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