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