332 lines
11 KiB
Python
332 lines
11 KiB
Python
# pragma pylint: disable=missing-docstring,W0212,C0103
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from datetime import datetime
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import os
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from unittest.mock import MagicMock
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import pandas as pd
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import pytest
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from freqtrade.optimize import load_tickerdata_file
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from freqtrade.optimize.hyperopt import Hyperopt, start
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from freqtrade.resolvers 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
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@pytest.fixture(scope='function')
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def hyperopt(default_conf, mocker):
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patch_exchange(mocker)
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return Hyperopt(default_conf)
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# Functions for recurrent object patching
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def create_trials(mocker, hyperopt) -> None:
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"""
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When creating trials, mock the hyperopt Trials 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.trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
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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)
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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:
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start_mock = MagicMock()
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mocker.patch(
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'freqtrade.configuration.Configuration._load_config_file',
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lambda *args, **kwargs: default_conf
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)
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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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'--config', 'config.json',
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'--strategy', 'DefaultStrategy',
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'hyperopt',
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'--epochs', '5'
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]
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args = get_args(args)
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StrategyResolver({'strategy': 'DefaultStrategy'})
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start(args)
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import pprint
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pprint.pprint(caplog.record_tuples)
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assert log_has(
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'Starting freqtrade in Hyperopt mode',
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caplog.record_tuples
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)
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assert start_mock.call_count == 1
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def test_start_failure(mocker, default_conf, caplog) -> None:
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start_mock = MagicMock()
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mocker.patch(
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'freqtrade.configuration.Configuration._load_config_file',
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lambda *args, **kwargs: default_conf
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)
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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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'--config', 'config.json',
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'--strategy', 'TestStrategy',
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'hyperopt',
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'--epochs', '5'
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]
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args = get_args(args)
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StrategyResolver({'strategy': 'DefaultStrategy'})
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with pytest.raises(ValueError):
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start(args)
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assert log_has(
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"Please don't use --strategy for hyperopt.",
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caplog.record_tuples
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)
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def test_loss_calculation_prefer_correct_trade_count(hyperopt) -> None:
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StrategyResolver({'strategy': 'DefaultStrategy'})
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correct = hyperopt.calculate_loss(1, hyperopt.target_trades, 20)
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over = hyperopt.calculate_loss(1, hyperopt.target_trades + 100, 20)
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under = hyperopt.calculate_loss(1, hyperopt.target_trades - 100, 20)
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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(hyperopt) -> None:
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shorter = hyperopt.calculate_loss(1, 100, 20)
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longer = hyperopt.calculate_loss(1, 100, 30)
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assert shorter < longer
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def test_loss_calculation_has_limited_profit(hyperopt) -> None:
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correct = hyperopt.calculate_loss(hyperopt.expected_max_profit, hyperopt.target_trades, 20)
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over = hyperopt.calculate_loss(hyperopt.expected_max_profit * 2, hyperopt.target_trades, 20)
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under = hyperopt.calculate_loss(hyperopt.expected_max_profit / 2, hyperopt.target_trades, 20)
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assert over == correct
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assert under > correct
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def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
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hyperopt.current_best_loss = 2
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hyperopt.log_results(
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{
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'loss': 1,
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'current_tries': 1,
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'total_tries': 2,
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'result': 'foo'
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}
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)
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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(hyperopt, caplog) -> None:
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hyperopt.current_best_loss = 2
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hyperopt.log_results(
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{
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'loss': 3,
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}
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)
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assert caplog.record_tuples == []
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def test_save_trials_saves_trials(mocker, hyperopt, caplog) -> None:
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trials = create_trials(mocker, hyperopt)
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mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
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hyperopt.trials = trials
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hyperopt.save_trials()
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trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
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assert log_has(
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'Saving 1 evaluations to \'{}\''.format(trials_file),
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caplog.record_tuples
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)
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mock_dump.assert_called_once()
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def test_read_trials_returns_trials_file(mocker, hyperopt, caplog) -> None:
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trials = create_trials(mocker, hyperopt)
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mock_load = mocker.patch('freqtrade.optimize.hyperopt.load', return_value=trials)
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hyperopt_trial = hyperopt.read_trials()
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trials_file = os.path.join('freqtrade', 'tests', 'optimize', 'ut_trials.pickle')
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assert log_has(
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'Reading Trials from \'{}\''.format(trials_file),
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caplog.record_tuples
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)
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assert hyperopt_trial == trials
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mock_load.assert_called_once()
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def test_roi_table_generation(hyperopt) -> None:
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params = {
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'roi_t1': 5,
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'roi_t2': 10,
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'roi_t3': 15,
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'roi_p1': 1,
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'roi_p2': 2,
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'roi_p3': 3,
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}
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assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
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def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
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dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
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mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
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mocker.patch('freqtrade.optimize.hyperopt.multiprocessing.cpu_count', MagicMock(return_value=1))
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parallel = mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
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MagicMock(return_value=[{'loss': 1, 'result': 'foo result', 'params': {}}])
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)
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patch_exchange(mocker)
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default_conf.update({'config': 'config.json.example'})
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default_conf.update({'epochs': 1})
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default_conf.update({'timerange': None})
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default_conf.update({'spaces': 'all'})
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hyperopt = Hyperopt(default_conf)
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hyperopt.strategy.tickerdata_to_dataframe = MagicMock()
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hyperopt.start()
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parallel.assert_called_once()
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assert 'Best result:\nfoo result\nwith values:\n{}' in caplog.text
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assert dumper.called
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def test_format_results(hyperopt):
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# Test with BTC as stake_currency
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trades = [
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('ETH/BTC', 2, 2, 123),
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('LTC/BTC', 1, 1, 123),
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('XPR/BTC', -1, -2, -246)
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]
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labels = ['currency', 'profit_percent', 'profit_abs', 'trade_duration']
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df = pd.DataFrame.from_records(trades, columns=labels)
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result = hyperopt.format_results(df)
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assert result.find(' 66.67%')
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assert result.find('Total profit 1.00000000 BTC')
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assert result.find('2.0000Σ %')
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# Test with EUR as stake_currency
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trades = [
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('ETH/EUR', 2, 2, 123),
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('LTC/EUR', 1, 1, 123),
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('XPR/EUR', -1, -2, -246)
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]
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df = pd.DataFrame.from_records(trades, columns=labels)
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result = hyperopt.format_results(df)
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assert result.find('Total profit 1.00000000 EUR')
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def test_has_space(hyperopt):
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hyperopt.config.update({'spaces': ['buy', 'roi']})
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assert hyperopt.has_space('roi')
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assert hyperopt.has_space('buy')
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assert not hyperopt.has_space('stoploss')
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hyperopt.config.update({'spaces': ['all']})
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assert hyperopt.has_space('buy')
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def test_populate_indicators(hyperopt) -> None:
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tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
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tickerlist = {'UNITTEST/BTC': tick}
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dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
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dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
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{'pair': 'UNITTEST/BTC'})
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# Check if some indicators are generated. We will not test all of them
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assert 'adx' in dataframe
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assert 'mfi' in dataframe
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assert 'rsi' in dataframe
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def test_buy_strategy_generator(hyperopt) -> None:
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tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
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tickerlist = {'UNITTEST/BTC': tick}
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dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
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dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
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{'pair': 'UNITTEST/BTC'})
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populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
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{
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'adx-value': 20,
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'fastd-value': 20,
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'mfi-value': 20,
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'rsi-value': 20,
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'adx-enabled': True,
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'fastd-enabled': True,
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'mfi-enabled': True,
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'rsi-enabled': True,
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'trigger': 'bb_lower'
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}
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)
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result = populate_buy_trend(dataframe, {'pair': '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
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assert 1 in result['buy']
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def test_generate_optimizer(mocker, default_conf) -> None:
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default_conf.update({'config': 'config.json.example'})
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default_conf.update({'timerange': None})
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default_conf.update({'spaces': 'all'})
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trades = [
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('POWR/BTC', 0.023117, 0.000233, 100)
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]
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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)
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mocker.patch(
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'freqtrade.optimize.hyperopt.Hyperopt.backtest',
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MagicMock(return_value=backtest_result)
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)
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mocker.patch(
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'freqtrade.optimize.hyperopt.get_timeframe',
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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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mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
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optimizer_param = {
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'adx-value': 0,
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'fastd-value': 35,
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'mfi-value': 0,
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'rsi-value': 0,
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'adx-enabled': False,
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'fastd-enabled': True,
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'mfi-enabled': False,
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'rsi-enabled': False,
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'trigger': 'macd_cross_signal',
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'roi_t1': 60.0,
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'roi_t2': 30.0,
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'roi_t3': 20.0,
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'roi_p1': 0.01,
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'roi_p2': 0.01,
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'roi_p3': 0.1,
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'stoploss': -0.4,
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}
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response_expected = {
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'loss': 1.9840569076926293,
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'result': ' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
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'(0.0231Σ%). Avg duration 100.0 mins.',
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'params': optimizer_param
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}
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hyperopt = Hyperopt(default_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
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