stable/tests/freqai/test_freqai.py

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2022-07-19 14:16:44 +00:00
# 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