85 lines
3.6 KiB
Python
85 lines
3.6 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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from copy import deepcopy
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import pandas as pd
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from arrow import Arrow
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from freqtrade.configuration import TimeRange
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from freqtrade.data import history
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from freqtrade.data.history import get_timerange
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from freqtrade.enums import ExitType
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from freqtrade.optimize.backtesting import Backtesting
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from tests.conftest import patch_exchange
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def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
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default_conf['use_sell_signal'] = False
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mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
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mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
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mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
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patch_exchange(mocker)
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default_conf.update({
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"stake_amount": 100.0,
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"dry_run_wallet": 1000.0,
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"strategy": "StrategyTestV2"
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})
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backtesting = Backtesting(default_conf)
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backtesting._set_strategy(backtesting.strategylist[0])
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pair = 'UNITTEST/BTC'
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timerange = TimeRange('date', None, 1517227800, 0)
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data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
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timerange=timerange)
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backtesting.strategy.position_adjustment_enable = True
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processed = backtesting.strategy.advise_all_indicators(data)
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min_date, max_date = get_timerange(processed)
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result = backtesting.backtest(
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processed=deepcopy(processed),
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start_date=min_date,
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end_date=max_date,
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max_open_trades=10,
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position_stacking=False,
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)
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results = result['results']
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assert not results.empty
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assert len(results) == 2
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expected = pd.DataFrame(
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{'pair': [pair, pair],
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'stake_amount': [500.0, 100.0],
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'amount': [4806.87657523, 970.63960782],
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'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
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Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
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),
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'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 00, 0).datetime,
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Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
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'open_rate': [0.10401764894444211, 0.10302485],
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'close_rate': [0.10453904066847439, 0.103541],
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'fee_open': [0.0025, 0.0025],
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'fee_close': [0.0025, 0.0025],
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'trade_duration': [200, 40],
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'profit_ratio': [0.0, 0.0],
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'profit_abs': [0.0, 0.0],
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'exit_reason': [ExitType.ROI.value, ExitType.ROI.value],
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'initial_stop_loss_abs': [0.0940005, 0.09272236],
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'initial_stop_loss_ratio': [-0.1, -0.1],
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'stop_loss_abs': [0.0940005, 0.09272236],
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'stop_loss_ratio': [-0.1, -0.1],
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'min_rate': [0.10370188, 0.10300000000000001],
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'max_rate': [0.10481985, 0.1038888],
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'is_open': [False, False],
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'enter_tag': [None, None],
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'is_short': [False, False],
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})
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pd.testing.assert_frame_equal(results, expected)
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data_pair = processed[pair]
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for _, t in results.iterrows():
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ln = data_pair.loc[data_pair["date"] == t["open_date"]]
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# Check open trade rate alignes to open rate
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assert ln is not None
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# check close trade rate alignes to close rate or is between high and low
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ln = data_pair.loc[data_pair["date"] == t["close_date"]]
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assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
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round(ln.iloc[0]["low"], 6) < round(
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t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
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