Fix tests
This commit is contained in:
Matthias
parent
6aaaad29d7
commit
852f125347
@ -5,7 +5,7 @@ import re
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from datetime import datetime
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from datetime import datetime
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from pathlib import Path
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from pathlib import Path
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from typing import Dict, List
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from typing import Dict, List
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from unittest.mock import MagicMock
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from unittest.mock import ANY, MagicMock
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import pandas as pd
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import pandas as pd
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import pytest
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import pytest
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@ -18,10 +18,12 @@ from freqtrade.exceptions import OperationalException
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from freqtrade.optimize.hyperopt import Hyperopt
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from freqtrade.optimize.hyperopt import Hyperopt
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from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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from freqtrade.optimize.optimize_reports import generate_strategy_stats
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from freqtrade.optimize.space import SKDecimal
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from freqtrade.optimize.space import SKDecimal
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from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
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from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
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from freqtrade.state import RunMode
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from freqtrade.state import RunMode
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from freqtrade.strategy.hyper import IntParameter
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from freqtrade.strategy.hyper import IntParameter
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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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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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patched_configuration_load_config_file)
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@ -433,18 +435,41 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
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assert hasattr(hyperopt, "position_stacking")
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assert hasattr(hyperopt, "position_stacking")
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def test_format_results(hyperopt):
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def test_hyperopt_format_results(hyperopt):
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# Test with BTC as stake_currency
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trades = [
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bt_result = {
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('ETH/BTC', 2, 2, 123),
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'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
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('LTC/BTC', 1, 1, 123),
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"UNITTEST/BTC", "UNITTEST/BTC"],
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('XPR/BTC', -1, -2, -246)
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"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
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]
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"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
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labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
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"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
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df = pd.DataFrame.from_records(trades, columns=labels)
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Arrow(2017, 11, 14, 21, 36, 00).datetime,
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results_metrics = hyperopt._calculate_results_metrics(df)
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Arrow(2017, 11, 14, 22, 12, 00).datetime,
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Arrow(2017, 11, 14, 22, 44, 00).datetime],
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"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
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Arrow(2017, 11, 14, 22, 10, 00).datetime,
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Arrow(2017, 11, 14, 22, 43, 00).datetime,
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Arrow(2017, 11, 14, 22, 58, 00).datetime],
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"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
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"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
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"trade_duration": [123, 34, 31, 14],
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"is_open": [False, False, False, True],
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"stake_amount": [0.01, 0.01, 0.01, 0.01],
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"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
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SellType.ROI, SellType.FORCE_SELL]
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}),
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'config': hyperopt.config,
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'locks': [],
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'final_balance': 0.02,
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'backtest_start_time': 1619718665,
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'backtest_end_time': 1619718665,
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}
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results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
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Arrow(2017, 11, 14, 19, 32, 00),
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Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
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results_explanation = hyperopt._format_results_explanation_string(results_metrics)
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results_explanation = hyperopt._format_results_explanation_string(results_metrics)
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total_profit = results_metrics['total_profit']
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total_profit = results_metrics['profit_total_abs']
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results = {
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results = {
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'loss': 0.0,
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'loss': 0.0,
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@ -458,21 +483,9 @@ def test_format_results(hyperopt):
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}
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}
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result = HyperoptTools._format_explanation_string(results, 1)
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result = HyperoptTools._format_explanation_string(results, 1)
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assert result.find(' 66.67%')
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assert ' 0.71%' in result
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assert result.find('Total profit 1.00000000 BTC')
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assert 'Total profit 0.00003100 BTC' in result
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assert result.find('2.0000Σ %')
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assert '0:50:00 min' in result
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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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results_metrics = hyperopt._calculate_results_metrics(df)
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results['total_profit'] = results_metrics['total_profit']
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result = HyperoptTools._format_explanation_string(results, 1)
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assert result.find('Total profit 1.00000000 EUR')
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@pytest.mark.parametrize("spaces, expected_results", [
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@pytest.mark.parametrize("spaces, expected_results", [
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@ -577,22 +590,32 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
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'hyperopt_min_trades': 1,
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'hyperopt_min_trades': 1,
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})
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})
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trades = [
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backtest_result = pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
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('TRX/BTC', 0.023117, 0.000233, 100)
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"UNITTEST/BTC", "UNITTEST/BTC"],
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]
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"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
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labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
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"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
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backtest_result = pd.DataFrame.from_records(trades, columns=labels)
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"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
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Arrow(2017, 11, 14, 21, 36, 00).datetime,
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Arrow(2017, 11, 14, 22, 12, 00).datetime,
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Arrow(2017, 11, 14, 22, 44, 00).datetime],
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"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
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Arrow(2017, 11, 14, 22, 10, 00).datetime,
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Arrow(2017, 11, 14, 22, 43, 00).datetime,
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Arrow(2017, 11, 14, 22, 58, 00).datetime],
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"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
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"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
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"trade_duration": [123, 34, 31, 14],
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"is_open": [False, False, False, True],
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"stake_amount": [0.01, 0.01, 0.01, 0.01],
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"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
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SellType.ROI, SellType.FORCE_SELL]
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})
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mocker.patch(
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mocker.patch('freqtrade.optimize.hyperopt.Backtesting.backtest', return_value=backtest_result)
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'freqtrade.optimize.hyperopt.Backtesting.backtest',
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mocker.patch('freqtrade.optimize.hyperopt.get_timerange',
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MagicMock(return_value=backtest_result)
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return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
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)
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mocker.patch(
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'freqtrade.optimize.hyperopt.get_timerange',
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MagicMock(return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
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)
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patch_exchange(mocker)
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patch_exchange(mocker)
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mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
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mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
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optimizer_param = {
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optimizer_param = {
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'adx-value': 0,
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'adx-value': 0,
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@ -626,11 +649,11 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
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'trailing_only_offset_is_reached': False,
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'trailing_only_offset_is_reached': False,
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}
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}
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response_expected = {
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response_expected = {
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'loss': 1.9840569076926293,
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'loss': 1.9147239021396234,
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'results_explanation': (' 1 trades. 1/0/0 Wins/Draws/Losses. '
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'results_explanation': (' 4 trades. 4/0/0 Wins/Draws/Losses. '
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'Avg profit 2.31%. Median profit 2.31%. Total profit '
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'Avg profit 0.77%. Median profit 0.71%. Total profit '
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'0.00023300 BTC ( 2.31\N{GREEK CAPITAL LETTER SIGMA}%). '
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'0.00003100 BTC ( 0.00\N{GREEK CAPITAL LETTER SIGMA}%). '
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'Avg duration 100.0 min.'
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'Avg duration 0:50:00 min.'
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).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
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).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
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'params_details': {'buy': {'adx-enabled': False,
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'params_details': {'buy': {'adx-enabled': False,
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'adx-value': 0,
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'adx-value': 0,
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@ -660,17 +683,8 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
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'trailing_stop_positive': 0.02,
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'trailing_stop_positive': 0.02,
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'trailing_stop_positive_offset': 0.07}},
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'trailing_stop_positive_offset': 0.07}},
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'params_dict': optimizer_param,
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'params_dict': optimizer_param,
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'results_metrics': {'avg_profit': 2.3117,
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'results_metrics': ANY,
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'draws': 0,
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'total_profit': 3.1e-08
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'duration': 100.0,
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'losses': 0,
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'winsdrawslosses': ' 1 0 0',
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'median_profit': 2.3117,
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'profit': 2.3117,
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'total_profit': 0.000233,
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'trade_count': 1,
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'wins': 1},
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'total_profit': 0.00023300
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}
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}
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hyperopt = Hyperopt(hyperopt_conf)
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hyperopt = Hyperopt(hyperopt_conf)
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@ -14,7 +14,8 @@ from freqtrade.edge import PairInfo
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
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generate_edge_table, generate_pair_metrics,
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generate_edge_table, generate_pair_metrics,
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generate_sell_reason_stats,
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generate_sell_reason_stats,
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generate_strategy_comparison, generate_trading_stats, store_backtest_stats,
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generate_strategy_comparison,
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generate_trading_stats, store_backtest_stats,
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text_table_bt_results, text_table_sell_reason,
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text_table_bt_results, text_table_sell_reason,
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text_table_strategy)
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text_table_strategy)
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from freqtrade.resolvers.strategy_resolver import StrategyResolver
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from freqtrade.resolvers.strategy_resolver import StrategyResolver
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