479 lines
18 KiB
Python
479 lines
18 KiB
Python
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(str("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
|