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freqtrade_origin/tests/data/test_btanalysis.py
Matthias 261822147c Fix remaining arrow testcases
2023-05-18 07:00:36 +02:00

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from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock
import pytest
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 freqtrade.util import dt_utc
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([datetime(2017, 11, 13, 15, 40, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc),
datetime(2017, 11, 15, 3, 40, 0, tzinfo=timezone.utc),
], utc=True
),
'close_date': to_datetime([datetime(2017, 11, 13, 16, 40, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc),
datetime(2017, 11, 15, 3, 55, 0, tzinfo=timezone.utc),
], 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 == datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc)
assert trades1.iloc[0].close_date == datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc)
assert trades1.iloc[-1].open_date == datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc)
assert trades1.iloc[-1].close_date == datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc)
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 = [dt_utc(2020, 1, 1) + timedelta(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 = 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
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