stable/freqtrade/tests/optimize/test_backtest_detail.py

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# 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.strategy.interface import SellType
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
sell_r: SellType
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, sell_r=SellType.STOP_LOSS) # should be stoploss - drops 8%
tc_profit2 = BTContainer(data=data_profit, stop_loss=-0.10, roi=1,
trades=1, profit_perc=0.10557, sell_r=SellType.STOP_LOSS)
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, sell_r=SellType.STOP_LOSS)
# 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 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 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, sell_r=SellType.STOP_LOSS) # should be
# stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.003, sell_r=SellType.FORCE_SELL) #
# 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 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 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, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.03, roi=1, trades=1, profit_perc=-0.007, sell_r=SellType.FORCE_SELL) #
# 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 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 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, 9950, 10000, 9900, 9925, 12345, 0, 0],
[getdate('2018-06-10 12:00:00').datetime, 9925, 9975, 8000, 8000, 12345, 0, 0],
[getdate('2018-06-10 13:00:00').datetime, 9900, 9950, 9950, 9900, 12345, 0, 0]
], columns=columns),
stop_loss=-0.02, roi=1, trades=2, profit_perc=-0.04, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.02, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=1, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
# 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 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 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, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=0.06, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL)
# 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 07:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9960, 9975, 12345, 0, 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, sell_r=SellType.ROI) #should be
# stop_loss=-0.01, roi=0.03, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL)
# 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 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 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, sell_r=SellType.STOP_LOSS) #should be
# stop_loss=-0.02, roi=0.05, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
# 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 07:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 1, 0],
[getdate('2018-06-10 08:00:00').datetime, 10000, 10050, 9950, 9975, 12345, 0, 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, sell_r=SellType.ROI) #should be
# stop_loss=-0.02, roi=0.03, trades=1, profit_perc=-0.012, sell_r=SellType.FORCE_SELL) #
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)
# TODO: don't Mock fee to for now
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
backtesting.advise_buy = lambda a, m: data.data
backtesting.advise_sell = lambda a, m: data.data
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,
}
)
print(results.T)
assert len(results) == data.trades
assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
if data.sell_r == SellType.STOP_LOSS:
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.sell_r == SellType.FORCE_SELL:
assert log_has(log_test,
caplog.record_tuples)
else:
assert not log_has(log_test,
caplog.record_tuples)