# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument import logging from unittest.mock import MagicMock from typing import NamedTuple from pandas import DataFrame import pytest from arrow import get as getdate from freqtrade.optimize.backtesting import Backtesting from freqtrade.tests.conftest import patch_exchange, log_has class BTContainer(NamedTuple): """ NamedTuple Defining BacktestResults inputs. """ data: DataFrame stop_loss: float roi: float trades: int profit_perc: float sl: bool remains: bool columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell'] data_profit = DataFrame([ [getdate('2018-07-08 18:00:00').datetime, 0.0009910, 0.001011, 0.00098618, 0.001000, 12345, 1, 0], [getdate('2018-07-08 19:00:00').datetime, 0.001000, 0.001010, 0.0009900, 0.0009900, 12345, 0, 0], [getdate('2018-07-08 20:00:00').datetime, 0.0009900, 0.001011, 0.00091618, 0.0009900, 12345, 0, 0], [getdate('2018-07-08 21:00:00').datetime, 0.001000, 0.001011, 0.00098618, 0.001100, 12345, 0, 1], [getdate('2018-07-08 22:00:00').datetime, 0.001000, 0.001011, 0.00098618, 0.0009900, 12345, 0, 0] ], columns=columns) tc_profit1 = BTContainer(data=data_profit, stop_loss=-0.01, roi=1, trades=1, profit_perc=0.10557, sl=False, remains=False) # should be stoploss - drops 8% tc_profit2 = BTContainer(data=data_profit, stop_loss=-0.10, roi=1, trades=1, profit_perc=0.10557, sl=True, remains=False) tc_loss0 = BTContainer(data=DataFrame([ [getdate('2018-07-08 18:00:00').datetime, 0.0009910, 0.001011, 0.00098618, 0.001000, 12345, 1, 0], [getdate('2018-07-08 19:00:00').datetime, 0.001000, 0.001010, 0.0009900, 0.001000, 12345, 0, 0], [getdate('2018-07-08 20:00:00').datetime, 0.001000, 0.001011, 0.0010618, 0.00091618, 12345, 0, 0], [getdate('2018-07-08 21:00:00').datetime, 0.001000, 0.001011, 0.00098618, 0.00091618, 12345, 0, 0], [getdate('2018-07-08 22:00:00').datetime, 0.001000, 0.001011, 0.00098618, 0.00091618, 12345, 0, 0] ], columns=columns), stop_loss=-0.05, roi=1, trades=1, profit_perc=-0.08839, sl=True, remains=False) # Test 1 Minus 8% Close # Candle Data for test 1 – close at -8% (9200) # Test with Stop-loss at 1% # TC1: Stop-Loss Triggered 1% loss tc1 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9200, 9200, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9960, 9955, 12345, 0, 0], [getdate('2018-06-10 11:00:00').datetime, 9955, 9975, 9955, 9990, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9990, 9990, 9990, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.01, sl=True, remains=False) # should be stop_loss=-0.01, roi=1, trades=1, profit_perc=0.071, sl=False, remains=True) # # Test 2 Minus 4% Low, minus 1% close # Candle Data for test 2 # Test with Stop-Loss at 3% # TC2: Stop-Loss Triggered 3% Loss tc2 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9925, 9950, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9600, 9925, 12345, 0, 0], [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.03, sl=True, remains=False) #should be stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.00999, sl=False, remains=True) # # Test 3 Candle drops 4%, Recovers 1%. # Entry Criteria Met # Candle drops 20% # Candle Data for test 3 # Test with Stop-Loss at 2% # TC3: Trade-A: Stop-Loss Triggered 2% Loss # Trade-B: Stop-Loss Triggered 2% Loss tc3 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9600, 9950, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 1, 0], [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 8000, 8000, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9950, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.02, roi=1, trades=2, profit_perc=-0.4, sl=True, remains=False) #should be stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.19999, sl=True, remains=False) # # Test 4 Minus 3% / recovery +15% # Candle Data for test 4 – Candle drops 3% Closed 15% up # Test with Stop-loss at 2% ROI 6% # TC4: Stop-Loss Triggered 2% Loss tc4 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 11500, 9700, 11500, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9875, 9900, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.02, sl=False, remains=False) #should be stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.141, sl=True, remains=False) # Test 5 / Drops 0.5% Closes +20% # Candle Data for test 5 # Set stop-loss at 1% ROI 3% # TC5: ROI triggers 3% Gain tc5 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 10050, 9950, 9975, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 12000, 9950, 12000, 12345, 0, 0], [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.01, roi=0.03, trades=1, profit_perc=0.03, sl=False, remains=False) #should be stop_loss=-0.01, roi=0.03, trades=1, profit_perc=0.197, sl=False, remains=False) # Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve # Candle Data for test 6 # Set stop-loss at 2% ROI at 5% # TC6: Stop-Loss triggers 2% Loss tc6 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9700, 10100, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.02, sl=False, remains=False) #should be stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.025, sl=False, remains=True) # # Test 7 - 6% Positive / 1% Negative / Close 1% Positve # Candle Data for test 7 # Set stop-loss at 2% ROI at 3% # TC7: ROI Triggers 3% Gain tc7 = BTContainer(data=DataFrame([ [getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0], [getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9900, 10100, 12345, 0, 0], [getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0], [getdate('2018-06-10 11:00:00').datetime, 9925, 9975, 9945, 9900, 12345, 0, 0], [getdate('2018-06-10 12:00:00').datetime, 9900, 9950, 9850, 9900, 12345, 0, 0] ], columns=columns), # stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.03, sl=False, remains=False) #should be stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.025, sl=False, remains=True) # TESTS = [ # tc_profit1, # tc_profit2, # tc_loss0, tc1, tc2, tc3, tc4, tc5, tc6, tc7, ] @pytest.mark.parametrize("data", TESTS) def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None: """ run functional tests """ default_conf["stoploss"] = data.stop_loss default_conf["minimal_roi"] = {"0": data.roi} mocker.patch('freqtrade.exchange.Exchange.get_fee', fee) mocker.patch.multiple('freqtrade.analyze.Analyze', populate_sell_trend=MagicMock(return_value=data.data), populate_buy_trend=MagicMock(return_value=data.data)) patch_exchange(mocker) backtesting = Backtesting(default_conf) caplog.set_level(logging.DEBUG) pair = 'UNITTEST/BTC' # Dummy data as we mock the analyze functions data_processed = {pair: DataFrame()} results = backtesting.backtest( { 'stake_amount': default_conf['stake_amount'], 'processed': data_processed, 'max_open_trades': 10, 'realistic': True } ) print(results.T) assert len(results) == data.trades assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3) if data.sl: assert log_has("Stop loss hit.", caplog.record_tuples) else: assert not log_has("Stop loss hit.", caplog.record_tuples) log_test = (f'Force_selling still open trade UNITTEST/BTC with ' f'{results.iloc[-1].profit_percent} perc - {results.iloc[-1].profit_abs}') if data.remains: assert log_has(log_test, caplog.record_tuples) else: assert not log_has(log_test, caplog.record_tuples)