96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
|
# from unittest.mock import MagicMock
|
||
|
|
# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
|
||
|
|
import copy
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from freqtrade.configuration import TimeRange
|
||
|
|
from freqtrade.data.dataprovider import DataProvider
|
||
|
|
# from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
||
|
|
from freqtrade.exceptions import OperationalException
|
||
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||
|
|
from tests.conftest import get_patched_exchange
|
||
|
|
from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_patched_strategy
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"timerange, train_period_days, expected_result",
|
||
|
|
[
|
||
|
|
("20220101-20220201", 30, "20211202-20220201"),
|
||
|
|
("20220301-20220401", 15, "20220214-20220401"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_create_fulltimerange(
|
||
|
|
timerange, train_period_days, expected_result, default_conf, mocker, caplog
|
||
|
|
):
|
||
|
|
dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
|
||
|
|
assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
|
||
|
|
|
||
|
|
|
||
|
|
def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf):
|
||
|
|
dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
|
||
|
|
with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
|
||
|
|
dk.create_fulltimerange("20220101-20220201", 0.5)
|
||
|
|
with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
|
||
|
|
dk.create_fulltimerange("20220101-20220201", -1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_split_timerange(mocker, default_conf):
|
||
|
|
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||
|
|
freqaiconf.update({"timerange": "20220101-20220401"})
|
||
|
|
dk = get_patched_data_kitchen(mocker, freqaiconf)
|
||
|
|
tr_list, bt_list = dk.split_timerange("20220101-20220201", 30, 7)
|
||
|
|
assert len(tr_list) == len(bt_list) == 9
|
||
|
|
|
||
|
|
tr_list, bt_list = dk.split_timerange("20220101-20220201", 30, 0.5)
|
||
|
|
assert len(tr_list) == len(bt_list) == 120
|
||
|
|
|
||
|
|
tr_list, bt_list = dk.split_timerange("20220101-20220201", 10, 1)
|
||
|
|
assert len(tr_list) == len(bt_list) == 80
|
||
|
|
|
||
|
|
with pytest.raises(
|
||
|
|
OperationalException, match=r"train_period_days must be an integer greater than 0."
|
||
|
|
):
|
||
|
|
dk.split_timerange("20220101-20220201", -1, 0.5)
|
||
|
|
|
||
|
|
|
||
|
|
def test_update_historic_data(mocker, default_conf):
|
||
|
|
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||
|
|
strategy = get_patched_strategy(mocker, freqaiconf)
|
||
|
|
exchange = get_patched_exchange(mocker, freqaiconf)
|
||
|
|
strategy.dp = DataProvider(freqaiconf, exchange)
|
||
|
|
freqai = strategy.model.bridge
|
||
|
|
freqai.live = True
|
||
|
|
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||
|
|
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||
|
|
|
||
|
|
freqai.dk.load_all_pair_histories(timerange)
|
||
|
|
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||
|
|
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
|
||
|
|
candle_difference = dp_candles - historic_candles
|
||
|
|
freqai.dk.update_historic_data(strategy)
|
||
|
|
|
||
|
|
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||
|
|
|
||
|
|
assert updated_historic_candles - historic_candles == candle_difference
|
||
|
|
|
||
|
|
|
||
|
|
# def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
|
||
|
|
# np.random.seed(42)
|
||
|
|
# tf_mins = timeframe_to_minutes(timeframe)
|
||
|
|
|
||
|
|
# base = np.random.normal(20, 2, size=size)
|
||
|
|
|
||
|
|
# date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC')
|
||
|
|
# df = pd.DataFrame({
|
||
|
|
# 'date': date,
|
||
|
|
# 'open': base,
|
||
|
|
# 'high': base + np.random.normal(2, 1, size=size),
|
||
|
|
# 'low': base - np.random.normal(2, 1, size=size),
|
||
|
|
# 'close': base + np.random.normal(0, 1, size=size),
|
||
|
|
# 'volume': np.random.normal(200, size=size)
|
||
|
|
# }
|
||
|
|
# )
|
||
|
|
# df = df.dropna()
|
||
|
|
# return df
|