stable/tests/optimize/test_backtesting_adjust_position.py
2022-04-02 20:04:29 +02:00

85 lines
3.6 KiB
Python

# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
from copy import deepcopy
import pandas as pd
from arrow import Arrow
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.history import get_timerange
from freqtrade.enums import ExitType
from freqtrade.optimize.backtesting import Backtesting
from tests.conftest import patch_exchange
def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
default_conf['use_sell_signal'] = False
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
patch_exchange(mocker)
default_conf.update({
"stake_amount": 100.0,
"dry_run_wallet": 1000.0,
"strategy": "StrategyTestV2"
})
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
pair = 'UNITTEST/BTC'
timerange = TimeRange('date', None, 1517227800, 0)
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=timerange)
backtesting.strategy.position_adjustment_enable = True
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
result = backtesting.backtest(
processed=deepcopy(processed),
start_date=min_date,
end_date=max_date,
max_open_trades=10,
position_stacking=False,
)
results = result['results']
assert not results.empty
assert len(results) == 2
expected = pd.DataFrame(
{'pair': [pair, pair],
'stake_amount': [500.0, 100.0],
'amount': [4806.87657523, 970.63960782],
'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
),
'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 00, 0).datetime,
Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
'open_rate': [0.10401764894444211, 0.10302485],
'close_rate': [0.10453904066847439, 0.103541],
'fee_open': [0.0025, 0.0025],
'fee_close': [0.0025, 0.0025],
'trade_duration': [200, 40],
'profit_ratio': [0.0, 0.0],
'profit_abs': [0.0, 0.0],
'exit_reason': [ExitType.ROI.value, ExitType.ROI.value],
'initial_stop_loss_abs': [0.0940005, 0.09272236],
'initial_stop_loss_ratio': [-0.1, -0.1],
'stop_loss_abs': [0.0940005, 0.09272236],
'stop_loss_ratio': [-0.1, -0.1],
'min_rate': [0.10370188, 0.10300000000000001],
'max_rate': [0.10481985, 0.1038888],
'is_open': [False, False],
'enter_tag': [None, None],
'is_short': [False, False],
})
pd.testing.assert_frame_equal(results, expected)
data_pair = processed[pair]
for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate
assert ln is not None
# check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))