freqtrade_origin/freqtrade/tests/optimize/test_backtest_detail.py

# 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: float


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)  # 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)


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)


# Test 1 Minus 8% Close
# Candle Data for test 1 – close at -8% (9200)
# Test with Stop-loss at 1%
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, 9925, 9950, 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, 9200, 9200, 12345, 0, 0]
], columns=columns),
    stop_loss=-0.01, roi=1, trades=1, profit_perc=-0.07999, sl=True)

# Test 2 Minus 4% Low, minus 1% close
# Candle Data for test 2
# Test with Stop-Loss at 3%
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=-0.00999, sl=False)  #


# 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 = 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=1, profit_perc=-0.19999, sl=True)  #


# 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 = 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=-0.141, sl=True)

# Test 5 / Drops 0.5% Closes +20%
# Candle Data for test 5
# Set stop-loss at 1% ROI 3%
tc5 = 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, 12000, 9950, 12000, 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=-0.01, roi=0.03, trades=1, profit_perc=-0.177, sl=True)

# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
# Candle Data for test 6
# Set stop-loss at 2% ROI at 5%
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=-0.025, sl=False)

# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
# Candle Data for test 7
# Set stop-loss at 2% ROI at 3%

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=-0.025, sl=False)

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)