adapt functional tests for new version after rebase
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Matthias
parent
30a6e684a6
commit
409465ac8e
@ -9,6 +9,7 @@ from arrow import get as getdate
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from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.strategy.interface import SellType
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from freqtrade.tests.conftest import patch_exchange, log_has
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@ -21,8 +22,7 @@ class BTContainer(NamedTuple):
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roi: float
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trades: int
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profit_perc: float
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sl: bool
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remains: bool
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sell_r: SellType
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columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
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@ -40,9 +40,9 @@ data_profit = DataFrame([
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], columns=columns)
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tc_profit1 = BTContainer(data=data_profit, stop_loss=-0.01, roi=1, trades=1,
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profit_perc=0.10557, sl=False, remains=False) # should be stoploss - drops 8%
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profit_perc=0.10557, sell_r=SellType.STOP_LOSS) # should be stoploss - drops 8%
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tc_profit2 = BTContainer(data=data_profit, stop_loss=-0.10, roi=1,
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trades=1, profit_perc=0.10557, sl=True, remains=False)
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trades=1, profit_perc=0.10557, sell_r=SellType.STOP_LOSS)
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tc_loss0 = BTContainer(data=DataFrame([
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@ -57,7 +57,7 @@ tc_loss0 = BTContainer(data=DataFrame([
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[getdate('2018-07-08 22:00:00').datetime, 0.001000,
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0.001011, 0.00098618, 0.00091618, 12345, 0, 0]
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], columns=columns),
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stop_loss=-0.05, roi=1, trades=1, profit_perc=-0.08839, sl=True, remains=False)
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stop_loss=-0.05, roi=1, trades=1, profit_perc=-0.08839, sell_r=SellType.STOP_LOSS)
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# Test 1 Minus 8% Close
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@ -71,8 +71,8 @@ tc1 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9955, 9975, 9955, 9990, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9990, 9990, 9990, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.01, sl=True, remains=False) # should be
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stop_loss=-0.01, roi=1, trades=1, profit_perc=0.071, sl=False, remains=True) #
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# stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.01, sell_r=SellType.STOP_LOSS) # should be
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stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.003, sell_r=SellType.FORCE_SELL) #
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# Test 2 Minus 4% Low, minus 1% close
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@ -86,8 +86,8 @@ tc2 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.03, sl=True, remains=False) #should be
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stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.00999, sl=False, remains=True) #
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# stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.03, sell_r=SellType.STOP_LOSS) #should be
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stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
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# Test 3 Candle drops 4%, Recovers 1%.
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@ -104,8 +104,8 @@ tc3 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 8000, 8000, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9950, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.02, roi=1, trades=2, profit_perc=-0.4, sl=True, remains=False) #should be
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stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.19999, sl=True, remains=False) #
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# stop_loss=-0.02, roi=1, trades=2, profit_perc=-0.4, sell_r=SellType.STOP_LOSS) #should be
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stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
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# Test 4 Minus 3% / recovery +15%
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@ -119,8 +119,8 @@ tc4 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.02, sl=False, remains=False) #should be
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stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.141, sl=True, remains=False)
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# stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.02, sell_r=SellType.STOP_LOSS) #should be
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stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL)
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# Test 5 / Drops 0.5% Closes +20%
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# Candle Data for test 5
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@ -133,8 +133,8 @@ tc5 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.01, roi=0.03, trades=1, profit_perc=0.03, sl=False, remains=False) #should be
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stop_loss=-0.01, roi=0.03, trades=1, profit_perc=0.197, sl=False, remains=False)
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# stop_loss=-0.01, roi=0.03, trades=1, profit_perc=0.03, sell_r=SellType.ROI) #should be
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stop_loss=-0.01, roi=0.03, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL)
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# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
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# Candle Data for test 6
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@ -147,8 +147,8 @@ tc6 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.02, sl=False, remains=False) #should be
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stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.025, sl=False, remains=True) #
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# stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.02, sell_r=SellType.STOP_LOSS) #should be
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stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
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# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
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# Candle Data for test 7
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@ -161,8 +161,8 @@ tc7 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0]
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], columns=columns),
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# stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.03, sl=False, remains=False) #should be
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stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.025, sl=False, remains=True) #
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# stop_loss=-0.02, roi=0.03, trades=1, profit_perc=0.03, sell_r=SellType.ROI) #should be
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stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
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TESTS = [
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# tc_profit1,
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@ -186,12 +186,11 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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default_conf["stoploss"] = data.stop_loss
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default_conf["minimal_roi"] = {"0": data.roi}
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mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
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mocker.patch.multiple('freqtrade.analyze.Analyze',
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populate_sell_trend=MagicMock(return_value=data.data),
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populate_buy_trend=MagicMock(return_value=data.data))
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patch_exchange(mocker)
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backtesting = Backtesting(default_conf)
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backtesting.advise_buy = lambda a, m: data.data
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backtesting.advise_sell = lambda a, m: data.data
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caplog.set_level(logging.DEBUG)
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pair = 'UNITTEST/BTC'
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@ -202,20 +201,19 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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'stake_amount': default_conf['stake_amount'],
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'processed': data_processed,
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'max_open_trades': 10,
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'realistic': True
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}
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)
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print(results.T)
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assert len(results) == data.trades
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assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
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if data.sl:
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if data.sell_r == SellType.STOP_LOSS:
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assert log_has("Stop loss hit.", caplog.record_tuples)
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else:
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assert not log_has("Stop loss hit.", caplog.record_tuples)
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log_test = (f'Force_selling still open trade UNITTEST/BTC with '
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f'{results.iloc[-1].profit_percent} perc - {results.iloc[-1].profit_abs}')
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if data.remains:
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if data.sell_r == SellType.FORCE_SELL:
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assert log_has(log_test,
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caplog.record_tuples)
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else:
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