1203 lines
47 KiB
Python
1203 lines
47 KiB
Python
# pragma pylint: disable=missing-docstring,W0212,C0103
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import locale
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import logging
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from datetime import datetime
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from pathlib import Path
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from copy import deepcopy
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from typing import Dict, List
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from unittest.mock import MagicMock, PropertyMock
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import pandas as pd
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import pytest
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from arrow import Arrow
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from filelock import Timeout
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from freqtrade import constants
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from freqtrade.commands.optimize_commands import (setup_optimize_configuration,
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start_hyperopt)
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from freqtrade.data.history import load_data
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from freqtrade.exceptions import DependencyException, OperationalException
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from freqtrade.optimize.default_hyperopt_loss import DefaultHyperOptLoss
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from freqtrade.optimize.hyperopt import Hyperopt
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from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
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HyperOptResolver)
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from freqtrade.state import RunMode
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from freqtrade.strategy.interface import SellType
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from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
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patched_configuration_load_config_file)
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from .hyperopts.default_hyperopt import DefaultHyperOpt
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@pytest.fixture(scope='function')
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def hyperopt_conf(default_conf):
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hyperconf = deepcopy(default_conf)
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hyperconf.update({
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'hyperopt': 'DefaultHyperOpt',
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'hyperopt_path': str(Path(__file__).parent / 'hyperopts'),
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'epochs': 1,
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'timerange': None,
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'spaces': ['default'],
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'hyperopt_jobs': 1,
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})
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return hyperconf
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@pytest.fixture(scope='function')
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def hyperopt(hyperopt_conf, mocker):
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patch_exchange(mocker)
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return Hyperopt(hyperopt_conf)
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@pytest.fixture(scope='function')
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def hyperopt_results():
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return pd.DataFrame(
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{
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'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
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'profit_percent': [-0.1, 0.2, 0.3],
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'profit_abs': [-0.2, 0.4, 0.6],
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'trade_duration': [10, 30, 10],
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
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'close_time':
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[
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datetime(2019, 1, 1, 9, 26, 3, 478039),
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datetime(2019, 2, 1, 9, 26, 3, 478039),
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datetime(2019, 3, 1, 9, 26, 3, 478039)
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]
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}
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)
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# Functions for recurrent object patching
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def create_results(mocker, hyperopt, testdatadir) -> List[Dict]:
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"""
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When creating results, mock the hyperopt so that *by default*
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- we don't create any pickle'd files in the filesystem
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- we might have a pickle'd file so make sure that we return
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false when looking for it
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"""
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hyperopt.results_file = testdatadir / 'optimize/ut_results.pickle'
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mocker.patch.object(Path, "is_file", MagicMock(return_value=False))
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stat_mock = MagicMock()
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stat_mock.st_size = PropertyMock(return_value=1)
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mocker.patch.object(Path, "stat", MagicMock(return_value=False))
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mocker.patch.object(Path, "unlink", MagicMock(return_value=True))
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mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
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return [{'loss': 1, 'result': 'foo', 'params': {}}]
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def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
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patched_configuration_load_config_file(mocker, default_conf)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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]
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config = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
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assert 'max_open_trades' in config
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assert 'stake_currency' in config
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assert 'stake_amount' in config
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assert 'exchange' in config
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assert 'pair_whitelist' in config['exchange']
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assert 'datadir' in config
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assert log_has('Using data directory: {} ...'.format(config['datadir']), caplog)
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assert 'timeframe' in config
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assert not log_has_re('Parameter -i/--ticker-interval detected .*', caplog)
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assert 'position_stacking' not in config
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assert not log_has('Parameter --enable-position-stacking detected ...', caplog)
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assert 'timerange' not in config
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assert 'runmode' in config
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assert config['runmode'] == RunMode.HYPEROPT
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def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplog) -> None:
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patched_configuration_load_config_file(mocker, default_conf)
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mocker.patch(
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'freqtrade.configuration.configuration.create_datadir',
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lambda c, x: x
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)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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'--datadir', '/foo/bar',
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'--timeframe', '1m',
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'--timerange', ':100',
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'--enable-position-stacking',
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'--disable-max-market-positions',
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'--epochs', '1000',
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'--spaces', 'default',
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'--print-all'
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]
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config = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
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assert 'max_open_trades' in config
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assert 'stake_currency' in config
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assert 'stake_amount' in config
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assert 'exchange' in config
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assert 'pair_whitelist' in config['exchange']
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assert 'datadir' in config
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assert config['runmode'] == RunMode.HYPEROPT
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assert log_has('Using data directory: {} ...'.format(config['datadir']), caplog)
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assert 'timeframe' in config
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assert log_has('Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
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caplog)
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assert 'position_stacking' in config
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assert log_has('Parameter --enable-position-stacking detected ...', caplog)
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assert 'use_max_market_positions' in config
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assert log_has('Parameter --disable-max-market-positions detected ...', caplog)
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assert log_has('max_open_trades set to unlimited ...', caplog)
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assert 'timerange' in config
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assert log_has('Parameter --timerange detected: {} ...'.format(config['timerange']), caplog)
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assert 'epochs' in config
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assert log_has('Parameter --epochs detected ... Will run Hyperopt with for 1000 epochs ...',
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caplog)
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assert 'spaces' in config
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assert log_has('Parameter -s/--spaces detected: {}'.format(config['spaces']), caplog)
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assert 'print_all' in config
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assert log_has('Parameter --print-all detected ...', caplog)
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def test_setup_hyperopt_configuration_unlimited_stake_amount(mocker, default_conf) -> None:
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default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
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patched_configuration_load_config_file(mocker, default_conf)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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]
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with pytest.raises(DependencyException, match=r'.`stake_amount`.*'):
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setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
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def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
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patched_configuration_load_config_file(mocker, default_conf)
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hyperopt = DefaultHyperOpt
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delattr(hyperopt, 'populate_indicators')
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delattr(hyperopt, 'populate_buy_trend')
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delattr(hyperopt, 'populate_sell_trend')
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mocker.patch(
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'freqtrade.resolvers.hyperopt_resolver.HyperOptResolver.load_object',
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MagicMock(return_value=hyperopt(default_conf))
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)
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default_conf.update({'hyperopt': 'DefaultHyperOpt'})
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x = HyperOptResolver.load_hyperopt(default_conf)
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assert not hasattr(x, 'populate_indicators')
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assert not hasattr(x, 'populate_buy_trend')
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assert not hasattr(x, 'populate_sell_trend')
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assert log_has("Hyperopt class does not provide populate_indicators() method. "
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"Using populate_indicators from the strategy.", caplog)
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assert log_has("Hyperopt class does not provide populate_sell_trend() method. "
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"Using populate_sell_trend from the strategy.", caplog)
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assert log_has("Hyperopt class does not provide populate_buy_trend() method. "
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"Using populate_buy_trend from the strategy.", caplog)
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assert hasattr(x, "ticker_interval") # DEPRECATED
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assert hasattr(x, "timeframe")
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def test_hyperoptresolver_wrongname(default_conf) -> None:
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default_conf.update({'hyperopt': "NonExistingHyperoptClass"})
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with pytest.raises(OperationalException, match=r'Impossible to load Hyperopt.*'):
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HyperOptResolver.load_hyperopt(default_conf)
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def test_hyperoptresolver_noname(default_conf):
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default_conf['hyperopt'] = ''
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with pytest.raises(OperationalException,
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match="No Hyperopt set. Please use `--hyperopt` to specify "
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"the Hyperopt class to use."):
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HyperOptResolver.load_hyperopt(default_conf)
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def test_hyperoptlossresolver(mocker, default_conf) -> None:
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hl = DefaultHyperOptLoss
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mocker.patch(
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'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver.load_object',
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MagicMock(return_value=hl)
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)
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x = HyperOptLossResolver.load_hyperoptloss(default_conf)
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assert hasattr(x, "hyperopt_loss_function")
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def test_hyperoptlossresolver_wrongname(default_conf) -> None:
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default_conf.update({'hyperopt_loss': "NonExistingLossClass"})
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with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'):
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HyperOptLossResolver.load_hyperoptloss(default_conf)
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def test_start_not_installed(mocker, default_conf, import_fails) -> None:
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start_mock = MagicMock()
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patched_configuration_load_config_file(mocker, default_conf)
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mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
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patch_exchange(mocker)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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'--hyperopt-path',
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str(Path(__file__).parent / "hyperopts"),
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'--epochs', '5'
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]
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pargs = get_args(args)
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with pytest.raises(OperationalException, match=r"Please ensure that the hyperopt dependencies"):
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start_hyperopt(pargs)
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def test_start(mocker, hyperopt_conf, caplog) -> None:
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start_mock = MagicMock()
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patched_configuration_load_config_file(mocker, hyperopt_conf)
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mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
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patch_exchange(mocker)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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'--epochs', '5'
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]
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pargs = get_args(args)
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start_hyperopt(pargs)
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assert log_has('Starting freqtrade in Hyperopt mode', caplog)
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assert start_mock.call_count == 1
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def test_start_no_data(mocker, hyperopt_conf) -> None:
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patched_configuration_load_config_file(mocker, hyperopt_conf)
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mocker.patch('freqtrade.data.history.load_pair_history', MagicMock(return_value=pd.DataFrame))
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mocker.patch(
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'freqtrade.optimize.hyperopt.get_timerange',
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MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
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)
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patch_exchange(mocker)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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'--epochs', '5'
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]
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pargs = get_args(args)
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with pytest.raises(OperationalException, match='No data found. Terminating.'):
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start_hyperopt(pargs)
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def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
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start_mock = MagicMock(side_effect=Timeout(Hyperopt.get_lock_filename(hyperopt_conf)))
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patched_configuration_load_config_file(mocker, hyperopt_conf)
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mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
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patch_exchange(mocker)
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args = [
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'hyperopt',
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'--config', 'config.json',
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'--hyperopt', 'DefaultHyperOpt',
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'--epochs', '5'
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]
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pargs = get_args(args)
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start_hyperopt(pargs)
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assert log_has("Another running instance of freqtrade Hyperopt detected.", caplog)
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def test_loss_calculation_prefer_correct_trade_count(default_conf, hyperopt_results) -> None:
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(hyperopt_results, 600 + 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(hyperopt_results, 600 - 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over > correct
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assert under > correct
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def test_loss_calculation_prefer_shorter_trades(default_conf, hyperopt_results) -> None:
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resultsb = hyperopt_results.copy()
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resultsb.loc[1, 'trade_duration'] = 20
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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longer = hl.hyperopt_loss_function(hyperopt_results, 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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shorter = hl.hyperopt_loss_function(resultsb, 100,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert shorter < longer
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def test_loss_calculation_has_limited_profit(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, 600,
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_sharpe_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'SharpeHyperOptLoss'})
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'})
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_sortino_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'SortinoHyperOptLoss'})
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'SortinoHyperOptLossDaily'})
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hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
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correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
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datetime(2019, 1, 1), datetime(2019, 5, 1))
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assert over < correct
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assert under > correct
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def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
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results_over = hyperopt_results.copy()
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results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
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results_under = hyperopt_results.copy()
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results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
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default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'})
|
|
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
|
|
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
|
|
datetime(2019, 1, 1), datetime(2019, 5, 1))
|
|
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
|
|
datetime(2019, 1, 1), datetime(2019, 5, 1))
|
|
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
|
|
datetime(2019, 1, 1), datetime(2019, 5, 1))
|
|
assert over < correct
|
|
assert under > correct
|
|
|
|
|
|
def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
|
|
hyperopt.current_best_loss = 2
|
|
hyperopt.total_epochs = 2
|
|
|
|
hyperopt.print_results(
|
|
{
|
|
'loss': 1,
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
},
|
|
'total_profit': 0,
|
|
'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
|
|
'is_initial_point': False,
|
|
'is_best': True
|
|
}
|
|
)
|
|
out, err = capsys.readouterr()
|
|
assert all(x in out
|
|
for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC (1.00%)", "20.0 m"])
|
|
|
|
|
|
def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
|
|
hyperopt.current_best_loss = 2
|
|
hyperopt.print_results(
|
|
{
|
|
'is_best': False,
|
|
'loss': 3,
|
|
'current_epoch': 1,
|
|
}
|
|
)
|
|
assert caplog.record_tuples == []
|
|
|
|
|
|
def test_save_results_saves_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
|
|
epochs = create_results(mocker, hyperopt, testdatadir)
|
|
mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
|
|
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
|
|
|
|
caplog.set_level(logging.DEBUG)
|
|
|
|
hyperopt.epochs = epochs
|
|
hyperopt._save_results()
|
|
assert log_has(f"1 epoch saved to '{results_file}'.", caplog)
|
|
mock_dump.assert_called_once()
|
|
|
|
hyperopt.epochs = epochs + epochs
|
|
hyperopt._save_results()
|
|
assert log_has(f"2 epochs saved to '{results_file}'.", caplog)
|
|
|
|
|
|
def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
|
|
epochs = create_results(mocker, hyperopt, testdatadir)
|
|
mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=epochs)
|
|
results_file = testdatadir / 'optimize' / 'ut_results.pickle'
|
|
hyperopt_epochs = hyperopt._read_results(results_file)
|
|
assert log_has(f"Reading epochs from '{results_file}'", caplog)
|
|
assert hyperopt_epochs == epochs
|
|
mock_load.assert_called_once()
|
|
|
|
|
|
def test_roi_table_generation(hyperopt) -> None:
|
|
params = {
|
|
'roi_t1': 5,
|
|
'roi_t2': 10,
|
|
'roi_t3': 15,
|
|
'roi_p1': 1,
|
|
'roi_p2': 2,
|
|
'roi_p3': 3,
|
|
}
|
|
|
|
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
|
|
|
|
|
|
def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result',
|
|
'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
},
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
# Co-test loading timeframe from strategy
|
|
del hyperopt_conf['timeframe']
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
|
|
assert hasattr(hyperopt, "max_open_trades")
|
|
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
|
|
assert hasattr(hyperopt, "position_stacking")
|
|
|
|
|
|
def test_format_results(hyperopt):
|
|
# Test with BTC as stake_currency
|
|
trades = [
|
|
('ETH/BTC', 2, 2, 123),
|
|
('LTC/BTC', 1, 1, 123),
|
|
('XPR/BTC', -1, -2, -246)
|
|
]
|
|
labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
|
|
df = pd.DataFrame.from_records(trades, columns=labels)
|
|
results_metrics = hyperopt._calculate_results_metrics(df)
|
|
results_explanation = hyperopt._format_results_explanation_string(results_metrics)
|
|
total_profit = results_metrics['total_profit']
|
|
|
|
results = {
|
|
'loss': 0.0,
|
|
'params_dict': None,
|
|
'params_details': None,
|
|
'results_metrics': results_metrics,
|
|
'results_explanation': results_explanation,
|
|
'total_profit': total_profit,
|
|
'current_epoch': 1,
|
|
'is_initial_point': True,
|
|
}
|
|
|
|
result = hyperopt._format_explanation_string(results, 1)
|
|
assert result.find(' 66.67%')
|
|
assert result.find('Total profit 1.00000000 BTC')
|
|
assert result.find('2.0000Σ %')
|
|
|
|
# Test with EUR as stake_currency
|
|
trades = [
|
|
('ETH/EUR', 2, 2, 123),
|
|
('LTC/EUR', 1, 1, 123),
|
|
('XPR/EUR', -1, -2, -246)
|
|
]
|
|
df = pd.DataFrame.from_records(trades, columns=labels)
|
|
results_metrics = hyperopt._calculate_results_metrics(df)
|
|
results['total_profit'] = results_metrics['total_profit']
|
|
result = hyperopt._format_explanation_string(results, 1)
|
|
assert result.find('Total profit 1.00000000 EUR')
|
|
|
|
|
|
@pytest.mark.parametrize("spaces, expected_results", [
|
|
(['buy'],
|
|
{'buy': True, 'sell': False, 'roi': False, 'stoploss': False, 'trailing': False}),
|
|
(['sell'],
|
|
{'buy': False, 'sell': True, 'roi': False, 'stoploss': False, 'trailing': False}),
|
|
(['roi'],
|
|
{'buy': False, 'sell': False, 'roi': True, 'stoploss': False, 'trailing': False}),
|
|
(['stoploss'],
|
|
{'buy': False, 'sell': False, 'roi': False, 'stoploss': True, 'trailing': False}),
|
|
(['trailing'],
|
|
{'buy': False, 'sell': False, 'roi': False, 'stoploss': False, 'trailing': True}),
|
|
(['buy', 'sell', 'roi', 'stoploss'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
|
|
(['buy', 'sell', 'roi', 'stoploss', 'trailing'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
|
|
(['buy', 'roi'],
|
|
{'buy': True, 'sell': False, 'roi': True, 'stoploss': False, 'trailing': False}),
|
|
(['all'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
|
|
(['default'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
|
|
(['default', 'trailing'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
|
|
(['all', 'buy'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': True}),
|
|
(['default', 'buy'],
|
|
{'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
|
|
])
|
|
def test_has_space(hyperopt, spaces, expected_results):
|
|
for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
|
|
hyperopt.config.update({'spaces': spaces})
|
|
assert hyperopt.has_space(s) == expected_results[s]
|
|
|
|
|
|
def test_populate_indicators(hyperopt, testdatadir) -> None:
|
|
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
|
|
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
|
|
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
|
|
{'pair': 'UNITTEST/BTC'})
|
|
|
|
# Check if some indicators are generated. We will not test all of them
|
|
assert 'adx' in dataframe
|
|
assert 'mfi' in dataframe
|
|
assert 'rsi' in dataframe
|
|
|
|
|
|
def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
|
|
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
|
|
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
|
|
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
|
|
{'pair': 'UNITTEST/BTC'})
|
|
|
|
populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
|
|
{
|
|
'adx-value': 20,
|
|
'fastd-value': 20,
|
|
'mfi-value': 20,
|
|
'rsi-value': 20,
|
|
'adx-enabled': True,
|
|
'fastd-enabled': True,
|
|
'mfi-enabled': True,
|
|
'rsi-enabled': True,
|
|
'trigger': 'bb_lower'
|
|
}
|
|
)
|
|
result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
|
|
# Check if some indicators are generated. We will not test all of them
|
|
assert 'buy' in result
|
|
assert 1 in result['buy']
|
|
|
|
|
|
def test_sell_strategy_generator(hyperopt, testdatadir) -> None:
|
|
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
|
|
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
|
|
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
|
|
{'pair': 'UNITTEST/BTC'})
|
|
|
|
populate_sell_trend = hyperopt.custom_hyperopt.sell_strategy_generator(
|
|
{
|
|
'sell-adx-value': 20,
|
|
'sell-fastd-value': 75,
|
|
'sell-mfi-value': 80,
|
|
'sell-rsi-value': 20,
|
|
'sell-adx-enabled': True,
|
|
'sell-fastd-enabled': True,
|
|
'sell-mfi-enabled': True,
|
|
'sell-rsi-enabled': True,
|
|
'sell-trigger': 'sell-bb_upper'
|
|
}
|
|
)
|
|
result = populate_sell_trend(dataframe, {'pair': 'UNITTEST/BTC'})
|
|
# Check if some indicators are generated. We will not test all of them
|
|
print(result)
|
|
assert 'sell' in result
|
|
assert 1 in result['sell']
|
|
|
|
|
|
def test_generate_optimizer(mocker, hyperopt_conf) -> None:
|
|
hyperopt_conf.update({'spaces': 'all',
|
|
'hyperopt_min_trades': 1,
|
|
})
|
|
|
|
trades = [
|
|
('TRX/BTC', 0.023117, 0.000233, 100)
|
|
]
|
|
labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
|
|
backtest_result = pd.DataFrame.from_records(trades, columns=labels)
|
|
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Backtesting.backtest',
|
|
MagicMock(return_value=backtest_result)
|
|
)
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
|
|
)
|
|
patch_exchange(mocker)
|
|
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
|
|
|
|
optimizer_param = {
|
|
'adx-value': 0,
|
|
'fastd-value': 35,
|
|
'mfi-value': 0,
|
|
'rsi-value': 0,
|
|
'adx-enabled': False,
|
|
'fastd-enabled': True,
|
|
'mfi-enabled': False,
|
|
'rsi-enabled': False,
|
|
'trigger': 'macd_cross_signal',
|
|
'sell-adx-value': 0,
|
|
'sell-fastd-value': 75,
|
|
'sell-mfi-value': 0,
|
|
'sell-rsi-value': 0,
|
|
'sell-adx-enabled': False,
|
|
'sell-fastd-enabled': True,
|
|
'sell-mfi-enabled': False,
|
|
'sell-rsi-enabled': False,
|
|
'sell-trigger': 'macd_cross_signal',
|
|
'roi_t1': 60.0,
|
|
'roi_t2': 30.0,
|
|
'roi_t3': 20.0,
|
|
'roi_p1': 0.01,
|
|
'roi_p2': 0.01,
|
|
'roi_p3': 0.1,
|
|
'stoploss': -0.4,
|
|
'trailing_stop': True,
|
|
'trailing_stop_positive': 0.02,
|
|
'trailing_stop_positive_offset_p1': 0.05,
|
|
'trailing_only_offset_is_reached': False,
|
|
}
|
|
response_expected = {
|
|
'loss': 1.9840569076926293,
|
|
'results_explanation': (' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
|
|
'( 2.31\N{GREEK CAPITAL LETTER SIGMA}%). Avg duration 100.0 min.'
|
|
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
|
|
'params_details': {'buy': {'adx-enabled': False,
|
|
'adx-value': 0,
|
|
'fastd-enabled': True,
|
|
'fastd-value': 35,
|
|
'mfi-enabled': False,
|
|
'mfi-value': 0,
|
|
'rsi-enabled': False,
|
|
'rsi-value': 0,
|
|
'trigger': 'macd_cross_signal'},
|
|
'roi': {0: 0.12000000000000001,
|
|
20.0: 0.02,
|
|
50.0: 0.01,
|
|
110.0: 0},
|
|
'sell': {'sell-adx-enabled': False,
|
|
'sell-adx-value': 0,
|
|
'sell-fastd-enabled': True,
|
|
'sell-fastd-value': 75,
|
|
'sell-mfi-enabled': False,
|
|
'sell-mfi-value': 0,
|
|
'sell-rsi-enabled': False,
|
|
'sell-rsi-value': 0,
|
|
'sell-trigger': 'macd_cross_signal'},
|
|
'stoploss': {'stoploss': -0.4},
|
|
'trailing': {'trailing_only_offset_is_reached': False,
|
|
'trailing_stop': True,
|
|
'trailing_stop_positive': 0.02,
|
|
'trailing_stop_positive_offset': 0.07}},
|
|
'params_dict': optimizer_param,
|
|
'results_metrics': {'avg_profit': 2.3117,
|
|
'duration': 100.0,
|
|
'profit': 2.3117,
|
|
'total_profit': 0.000233,
|
|
'trade_count': 1},
|
|
'total_profit': 0.00023300
|
|
}
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.dimensions = hyperopt.hyperopt_space()
|
|
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
|
|
assert generate_optimizer_value == response_expected
|
|
|
|
|
|
def test_clean_hyperopt(mocker, hyperopt_conf, caplog):
|
|
patch_exchange(mocker)
|
|
|
|
mocker.patch("freqtrade.optimize.hyperopt.Path.is_file", MagicMock(return_value=True))
|
|
unlinkmock = mocker.patch("freqtrade.optimize.hyperopt.Path.unlink", MagicMock())
|
|
h = Hyperopt(hyperopt_conf)
|
|
|
|
assert unlinkmock.call_count == 2
|
|
assert log_has(f"Removing `{h.data_pickle_file}`.", caplog)
|
|
|
|
|
|
def test_continue_hyperopt(mocker, hyperopt_conf, caplog):
|
|
patch_exchange(mocker)
|
|
hyperopt_conf.update({'hyperopt_continue': True})
|
|
mocker.patch("freqtrade.optimize.hyperopt.Path.is_file", MagicMock(return_value=True))
|
|
unlinkmock = mocker.patch("freqtrade.optimize.hyperopt.Path.unlink", MagicMock())
|
|
Hyperopt(hyperopt_conf)
|
|
|
|
assert unlinkmock.call_count == 0
|
|
assert log_has("Continuing on previous hyperopt results.", caplog)
|
|
|
|
|
|
def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
|
'params_details': {
|
|
'buy': {'mfi-value': None},
|
|
'sell': {'sell-mfi-value': None},
|
|
'roi': {}, 'stoploss': {'stoploss': None},
|
|
'trailing': {'trailing_stop': None}
|
|
},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
}
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': 'all',
|
|
'hyperopt_jobs': 1,
|
|
'print_json': True,
|
|
})
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
result_str = (
|
|
'{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi"'
|
|
':{},"stoploss":null,"trailing_stop":null}'
|
|
)
|
|
assert result_str in out # noqa: E501
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
|
|
|
|
def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
|
'params_details': {
|
|
'buy': {'mfi-value': None},
|
|
'sell': {'sell-mfi-value': None},
|
|
'roi': {}, 'stoploss': {'stoploss': None}
|
|
},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
}
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'print_json': True})
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}' in out # noqa: E501
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
|
|
|
|
def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
|
'params_details': {'roi': {}, 'stoploss': {'stoploss': None}},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
}
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': 'roi stoploss',
|
|
'hyperopt_jobs': 1,
|
|
'print_json': True,
|
|
})
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
assert '{"minimal_roi":{},"stoploss":null}' in out
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
|
|
|
|
def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
}
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': 'roi stoploss'})
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
|
|
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
|
|
del hyperopt.custom_hyperopt.__class__.indicator_space
|
|
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
|
|
assert hasattr(hyperopt, "max_open_trades")
|
|
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
|
|
assert hasattr(hyperopt, "position_stacking")
|
|
|
|
|
|
def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
|
|
mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': 'all', })
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
|
|
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
|
|
del hyperopt.custom_hyperopt.__class__.indicator_space
|
|
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
|
|
|
|
with pytest.raises(OperationalException, match=r"The 'buy' space is included into *"):
|
|
hyperopt.start()
|
|
|
|
|
|
def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
}
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': 'buy'})
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
# TODO: sell_strategy_generator() is actually not called because
|
|
# run_optimizer_parallel() is mocked
|
|
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
|
|
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
|
|
assert hasattr(hyperopt, "max_open_trades")
|
|
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
|
|
assert hasattr(hyperopt, "position_stacking")
|
|
|
|
|
|
def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
|
|
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
parallel = mocker.patch(
|
|
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
|
MagicMock(return_value=[{
|
|
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
|
'results_metrics':
|
|
{
|
|
'trade_count': 1,
|
|
'avg_profit': 0.1,
|
|
'total_profit': 0.001,
|
|
'profit': 1.0,
|
|
'duration': 20.0
|
|
}
|
|
}])
|
|
)
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': 'sell', })
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
# TODO: buy_strategy_generator() is actually not called because
|
|
# run_optimizer_parallel() is mocked
|
|
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
|
|
del hyperopt.custom_hyperopt.__class__.indicator_space
|
|
|
|
hyperopt.start()
|
|
|
|
parallel.assert_called_once()
|
|
|
|
out, err = capsys.readouterr()
|
|
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
|
|
assert dumper.called
|
|
# Should be called twice, once for historical candle data, once to save evaluations
|
|
assert dumper.call_count == 2
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
|
|
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
|
|
assert hasattr(hyperopt, "max_open_trades")
|
|
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
|
|
assert hasattr(hyperopt, "position_stacking")
|
|
|
|
|
|
@pytest.mark.parametrize("method,space", [
|
|
('buy_strategy_generator', 'buy'),
|
|
('indicator_space', 'buy'),
|
|
('sell_strategy_generator', 'sell'),
|
|
('sell_indicator_space', 'sell'),
|
|
])
|
|
def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> None:
|
|
mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
|
|
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
|
|
MagicMock(return_value=(MagicMock(), None)))
|
|
mocker.patch(
|
|
'freqtrade.optimize.hyperopt.get_timerange',
|
|
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
|
|
)
|
|
|
|
patch_exchange(mocker)
|
|
|
|
hyperopt_conf.update({'spaces': space})
|
|
|
|
hyperopt = Hyperopt(hyperopt_conf)
|
|
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
|
|
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
|
|
|
|
delattr(hyperopt.custom_hyperopt.__class__, method)
|
|
|
|
with pytest.raises(OperationalException, match=f"The '{space}' space is included into *"):
|
|
hyperopt.start()
|