG_CN, **cls._setup_kwargs)
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if enable_1min:
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GetData().qlib_data(
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name="qlib_data",
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region=REG_CN,
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interval="1min",
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target_dir=cls.provider_uri_1min,
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delete_old=False,
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exists_skip=True,
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)
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provider_uri_map = {"1min": cls.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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init(
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provider_uri=cls.provider_uri,
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region=REG_CN,
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expression_cache=None,
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dataset_cache=None,
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**client_config,
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**cls._setup_kwargs,
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)
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@@ -14,7 +14,7 @@ from qlib.utils import exists_qlib_data
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class GetData:
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DATASET_VERSION = "v1"
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DATASET_VERSION = "v2"
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REMOTE_URL = "http://fintech.msra.cn/stock_data/downloads"
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QLIB_DATA_NAME = "{dataset_name}_{region}_{interval}_{qlib_version}.zip"
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133
tests/backtest/test_high_freq_trading.py
Normal file
133
tests/backtest/test_high_freq_trading.py
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@@ -0,0 +1,133 @@
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from typing import List, Tuple, Union
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from qlib.backtest.position import Position
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from qlib.backtest import collect_data, format_decisions
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from qlib.backtest.order import BaseTradeDecision, TradeRangeByTime
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import qlib
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from qlib.tests import TestAutoData
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import unittest
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from qlib.config import REG_CN, HIGH_FREQ_CONFIG
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import pandas as pd
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@unittest.skip("This test takes a lot of time due to the large size of high-frequency data")
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class TestHFBacktest(TestAutoData):
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@classmethod
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def setUpClass(cls) -> None:
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super().setUpClass(enable_1min=True, enable_1d_type="full")
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def _gen_orders(self, inst, date, pos) -> 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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[date, inst, pos, "sell"],
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]
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return pd.DataFrame(orders, columns=headers)
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def test_trading(self):
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# date = "2020-02-03"
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# inst = "SH600068"
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# pos = 2.0167
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pos = 100000
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inst, date = "SH600519", "2021-01-18"
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market = [inst]
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start_time = f"{date}"
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end_time = f"{date} 15:00" # include the high-freq data on the end day
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freq_l0 = "day"
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freq_l1 = "30min"
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freq_l2 = "1min"
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orders = self._gen_orders(inst=inst, date=date, pos=pos * 0.90)
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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": {
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"trade_range": TradeRangeByTime("10:45", "14:44"),
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"file": orders,
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},
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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": {
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"cash": 0,
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inst: pos,
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},
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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_l2, # use the most fine-grained data as the exchange
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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": market,
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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": "NestedExecutor", # Level 1 Order execution
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": freq_l0,
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"inner_executor": {
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"class": "NestedExecutor", # Leve 2 Order Execution
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"module_path": "qlib.backtest.executor",
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"kwargs": {
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"time_per_step": freq_l1,
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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_l2,
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"generate_report": 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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"track_data": True,
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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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"generate_report": False,
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"indicator_config": {
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"show_indicator": True,
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},
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"track_data": True,
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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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"generate_report": False,
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"indicator_config": {
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"show_indicator": True,
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},
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"track_data": True,
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},
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}
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ret_val = {}
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decisions = list(
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collect_data(executor=executor_config, strategy=strategy_config, **backtest_config, return_value=ret_val)
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)
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report, indicator = ret_val["report"], ret_val["indicator"]
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# NOTE: please refer to the docs of format_decisions
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# NOTE: `"track_data": True,` is very NECESSARY for collecting the decision!!!!!
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f_dec = format_decisions(decisions)
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print(indicator["1day"])
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if __name__ == "__main__":
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unittest.main()
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