stable/freqtrade/tests/optimize/test_hyperopt.py

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# pragma pylint: disable=missing-docstring,W0212,C0103
import os
from copy import deepcopy
from unittest.mock import MagicMock
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import pandas as pd
import pytest
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from freqtrade.optimize.__init__ import load_tickerdata_file
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from freqtrade.optimize.hyperopt import Hyperopt, start
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from freqtrade.strategy.resolver import StrategyResolver
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from freqtrade.tests.conftest import log_has, patch_exchange
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from freqtrade.tests.optimize.test_backtesting import get_args
# Avoid to reinit the same object again and again
_HYPEROPT_INITIALIZED = False
_HYPEROPT = None
@pytest.fixture(scope='function')
def init_hyperopt(default_conf, mocker):
global _HYPEROPT_INITIALIZED, _HYPEROPT
if not _HYPEROPT_INITIALIZED:
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patch_exchange(mocker)
_HYPEROPT = Hyperopt(default_conf)
_HYPEROPT_INITIALIZED = True
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# Functions for recurrent object patching
def create_trials(mocker) -> None:
"""
When creating trials, mock the hyperopt Trials so that *by default*
- we don't create any pickle'd files in the filesystem
- we might have a pickle'd file so make sure that we return
false when looking for it
"""
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_HYPEROPT.trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
mocker.patch('freqtrade.optimize.hyperopt.os.path.exists', return_value=False)
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mocker.patch('freqtrade.optimize.hyperopt.os.path.getsize', return_value=1)
mocker.patch('freqtrade.optimize.hyperopt.os.remove', 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_start(mocker, default_conf, caplog) -> None:
"""
Test start() function
"""
start_mock = MagicMock()
mocker.patch(
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: 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 = [
'--config', 'config.json',
'--strategy', 'DefaultStrategy',
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'hyperopt',
'--epochs', '5'
]
args = get_args(args)
StrategyResolver({'strategy': 'DefaultStrategy'})
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start(args)
import pprint
pprint.pprint(caplog.record_tuples)
assert log_has(
'Starting freqtrade in Hyperopt mode',
caplog.record_tuples
)
assert start_mock.call_count == 1
def test_loss_calculation_prefer_correct_trade_count(init_hyperopt) -> None:
hyperopt = _HYPEROPT
StrategyResolver({'strategy': 'DefaultStrategy'})
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correct = hyperopt.calculate_loss(1, hyperopt.target_trades, 20)
over = hyperopt.calculate_loss(1, hyperopt.target_trades + 100, 20)
under = hyperopt.calculate_loss(1, hyperopt.target_trades - 100, 20)
assert over > correct
assert under > correct
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def test_loss_calculation_prefer_shorter_trades(init_hyperopt) -> None:
hyperopt = _HYPEROPT
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shorter = hyperopt.calculate_loss(1, 100, 20)
longer = hyperopt.calculate_loss(1, 100, 30)
assert shorter < longer
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def test_loss_calculation_has_limited_profit(init_hyperopt) -> None:
hyperopt = _HYPEROPT
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correct = hyperopt.calculate_loss(hyperopt.expected_max_profit, hyperopt.target_trades, 20)
over = hyperopt.calculate_loss(hyperopt.expected_max_profit * 2, hyperopt.target_trades, 20)
under = hyperopt.calculate_loss(hyperopt.expected_max_profit / 2, hyperopt.target_trades, 20)
assert over == correct
assert under > correct
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def test_log_results_if_loss_improves(init_hyperopt, capsys) -> None:
hyperopt = _HYPEROPT
hyperopt.current_best_loss = 2
hyperopt.log_results(
{
'loss': 1,
'current_tries': 1,
'total_tries': 2,
'result': 'foo'
}
)
out, err = capsys.readouterr()
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assert ' 1/2: foo. Loss 1.00000' in out
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def test_no_log_if_loss_does_not_improve(init_hyperopt, caplog) -> None:
hyperopt = _HYPEROPT
hyperopt.current_best_loss = 2
hyperopt.log_results(
{
'loss': 3,
}
)
assert caplog.record_tuples == []
def test_save_trials_saves_trials(mocker, init_hyperopt, caplog) -> None:
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trials = create_trials(mocker)
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mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
hyperopt = _HYPEROPT
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_HYPEROPT.trials = trials
hyperopt.save_trials()
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trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
assert log_has(
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'Saving 1 evaluations to \'{}\''.format(trials_file),
caplog.record_tuples
)
mock_dump.assert_called_once()
def test_read_trials_returns_trials_file(mocker, init_hyperopt, caplog) -> None:
trials = create_trials(mocker)
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mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials)
hyperopt = _HYPEROPT
hyperopt_trial = hyperopt.read_trials()
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trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
assert log_has(
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'Reading Trials from \'{}\''.format(trials_file),
caplog.record_tuples
)
assert hyperopt_trial == trials
mock_load.assert_called_once()
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def test_roi_table_generation(init_hyperopt) -> None:
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params = {
'roi_t1': 5,
'roi_t2': 10,
'roi_t3': 15,
'roi_p1': 1,
'roi_p2': 2,
'roi_p3': 3,
}
hyperopt = _HYPEROPT
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
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def test_start_calls_optimizer(mocker, init_hyperopt, default_conf, caplog) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
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mocker.patch('freqtrade.optimize.hyperopt.multiprocessing.cpu_count', MagicMock(return_value=1))
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}])
)
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patch_exchange(mocker)
conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'epochs': 1})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
hyperopt = Hyperopt(conf)
hyperopt.tickerdata_to_dataframe = MagicMock()
hyperopt.start()
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parallel.assert_called_once()
assert 'Best result:\nfoo result\nwith values:\n{}' in caplog.text
assert dumper.called
def test_format_results(init_hyperopt):
"""
Test Hyperopt.format_results()
"""
# 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)
result = _HYPEROPT.format_results(df)
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)
result = _HYPEROPT.format_results(df)
assert result.find('Total profit 1.00000000 EUR')
def test_has_space(init_hyperopt):
_HYPEROPT.config.update({'spaces': ['buy', 'roi']})
assert _HYPEROPT.has_space('roi')
assert _HYPEROPT.has_space('buy')
assert not _HYPEROPT.has_space('stoploss')
_HYPEROPT.config.update({'spaces': ['all']})
assert _HYPEROPT.has_space('buy')
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def test_populate_indicators(init_hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
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dataframes = _HYPEROPT.tickerdata_to_dataframe(tickerlist)
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dataframe = _HYPEROPT.populate_indicators(dataframes['UNITTEST/BTC'], 'UNITTEST/BTC')
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# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
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assert 'mfi' in dataframe
assert 'rsi' in dataframe
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def test_buy_strategy_generator(init_hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
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dataframes = _HYPEROPT.tickerdata_to_dataframe(tickerlist)
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dataframe = _HYPEROPT.populate_indicators(dataframes['UNITTEST/BTC'], 'UNITTEST/BTC')
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populate_buy_trend = _HYPEROPT.buy_strategy_generator(
{
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'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'
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}
)
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result = populate_buy_trend(dataframe, 'UNITTEST/BTC')
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# Check if some indicators are generated. We will not test all of them
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assert 'buy' in result
assert 1 in result['buy']
def test_generate_optimizer(mocker, init_hyperopt, default_conf) -> None:
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conf = deepcopy(default_conf)
conf.update({'config': 'config.json.example'})
conf.update({'timerange': None})
conf.update({'spaces': 'all'})
trades = [
('POWR/BTC', 0.023117, 0.000233, 100)
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]
labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
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backtest_result = pd.DataFrame.from_records(trades, columns=labels)
mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.backtest',
MagicMock(return_value=backtest_result)
)
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patch_exchange(mocker)
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
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optimizer_param = {
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'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',
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'roi_t1': 60.0,
'roi_t2': 30.0,
'roi_t3': 20.0,
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'roi_p1': 0.01,
'roi_p2': 0.01,
'roi_p3': 0.1,
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'stoploss': -0.4,
}
response_expected = {
'loss': 1.9840569076926293,
'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'(0.0231Σ%). Avg duration 100.0 mins.',
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'params': optimizer_param
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}
hyperopt = Hyperopt(conf)
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generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
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assert generate_optimizer_value == response_expected