import logging import shutil from pathlib import Path from unittest.mock import MagicMock import pytest from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import RunMode from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange from freqtrade.optimize.backtesting import Backtesting from freqtrade.persistence import Trade from freqtrade.plugins.pairlistmanager import PairListManager from tests.conftest import ( EXMS, create_mock_trades, get_patched_exchange, is_arm, is_mac, log_has_re, ) from tests.freqai.conftest import ( get_patched_freqai_strategy, make_rl_config, mock_pytorch_mlp_model_training_parameters, ) def can_run_model(model: str) -> None: is_pytorch_model = "Reinforcement" in model or "PyTorch" in model if is_pytorch_model and is_mac(): pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.") @pytest.mark.parametrize( "model, pca, dbscan, float32, can_short, shuffle, buffer, noise", [ ("LightGBMRegressor", True, False, True, True, False, 0, 0), ("XGBoostRegressor", False, True, False, True, False, 10, 0.05), ("XGBoostRFRegressor", False, False, False, True, False, 0, 0), ("PyTorchMLPRegressor", False, False, False, False, False, 0, 0), ("PyTorchTransformerRegressor", False, False, False, False, False, 0, 0), ("ReinforcementLearner", False, True, False, True, False, 0, 0), ("ReinforcementLearner_multiproc", False, False, False, True, False, 0, 0), ("ReinforcementLearner_test_3ac", False, False, False, False, False, 0, 0), ("ReinforcementLearner_test_3ac", False, False, False, True, False, 0, 0), ("ReinforcementLearner_test_4ac", False, False, False, True, False, 0, 0), ], ) def test_extract_data_and_train_model_Standard( mocker, freqai_conf, model, pca, dbscan, float32, can_short, shuffle, buffer, noise ): can_run_model(model) test_tb = True if is_mac(): test_tb = False model_save_ext = "joblib" freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"strategy": "freqai_test_strat"}) freqai_conf["freqai"]["feature_parameters"].update({"principal_component_analysis": pca}) freqai_conf["freqai"]["feature_parameters"].update({"use_DBSCAN_to_remove_outliers": dbscan}) freqai_conf.update({"reduce_df_footprint": float32}) freqai_conf["freqai"]["feature_parameters"].update({"shuffle_after_split": shuffle}) freqai_conf["freqai"]["feature_parameters"].update({"buffer_train_data_candles": buffer}) freqai_conf["freqai"]["feature_parameters"].update({"noise_standard_deviation": noise}) if "ReinforcementLearner" in model: model_save_ext = "zip" freqai_conf = make_rl_config(freqai_conf) # test the RL guardrails freqai_conf["freqai"]["feature_parameters"].update({"use_SVM_to_remove_outliers": True}) freqai_conf["freqai"]["feature_parameters"].update({"DI_threshold": 2}) freqai_conf["freqai"]["data_split_parameters"].update({"shuffle": True}) if "test_3ac" in model or "test_4ac" in model: freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") freqai_conf["freqai"]["rl_config"]["drop_ohlc_from_features"] = True if "PyTorch" in model: model_save_ext = "zip" pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters() freqai_conf["freqai"]["model_training_parameters"].update(pytorch_mlp_mtp) if "Transformer" in model: # transformer model takes a window, unlike the MLP regressor freqai_conf.update({"conv_width": 10}) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True freqai.activate_tensorboard = test_tb freqai.can_short = can_short freqai.dk = FreqaiDataKitchen(freqai_conf) freqai.dk.live = True freqai.dk.set_paths("ADA/BTC", 10000) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() data_load_timerange = TimeRange.parse_timerange("20180125-20180130") new_timerange = TimeRange.parse_timerange("20180127-20180130") freqai.dk.set_paths("ADA/BTC", None) freqai.train_timer("start", "ADA/BTC") freqai.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange ) freqai.train_timer("stop", "ADA/BTC") freqai.dd.save_metric_tracker_to_disk() freqai.dd.save_drawer_to_disk() assert Path(freqai.dk.full_path / "metric_tracker.json").is_file() assert Path(freqai.dk.full_path / "pair_dictionary.json").is_file() assert Path( freqai.dk.data_path / f"{freqai.dk.model_filename}_model.{model_save_ext}" ).is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() shutil.rmtree(Path(freqai.dk.full_path)) @pytest.mark.parametrize( "model, strat", [ ("LightGBMRegressorMultiTarget", "freqai_test_multimodel_strat"), ("XGBoostRegressorMultiTarget", "freqai_test_multimodel_strat"), ("LightGBMClassifierMultiTarget", "freqai_test_multimodel_classifier_strat"), ], ) @pytest.mark.filterwarnings(r"ignore:.*__sklearn_tags__.*:DeprecationWarning") def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat): can_run_model(model) freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"strategy": strat}) freqai_conf.update({"freqaimodel": model}) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() data_load_timerange = TimeRange.parse_timerange("20180110-20180130") new_timerange = TimeRange.parse_timerange("20180120-20180130") freqai.dk.set_paths("ADA/BTC", None) freqai.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange ) assert len(freqai.dk.label_list) == 2 assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file() assert len(freqai.dk.data["training_features_list"]) == 14 shutil.rmtree(Path(freqai.dk.full_path)) @pytest.mark.parametrize( "model", [ "LightGBMClassifier", "XGBoostClassifier", "XGBoostRFClassifier", "SKLearnRandomForestClassifier", "PyTorchMLPClassifier", ], ) def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model): can_run_model(model) freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"strategy": "freqai_test_classifier"}) freqai_conf.update({"timerange": "20180110-20180130"}) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() data_load_timerange = TimeRange.parse_timerange("20180110-20180130") new_timerange = TimeRange.parse_timerange("20180120-20180130") freqai.dk.set_paths("ADA/BTC", None) freqai.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange ) if "PyTorchMLPClassifier": pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters() freqai_conf["freqai"]["model_training_parameters"].update(pytorch_mlp_mtp) if freqai.dd.model_type == "joblib": model_file_extension = ".joblib" elif freqai.dd.model_type == "pytorch": model_file_extension = ".zip" else: raise Exception( f"Unsupported model type: {freqai.dd.model_type}, can't assign model_file_extension" ) assert Path( freqai.dk.data_path / f"{freqai.dk.model_filename}_model{model_file_extension}" ).exists() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists() shutil.rmtree(Path(freqai.dk.full_path)) @pytest.mark.parametrize( "model, num_files, strat", [ ("LightGBMRegressor", 2, "freqai_test_strat"), ("XGBoostRegressor", 2, "freqai_test_strat"), ("PyTorchMLPRegressor", 2, "freqai_test_strat"), ("PyTorchTransformerRegressor", 2, "freqai_test_strat"), ("ReinforcementLearner", 3, "freqai_rl_test_strat"), ("XGBoostClassifier", 2, "freqai_test_classifier"), ("LightGBMClassifier", 2, "freqai_test_classifier"), ("PyTorchMLPClassifier", 2, "freqai_test_classifier"), ], ) def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog): can_run_model(model) test_tb = True if is_mac() and not is_arm(): test_tb = False freqai_conf.get("freqai", {}).update({"save_backtest_models": True}) freqai_conf["runmode"] = RunMode.BACKTEST Trade.use_db = False freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf.update({"strategy": strat}) if "ReinforcementLearner" in model: freqai_conf = make_rl_config(freqai_conf) if "test_4ac" in model: freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") if "PyTorch" in model: pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters() freqai_conf["freqai"]["model_training_parameters"].update(pytorch_mlp_mtp) if "Transformer" in model: # transformer model takes a window, unlike the MLP regressor freqai_conf.update({"conv_width": 10}) freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( {"indicator_periods_candles": [2]} ) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False freqai.activate_tensorboard = test_tb freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = base_df[freqai_conf["timeframe"]] metadata = {"pair": "LTC/BTC"} freqai.dk.set_paths("LTC/BTC", None) freqai.start_backtesting(df, metadata, freqai.dk, strategy) model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] assert len(model_folders) == num_files Trade.use_db = True Backtesting.cleanup() shutil.rmtree(Path(freqai.dk.full_path)) def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf): freqai_conf.update({"timerange": "20180120-20180124"}) freqai_conf["runmode"] = "backtest" freqai_conf.get("freqai", {}).update( { "backtest_period_days": 0.5, "save_backtest_models": True, } ) freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( {"indicator_periods_candles": [2]} ) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = base_df[freqai_conf["timeframe"]] metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk, strategy) model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] assert len(model_folders) == 9 shutil.rmtree(Path(freqai.dk.full_path)) def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf["runmode"] = "backtest" freqai_conf.get("freqai", {}).update({"save_backtest_models": True}) freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( {"indicator_periods_candles": [2]} ) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180101-20180130") _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = base_df[freqai_conf["timeframe"]] pair = "ADA/BTC" metadata = {"pair": pair} freqai.dk.pair = pair freqai.start_backtesting(df, metadata, freqai.dk, strategy) model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] assert len(model_folders) == 2 # without deleting the existing folder structure, re-run freqai_conf.update({"timerange": "20180120-20180130"}) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180110-20180130") _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = base_df[freqai_conf["timeframe"]] pair = "ADA/BTC" metadata = {"pair": pair} freqai.dk.pair = pair freqai.start_backtesting(df, metadata, freqai.dk, strategy) assert log_has_re( "Found backtesting prediction file ", caplog, ) pair = "ETH/BTC" metadata = {"pair": pair} freqai.dk.pair = pair freqai.start_backtesting(df, metadata, freqai.dk, strategy) path = freqai.dd.full_path / freqai.dk.backtest_predictions_folder prediction_files = [x for x in path.iterdir() if x.is_file()] assert len(prediction_files) == 2 shutil.rmtree(Path(freqai.dk.full_path)) def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog): freqai_conf["runmode"] = "backtest" freqai_conf.get("freqai", {}).update({"fit_live_predictions_candles": 10}) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False freqai.dk = FreqaiDataKitchen(freqai_conf) timerange = TimeRange.parse_timerange("20180128-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180129-20180130") corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") df = strategy.set_freqai_targets(df.copy(), metadata={"pair": "LTC/BTC"}) df = freqai.dk.remove_special_chars_from_feature_names(df) freqai.dk.get_unique_classes_from_labels(df) freqai.dk.pair = "ADA/BTC" freqai.dk.full_df = df.fillna(0) assert "&-s_close_mean" not in freqai.dk.full_df.columns assert "&-s_close_std" not in freqai.dk.full_df.columns freqai.backtesting_fit_live_predictions(freqai.dk) assert "&-s_close_mean" in freqai.dk.full_df.columns assert "&-s_close_std" in freqai.dk.full_df.columns shutil.rmtree(Path(freqai.dk.full_path)) def test_plot_feature_importance(mocker, freqai_conf): from freqtrade.freqai.utils import plot_feature_importance freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( {"princpial_component_analysis": "true"} ) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) freqai.dd.pair_dict = { "ADA/BTC": { "model_filename": "fake_name", "trained_timestamp": 1, "data_path": "", "extras": {}, } } data_load_timerange = TimeRange.parse_timerange("20180110-20180130") new_timerange = TimeRange.parse_timerange("20180120-20180130") freqai.dk.set_paths("ADA/BTC", None) freqai.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange ) model = freqai.dd.load_data("ADA/BTC", freqai.dk) plot_feature_importance(model, "ADA/BTC", freqai.dk) assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html") shutil.rmtree(Path(freqai.dk.full_path)) @pytest.mark.parametrize( "timeframes,corr_pairs", [ (["5m"], ["ADA/BTC", "DASH/BTC"]), (["5m"], ["ADA/BTC", "DASH/BTC", "ETH/USDT"]), (["5m", "15m"], ["ADA/BTC", "DASH/BTC", "ETH/USDT"]), ], ) def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs): freqai_conf["freqai"]["feature_parameters"].update( { "include_timeframes": timeframes, "include_corr_pairlist": corr_pairs, } ) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) pairlists = PairListManager(exchange, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange, pairlists) pairlist = strategy.dp.current_whitelist() pairs_a = strategy.informative_pairs() assert len(pairs_a) == 0 pairs_b = strategy.gather_informative_pairs() # we expect unique pairs * timeframes assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes) def test_start_set_train_queue(mocker, freqai_conf, caplog): strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) pairlist = PairListManager(exchange, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange, pairlist) strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.live = False freqai.train_queue = freqai._set_train_queue() assert log_has_re( "Set fresh train queue from whitelist.", caplog, ) def test_get_required_data_timerange(mocker, freqai_conf): time_range = get_required_data_timerange(freqai_conf) assert (time_range.stopts - time_range.startts) == 177300 def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmp_path): caplog.set_level(logging.DEBUG) strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) pairlist = PairListManager(exchange, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange, pairlist) freqai_conf["pairs"] = freqai_conf["exchange"]["pair_whitelist"] freqai_conf["datadir"] = tmp_path download_all_data_for_training(strategy.dp, freqai_conf) assert log_has_re( "Downloading", caplog, ) @pytest.mark.usefixtures("init_persistence") @pytest.mark.parametrize("dp_exists", [(False), (True)]) def test_get_state_info(mocker, freqai_conf, dp_exists, caplog, tickers): if is_mac(): pytest.skip("Reinforcement learning module not available on intel based Mac OS") freqai_conf.update({"freqaimodel": "ReinforcementLearner"}) freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"strategy": "freqai_rl_test_strat"}) freqai_conf = make_rl_config(freqai_conf) freqai_conf["entry_pricing"]["price_side"] = "same" freqai_conf["exit_pricing"]["price_side"] = "same" strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) ticker_mock = MagicMock(return_value=tickers()["ETH/BTC"]) mocker.patch(f"{EXMS}.fetch_ticker", ticker_mock) strategy.dp = DataProvider(freqai_conf, exchange) if not dp_exists: strategy.dp._exchange = None strategy.freqai_info = freqai_conf.get("freqai", {}) freqai = strategy.freqai freqai.data_provider = strategy.dp freqai.live = True Trade.use_db = True create_mock_trades(MagicMock(return_value=0.0025), False, True) freqai.get_state_info("ADA/BTC") freqai.get_state_info("ETH/BTC") if not dp_exists: assert log_has_re( "No exchange available", caplog, )