mirror of
https://github.com/freqtrade/freqtrade.git
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480 lines
19 KiB
Python
480 lines
19 KiB
Python
from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from unittest.mock import MagicMock
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import pytest
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from arrow import Arrow
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from pandas import DataFrame, DateOffset, Timestamp, to_datetime
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism,
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extract_trades_of_period, get_latest_backtest_filename,
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get_latest_hyperopt_file, load_backtest_data,
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load_backtest_metadata, load_trades, load_trades_from_db)
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from freqtrade.data.history import load_data, load_pair_history
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from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
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calculate_expectancy, calculate_market_change,
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calculate_max_drawdown, calculate_sharpe, calculate_sortino,
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calculate_underwater, combine_dataframes_with_mean,
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create_cum_profit)
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from freqtrade.exceptions import OperationalException
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from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
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from tests.conftest_trades import MOCK_TRADE_COUNT
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def test_get_latest_backtest_filename(testdatadir, mocker):
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with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
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get_latest_backtest_filename(testdatadir / 'does_not_exist')
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with pytest.raises(ValueError,
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match=r"Directory .* does not seem to contain .*"):
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get_latest_backtest_filename(testdatadir)
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testdir_bt = testdatadir / "backtest_results"
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res = get_latest_backtest_filename(testdir_bt)
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assert res == 'backtest-result.json'
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res = get_latest_backtest_filename(str(testdir_bt))
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assert res == 'backtest-result.json'
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mocker.patch("freqtrade.data.btanalysis.json_load", return_value={})
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with pytest.raises(ValueError, match=r"Invalid '.last_result.json' format."):
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get_latest_backtest_filename(testdir_bt)
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def test_get_latest_hyperopt_file(testdatadir):
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res = get_latest_hyperopt_file(testdatadir / 'does_not_exist', 'testfile.pickle')
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assert res == testdatadir / 'does_not_exist/testfile.pickle'
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res = get_latest_hyperopt_file(testdatadir.parent)
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assert res == testdatadir.parent / "hyperopt_results.pickle"
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res = get_latest_hyperopt_file(str(testdatadir.parent))
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assert res == testdatadir.parent / "hyperopt_results.pickle"
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# Test with absolute path
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with pytest.raises(
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OperationalException,
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match="--hyperopt-filename expects only the filename, not an absolute path."):
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get_latest_hyperopt_file(str(testdatadir.parent), str(testdatadir.parent))
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def test_load_backtest_metadata(mocker, testdatadir):
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res = load_backtest_metadata(testdatadir / 'nonexistant.file.json')
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assert res == {}
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mocker.patch('freqtrade.data.btanalysis.get_backtest_metadata_filename')
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mocker.patch('freqtrade.data.btanalysis.json_load', side_effect=Exception())
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with pytest.raises(OperationalException,
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match=r"Unexpected error.*loading backtest metadata\."):
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load_backtest_metadata(testdatadir / 'nonexistant.file.json')
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def test_load_backtest_data_old_format(testdatadir, mocker):
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filename = testdatadir / "backtest-result_test222.json"
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mocker.patch('freqtrade.data.btanalysis.load_backtest_stats', return_value=[])
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with pytest.raises(OperationalException,
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match=r"Backtest-results with only trades data are no longer supported."):
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load_backtest_data(filename)
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def test_load_backtest_data_new_format(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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assert isinstance(bt_data, DataFrame)
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assert set(bt_data.columns) == set(BT_DATA_COLUMNS)
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assert len(bt_data) == 179
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# Test loading from string (must yield same result)
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bt_data2 = load_backtest_data(str(filename))
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assert bt_data.equals(bt_data2)
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# Test loading from folder (must yield same result)
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bt_data3 = load_backtest_data(testdatadir / "backtest_results")
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assert bt_data.equals(bt_data3)
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with pytest.raises(ValueError, match=r"File .* does not exist\."):
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load_backtest_data("filename" + "nofile")
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with pytest.raises(ValueError, match=r"Unknown dataformat."):
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load_backtest_data(testdatadir / "backtest_results" / LAST_BT_RESULT_FN)
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def test_load_backtest_data_multi(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result_multistrat.json"
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for strategy in ('StrategyTestV2', 'TestStrategy'):
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bt_data = load_backtest_data(filename, strategy=strategy)
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assert isinstance(bt_data, DataFrame)
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assert set(bt_data.columns) == set(
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BT_DATA_COLUMNS)
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assert len(bt_data) == 179
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# Test loading from string (must yield same result)
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bt_data2 = load_backtest_data(str(filename), strategy=strategy)
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assert bt_data.equals(bt_data2)
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with pytest.raises(ValueError, match=r"Strategy XYZ not available in the backtest result\."):
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load_backtest_data(filename, strategy='XYZ')
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with pytest.raises(ValueError, match=r"Detected backtest result with more than one strategy.*"):
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load_backtest_data(filename)
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@pytest.mark.usefixtures("init_persistence")
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@pytest.mark.parametrize('is_short', [False, True])
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def test_load_trades_from_db(default_conf, fee, is_short, mocker):
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create_mock_trades(fee, is_short)
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# remove init so it does not init again
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init_mock = mocker.patch('freqtrade.data.btanalysis.init_db', MagicMock())
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trades = load_trades_from_db(db_url=default_conf['db_url'])
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assert init_mock.call_count == 1
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assert len(trades) == MOCK_TRADE_COUNT
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assert isinstance(trades, DataFrame)
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assert "pair" in trades.columns
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assert "open_date" in trades.columns
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assert "profit_ratio" in trades.columns
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for col in BT_DATA_COLUMNS:
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if col not in ['index', 'open_at_end']:
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assert col in trades.columns
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trades = load_trades_from_db(db_url=default_conf['db_url'], strategy=CURRENT_TEST_STRATEGY)
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assert len(trades) == 4
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trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='NoneStrategy')
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assert len(trades) == 0
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def test_extract_trades_of_period(testdatadir):
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pair = "UNITTEST/BTC"
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# 2018-11-14 06:07:00
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timerange = TimeRange('date', None, 1510639620, 0)
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data = load_pair_history(pair=pair, timeframe='1m',
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datadir=testdatadir, timerange=timerange)
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trades = DataFrame(
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{'pair': [pair, pair, pair, pair],
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'profit_ratio': [0.0, 0.1, -0.2, -0.5],
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'profit_abs': [0.0, 1, -2, -5],
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'open_date': to_datetime([datetime(2017, 11, 13, 15, 40, 0, tzinfo=timezone.utc),
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datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc),
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datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc),
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datetime(2017, 11, 15, 3, 40, 0, tzinfo=timezone.utc),
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], utc=True
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),
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'close_date': to_datetime([datetime(2017, 11, 13, 16, 40, 0, tzinfo=timezone.utc),
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datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc),
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datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc),
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datetime(2017, 11, 15, 3, 55, 0, tzinfo=timezone.utc),
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], utc=True)
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})
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trades1 = extract_trades_of_period(data, trades)
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# First and last trade are dropped as they are out of range
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assert len(trades1) == 2
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assert trades1.iloc[0].open_date == datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc)
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assert trades1.iloc[0].close_date == datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc)
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assert trades1.iloc[-1].open_date == datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc)
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assert trades1.iloc[-1].close_date == datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc)
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def test_analyze_trade_parallelism(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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res = analyze_trade_parallelism(bt_data, "5m")
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assert isinstance(res, DataFrame)
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assert 'open_trades' in res.columns
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assert res['open_trades'].max() == 3
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assert res['open_trades'].min() == 0
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def test_load_trades(default_conf, mocker):
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db_mock = mocker.patch("freqtrade.data.btanalysis.load_trades_from_db", MagicMock())
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bt_mock = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
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load_trades("DB",
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db_url=default_conf.get('db_url'),
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exportfilename=default_conf.get('exportfilename'),
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no_trades=False,
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strategy=CURRENT_TEST_STRATEGY,
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)
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assert db_mock.call_count == 1
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assert bt_mock.call_count == 0
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db_mock.reset_mock()
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bt_mock.reset_mock()
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default_conf['exportfilename'] = Path("testfile.json")
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load_trades("file",
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db_url=default_conf.get('db_url'),
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exportfilename=default_conf.get('exportfilename'),
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)
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assert db_mock.call_count == 0
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assert bt_mock.call_count == 1
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db_mock.reset_mock()
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bt_mock.reset_mock()
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default_conf['exportfilename'] = "testfile.json"
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load_trades("file",
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db_url=default_conf.get('db_url'),
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exportfilename=default_conf.get('exportfilename'),
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no_trades=True
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)
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assert db_mock.call_count == 0
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assert bt_mock.call_count == 0
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def test_calculate_market_change(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
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result = calculate_market_change(data)
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assert isinstance(result, float)
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assert pytest.approx(result) == 0.01100002
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def test_combine_dataframes_with_mean(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
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df = combine_dataframes_with_mean(data)
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assert isinstance(df, DataFrame)
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assert "ETH/BTC" in df.columns
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assert "ADA/BTC" in df.columns
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assert "mean" in df.columns
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def test_combine_dataframes_with_mean_no_data(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe='6m')
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with pytest.raises(ValueError, match=r"No objects to concatenate"):
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combine_dataframes_with_mean(data)
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def test_create_cum_profit(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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timerange = TimeRange.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="TRX/BTC", timeframe='5m',
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datadir=testdatadir, timerange=timerange)
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cum_profits = create_cum_profit(df.set_index('date'),
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bt_data[bt_data["pair"] == 'TRX/BTC'],
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"cum_profits", timeframe="5m")
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]['cum_profits'] == 0
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assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 9.0225563e-05
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def test_create_cum_profit1(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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# Move close-time to "off" the candle, to make sure the logic still works
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bt_data['close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
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timerange = TimeRange.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="TRX/BTC", timeframe='5m',
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datadir=testdatadir, timerange=timerange)
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cum_profits = create_cum_profit(df.set_index('date'),
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bt_data[bt_data["pair"] == 'TRX/BTC'],
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"cum_profits", timeframe="5m")
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]['cum_profits'] == 0
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assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 9.0225563e-05
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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create_cum_profit(df.set_index('date'), bt_data[bt_data["pair"] == 'NOTAPAIR'],
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"cum_profits", timeframe="5m")
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def test_calculate_max_drawdown(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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_, hdate, lowdate, hval, lval, drawdown = calculate_max_drawdown(
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bt_data, value_col="profit_abs")
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assert isinstance(drawdown, float)
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assert pytest.approx(drawdown) == 0.29753914
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assert isinstance(hdate, Timestamp)
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assert isinstance(lowdate, Timestamp)
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assert isinstance(hval, float)
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assert isinstance(lval, float)
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assert hdate == Timestamp('2018-01-16 19:30:00', tz='UTC')
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assert lowdate == Timestamp('2018-01-16 22:25:00', tz='UTC')
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underwater = calculate_underwater(bt_data)
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assert isinstance(underwater, DataFrame)
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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calculate_max_drawdown(DataFrame())
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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calculate_underwater(DataFrame())
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def test_calculate_csum(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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csum_min, csum_max = calculate_csum(bt_data)
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assert isinstance(csum_min, float)
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assert isinstance(csum_max, float)
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assert csum_min < csum_max
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assert csum_min < 0.0001
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assert csum_max > 0.0002
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csum_min1, csum_max1 = calculate_csum(bt_data, 5)
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assert csum_min1 == csum_min + 5
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assert csum_max1 == csum_max + 5
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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csum_min, csum_max = calculate_csum(DataFrame())
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def test_calculate_expectancy(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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expectancy = calculate_expectancy(DataFrame())
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assert expectancy == 0.0
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expectancy = calculate_expectancy(bt_data)
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assert isinstance(expectancy, float)
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assert pytest.approx(expectancy) == 0.07151374226574791
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def test_calculate_sortino(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sortino = calculate_sortino(DataFrame(), None, None, 0)
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assert sortino == 0.0
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sortino = calculate_sortino(
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bt_data,
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bt_data['open_date'].min(),
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bt_data['close_date'].max(),
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0.01,
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)
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assert isinstance(sortino, float)
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assert pytest.approx(sortino) == 35.17722
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def test_calculate_sharpe(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sharpe = calculate_sharpe(DataFrame(), None, None, 0)
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assert sharpe == 0.0
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sharpe = calculate_sharpe(
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bt_data,
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bt_data['open_date'].min(),
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bt_data['close_date'].max(),
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0.01,
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)
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assert isinstance(sharpe, float)
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assert pytest.approx(sharpe) == 44.5078669
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def test_calculate_calmar(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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calmar = calculate_calmar(DataFrame(), None, None, 0)
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assert calmar == 0.0
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calmar = calculate_calmar(
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bt_data,
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bt_data['open_date'].min(),
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bt_data['close_date'].max(),
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0.01,
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)
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assert isinstance(calmar, float)
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assert pytest.approx(calmar) == 559.040508
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@pytest.mark.parametrize('start,end,days, expected', [
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(64900, 176000, 3 * 365, 0.3945),
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(64900, 176000, 365, 1.7119),
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(1000, 1000, 365, 0.0),
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(1000, 1500, 365, 0.5),
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(1000, 1500, 100, 3.3927), # sub year
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(0.01000000, 0.01762792, 120, 4.6087), # sub year BTC values
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])
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def test_calculate_cagr(start, end, days, expected):
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assert round(calculate_cagr(days, start, end), 4) == expected
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def test_calculate_max_drawdown2():
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values = [0.011580, 0.010048, 0.011340, 0.012161, 0.010416, 0.010009, 0.020024,
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-0.024662, -0.022350, 0.020496, -0.029859, -0.030511, 0.010041, 0.010872,
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-0.025782, 0.010400, 0.012374, 0.012467, 0.114741, 0.010303, 0.010088,
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-0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711]
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dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
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df = DataFrame(zip(values, dates), columns=['profit', 'open_date'])
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# sort by profit and reset index
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df = df.sort_values('profit').reset_index(drop=True)
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df1 = df.copy()
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drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
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df, date_col='open_date', value_col='profit')
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# Ensure df has not been altered.
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assert df.equals(df1)
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|
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assert isinstance(drawdown, float)
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assert isinstance(drawdown_rel, float)
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# High must be before low
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assert hdate < ldate
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# High value must be higher than low value
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assert hval > lval
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assert drawdown == 0.091755
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|
|
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df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
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with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
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calculate_max_drawdown(df, date_col='open_date', value_col='profit')
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|
|
|
|
|
@pytest.mark.parametrize('profits,relative,highd,lowd,result,result_rel', [
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([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909),
|
|
([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5),
|
|
|
|
])
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|
def test_calculate_max_drawdown_abs(profits, relative, highd, lowd, result, result_rel):
|
|
"""
|
|
Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
|
|
[1000, 500, 1000, 11000, 10000] # absolute results
|
|
[1000, 50%, 0%, 0%, ~9%] # Relative drawdowns
|
|
"""
|
|
init_date = datetime(2020, 1, 1, tzinfo=timezone.utc)
|
|
dates = [init_date + timedelta(days=i) for i in range(len(profits))]
|
|
df = DataFrame(zip(profits, dates), columns=['profit_abs', 'open_date'])
|
|
# sort by profit and reset index
|
|
df = df.sort_values('profit_abs').reset_index(drop=True)
|
|
df1 = df.copy()
|
|
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
|
|
df, date_col='open_date', starting_balance=1000, relative=relative)
|
|
# Ensure df has not been altered.
|
|
assert df.equals(df1)
|
|
|
|
assert isinstance(drawdown, float)
|
|
assert isinstance(drawdown_rel, float)
|
|
assert hdate == init_date + timedelta(days=highd)
|
|
assert ldate == init_date + timedelta(days=lowd)
|
|
|
|
# High must be before low
|
|
assert hdate < ldate
|
|
# High value must be higher than low value
|
|
assert hval > lval
|
|
assert drawdown == result
|
|
assert pytest.approx(drawdown_rel) == result_rel
|