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be4646b4b7
* 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
132 lines
4.7 KiB
Python
132 lines
4.7 KiB
Python
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.decision 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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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_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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"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_portfolio_metrics": 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_portfolio_metrics": 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"][0])
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if __name__ == "__main__":
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unittest.main()
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