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start adding tests
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robcaulk
parent
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714d9534b6
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@ -410,6 +410,11 @@ class FreqaiDataKitchen:
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bt_split: the backtesting length (dats). Specified in user configuration file
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"""
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if not isinstance(train_split, int) or train_split < 1:
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raise OperationalException(
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"train_period_days must be an integer greater than 0. "
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f"Got {train_split}."
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)
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train_period_days = train_split * SECONDS_IN_DAY
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bt_period = bt_split * SECONDS_IN_DAY
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@ -742,6 +747,13 @@ class FreqaiDataKitchen:
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return
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def create_fulltimerange(self, backtest_tr: str, backtest_period_days: int) -> str:
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if not isinstance(backtest_period_days, int):
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raise OperationalException('backtest_period_days must be an integer')
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if backtest_period_days < 0:
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raise OperationalException('backtest_period_days must be positive')
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backtest_timerange = TimeRange.parse_timerange(backtest_tr)
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if backtest_timerange.stopts == 0:
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@ -869,30 +881,6 @@ class FreqaiDataKitchen:
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self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts))
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# self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# enables persistence, but not fully implemented into save/load data yer
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# self.data['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# SUPERCEDED
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# def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict,
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# strategy: IStrategy) -> None:
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# exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'],
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# self.config, validate=False, freqai=True)
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# # exchange = strategy.dp._exchange # closes ccxt session
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# pairs = copy.deepcopy(self.freqai_config.get('corr_pairlist', []))
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# if str(metadata['pair']) not in pairs:
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# pairs.append(str(metadata['pair']))
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# refresh_backtest_ohlcv_data(
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# exchange, pairs=pairs, timeframes=self.freqai_config.get('timeframes'),
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# datadir=self.config['datadir'], timerange=timerange,
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# new_pairs_days=self.config['new_pairs_days'],
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# erase=False, data_format=self.config.get('dataformat_ohlcv', 'json'),
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# trading_mode=self.config.get('trading_mode', 'spot'),
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# prepend=self.config.get('prepend_data', False)
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# )
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def download_all_data_for_training(self, timerange: TimeRange) -> None:
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"""
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Called only once upon start of bot to download the necessary data for
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68
tests/freqai/conftest.py
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68
tests/freqai/conftest.py
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@ -0,0 +1,68 @@
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from copy import deepcopy
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from pathlib import Path
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from unittest.mock import MagicMock
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.resolvers import StrategyResolver
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from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
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# @pytest.fixture(scope="function")
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def freqai_conf(default_conf):
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freqaiconf = deepcopy(default_conf)
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freqaiconf.update(
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{
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"datadir": Path(default_conf["datadir"]),
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"strategy": "FreqaiExampleStrategy",
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"strategy-path": "freqtrade/templates",
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"freqaimodel": "LightGBMPredictionModel",
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"freqaimodel_path": "freqai/prediction_models",
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"timerange": "20180110-20180115",
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"freqai": {
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"startup_candles": 10000,
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"purge_old_models": True,
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"train_period_days": 15,
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"backtest_period_days": 7,
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"live_retrain_hours": 0,
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"identifier": "uniqe-id7",
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"live_trained_timestamp": 0,
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"feature_parameters": {
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"include_timeframes": ["5m"],
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"include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
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"label_period_candles": 20,
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"include_shifted_candles": 2,
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": False,
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"use_SVM_to_remove_outliers": True,
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"stratify_training_data": 0,
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"indicator_max_period_candles": 10,
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"indicator_periods_candles": [10],
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},
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"data_split_parameters": {"test_size": 0.33, "random_state": 1},
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"model_training_parameters": {"n_estimators": 1000, "task_type": "CPU"},
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},
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"config_files": [Path('config_examples', 'config_freqai_futures.example.json')]
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}
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)
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freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
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return freqaiconf
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def get_patched_data_kitchen(mocker, freqaiconf):
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dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
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dk = FreqaiDataKitchen(freqaiconf, dd)
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return dk
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def get_patched_strategy(mocker, freqaiconf):
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strategy = StrategyResolver.load_strategy(freqaiconf)
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strategy.bot_start()
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return strategy
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def get_patched_freqaimodel(mocker, freqaiconf):
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freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
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return freqaimodel
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95
tests/freqai/test_freqai.py
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95
tests/freqai/test_freqai.py
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@ -0,0 +1,95 @@
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# 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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