from copy import deepcopy from pathlib import Path import pytest from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.resolvers import StrategyResolver from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver from tests.conftest import get_patched_exchange @pytest.fixture(scope="function") def freqai_conf(default_conf, tmpdir): freqaiconf = deepcopy(default_conf) freqaiconf.update( { "datadir": Path(default_conf["datadir"]), "strategy": "freqai_test_strat", "user_data_dir": Path(tmpdir), "strategy-path": "freqtrade/tests/strategy/strats", "freqaimodel": "LightGBMPredictionModel", "freqaimodel_path": "freqai/prediction_models", "timerange": "20180110-20180115", "freqai": { "startup_candles": 10000, "purge_old_models": True, "train_period_days": 5, "backtest_period_days": 2, "live_retrain_hours": 0, "expiration_hours": 1, "identifier": "uniqe-id100", "live_trained_timestamp": 0, "feature_parameters": { "include_timeframes": ["5m"], "include_corr_pairlist": ["ADA/BTC", "DASH/BTC"], "label_period_candles": 20, "include_shifted_candles": 1, "DI_threshold": 0.9, "weight_factor": 0.9, "principal_component_analysis": False, "use_SVM_to_remove_outliers": True, "stratify_training_data": 0, "indicator_max_period_candles": 10, "indicator_periods_candles": [10], }, "data_split_parameters": {"test_size": 0.33, "random_state": 1}, "model_training_parameters": {"n_estimators": 100, "verbosity": 0}, }, "config_files": [Path('config_examples', 'config_freqai_futures.example.json')] } ) freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']}) return freqaiconf def get_patched_data_kitchen(mocker, freqaiconf): # dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock()) dk = FreqaiDataKitchen(freqaiconf) return dk def get_patched_data_drawer(mocker, freqaiconf): # dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock()) dd = FreqaiDataDrawer(freqaiconf) return dd def get_patched_freqai_strategy(mocker, freqaiconf): strategy = StrategyResolver.load_strategy(freqaiconf) strategy.ft_bot_start() return strategy def get_patched_freqaimodel(mocker, freqaiconf): freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf) return freqaimodel def get_freqai_live_analyzed_dataframe(mocker, freqaiconf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) strategy.analyze_pair('ADA/BTC', '5m') return strategy.dp.get_analyzed_dataframe('ADA/BTC', '5m') def get_freqai_analyzed_dataframe(mocker, freqaiconf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180111-20180114") corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC') def get_ready_to_train(mocker, freqaiconf): strategy = get_patched_freqai_strategy(mocker, freqaiconf) exchange = get_patched_exchange(mocker, freqaiconf) strategy.dp = DataProvider(freqaiconf, exchange) strategy.freqai_info = freqaiconf.get("freqai", {}) freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd) timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dk.load_all_pair_histories(timerange) sub_timerange = TimeRange.parse_timerange("20180111-20180114") corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC") return corr_df, base_df, freqai, strategy