230 lines
10 KiB
Python
230 lines
10 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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import logging
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from unittest.mock import MagicMock
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from typing import NamedTuple
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from pandas import DataFrame
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import pytest
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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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class BTContainer(NamedTuple):
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"""
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NamedTuple Defining BacktestResults inputs.
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"""
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data: DataFrame
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stop_loss: float
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roi: float
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trades: int
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profit_perc: float
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sell_r: SellType
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columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
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data_profit = DataFrame([
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[getdate('2018-07-08 18:00:00').datetime, 0.0009910,
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0.001011, 0.00098618, 0.001000, 12345, 1, 0],
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[getdate('2018-07-08 19:00:00').datetime, 0.001000,
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0.001010, 0.0009900, 0.0009900, 12345, 0, 0],
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[getdate('2018-07-08 20:00:00').datetime, 0.0009900,
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0.001011, 0.00091618, 0.0009900, 12345, 0, 0],
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[getdate('2018-07-08 21:00:00').datetime, 0.001000,
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0.001011, 0.00098618, 0.001100, 12345, 0, 1],
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[getdate('2018-07-08 22:00:00').datetime, 0.001000,
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0.001011, 0.00098618, 0.0009900, 12345, 0, 0]
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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, 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, sell_r=SellType.STOP_LOSS)
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tc_loss0 = BTContainer(data=DataFrame([
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[getdate('2018-07-08 18:00:00').datetime, 0.0009910,
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0.001011, 0.00098618, 0.001000, 12345, 1, 0],
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[getdate('2018-07-08 19:00:00').datetime, 0.001000,
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0.001010, 0.0009900, 0.001000, 12345, 0, 0],
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[getdate('2018-07-08 20:00:00').datetime, 0.001000,
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0.001011, 0.0010618, 0.00091618, 12345, 0, 0],
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[getdate('2018-07-08 21:00:00').datetime, 0.001000,
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0.001011, 0.00098618, 0.00091618, 12345, 0, 0],
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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, sell_r=SellType.STOP_LOSS)
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# Test 1 Minus 8% Close
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# Candle Data for test 1 – close at -8% (9200)
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# Test with Stop-loss at 1%
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# TC1: Stop-Loss Triggered 1% loss
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tc1 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9200, 9200, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9960, 9955, 12345, 0, 0],
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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, 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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# Candle Data for test 2
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# Test with Stop-Loss at 3%
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# TC2: Stop-Loss Triggered 3% Loss
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tc2 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9925, 9950, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9600, 9925, 12345, 0, 0],
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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, 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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# Entry Criteria Met
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# Candle drops 20%
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# Candle Data for test 3
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# Test with Stop-Loss at 2%
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# TC3: Trade-A: Stop-Loss Triggered 2% Loss
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# Trade-B: Stop-Loss Triggered 2% Loss
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tc3 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 10025, 9600, 9950, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 1, 0],
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[getdate('2018-06-10 11:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
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[getdate('2018-06-10 12:00:00').datetime, 9925, 9975, 8000, 8000, 12345, 0, 0],
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[getdate('2018-06-10 13:00:00').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, 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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# Candle Data for test 4 – Candle drops 3% Closed 15% up
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# Test with Stop-loss at 2% ROI 6%
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# TC4: Stop-Loss Triggered 2% Loss
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tc4 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 11500, 9700, 11500, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
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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, 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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# Set stop-loss at 1% ROI 3%
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# TC5: ROI triggers 3% Gain
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tc5 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 12000, 9950, 12000, 12345, 0, 0],
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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, 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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# Set stop-loss at 2% ROI at 5%
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# TC6: Stop-Loss triggers 2% Loss
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tc6 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9700, 10100, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
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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, 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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# Set stop-loss at 2% ROI at 3%
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# TC7: ROI Triggers 3% Gain
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tc7 = BTContainer(data=DataFrame([
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[getdate('2018-06-10 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
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[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 0],
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[getdate('2018-06-10 09:00:00').datetime, 9975, 10600, 9900, 10100, 12345, 0, 0],
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[getdate('2018-06-10 10:00:00').datetime, 9950, 10000, 9900, 9925, 12345, 0, 0],
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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, 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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# tc_profit2,
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# tc_loss0,
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tc1,
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tc2,
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tc3,
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tc4,
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tc5,
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tc6,
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tc7,
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]
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@pytest.mark.parametrize("data", TESTS)
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def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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"""
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run functional tests
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"""
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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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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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# Dummy data as we mock the analyze functions
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data_processed = {pair: DataFrame()}
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results = backtesting.backtest(
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{
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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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}
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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.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.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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assert not log_has(log_test,
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caplog.record_tuples)
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