import platform import shutil from pathlib import Path from unittest.mock import MagicMock import pytest from freqtrade.enums import RunMode from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.plugins.pairlistmanager import PairListManager from tests.conftest import get_patched_exchange, log_has_re from tests.freqai.conftest import get_patched_freqai_strategy from freqtrade.persistence import Trade from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange def is_arm() -> bool: machine = platform.machine() return "arm" in machine or "aarch64" in machine def is_mac() -> bool: machine = platform.system() return "Darwin" in machine @pytest.mark.parametrize('model', [ 'LightGBMRegressor', 'XGBoostRegressor', 'CatboostRegressor', 'ReinforcementLearner', 'ReinforcementLearner_multiproc', 'ReinforcementLearner_test_4ac' ]) def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model): if is_arm() and model == 'CatboostRegressor': pytest.skip("CatBoost is not supported on ARM") if is_mac(): pytest.skip("Reinforcement learning module not available on intel based Mac OS") model_save_ext = 'joblib' freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"strategy": "freqai_test_strat"}) if 'ReinforcementLearner' in model: model_save_ext = 'zip' freqai_conf.update({"strategy": "freqai_rl_test_strat"}) freqai_conf["freqai"].update({"model_training_parameters": { "learning_rate": 0.00025, "gamma": 0.9, "verbose": 1 }}) freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'}) freqai_conf["freqai"]["rl_config"] = { "train_cycles": 1, "thread_count": 2, "max_trade_duration_candles": 300, "model_type": "PPO", "policy_type": "MlpPolicy", "max_training_drawdown_pct": 0.5, "model_reward_parameters": { "rr": 1, "profit_aim": 0.02, "win_reward_factor": 2 }} if 'test_4ac' in model: freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") 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) 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.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) 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', [ 'LightGBMRegressorMultiTarget', 'XGBoostRegressorMultiTarget', 'CatboostRegressorMultiTarget', ]) def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model): if is_arm() and model == 'CatboostRegressorMultiTarget': pytest.skip("CatBoost is not supported on ARM") freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"strategy": "freqai_test_multimodel_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) 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.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 Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() assert len(freqai.dk.data['training_features_list']) == 14 shutil.rmtree(Path(freqai.dk.full_path)) @pytest.mark.parametrize('model', [ 'LightGBMClassifier', 'CatboostClassifier', 'XGBoostClassifier', ]) def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model): if is_arm() and model == 'CatboostClassifier': pytest.skip("CatBoost is not supported on ARM") 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) 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.extract_data_and_train_model(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").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() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists() shutil.rmtree(Path(freqai.dk.full_path)) @pytest.mark.parametrize( "model, num_files, strat", [ ("LightGBMRegressor", 6, "freqai_test_strat"), ("XGBoostRegressor", 6, "freqai_test_strat"), ("CatboostRegressor", 6, "freqai_test_strat"), ("ReinforcementLearner", 7, "freqai_rl_test_strat"), ("XGBoostClassifier", 6, "freqai_test_classifier"), ("LightGBMClassifier", 6, "freqai_test_classifier"), ("CatboostClassifier", 6, "freqai_test_classifier") ], ) def test_start_backtesting(mocker, freqai_conf, model, num_files, strat): freqai_conf.get("freqai", {}).update({"save_backtest_models": True}) freqai_conf['runmode'] = RunMode.BACKTEST Trade.use_db = False if is_arm() and "Catboost" in model: pytest.skip("CatBoost is not supported on ARM") if is_mac(): pytest.skip("Reinforcement learning module not available on intel based Mac OS") freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf.update({"strategy": strat}) if 'ReinforcementLearner' in model: freqai_conf["freqai"].update({"model_training_parameters": { "learning_rate": 0.00025, "gamma": 0.9, "verbose": 1 }}) freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'}) freqai_conf["freqai"]["rl_config"] = { "train_cycles": 1, "thread_count": 2, "max_trade_duration_candles": 300, "model_type": "PPO", "policy_type": "MlpPolicy", "max_training_drawdown_pct": 0.5, "model_reward_parameters": { "rr": 1, "profit_aim": 0.02, "win_reward_factor": 2 }} if 'test_4ac' in model: freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") 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") 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") metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) 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 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.get("freqai", {}).update({"backtest_period_days": 0.5}) freqai_conf.get("freqai", {}).update({"save_backtest_models": 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 = 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") 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") metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) 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.get("freqai", {}).update({"save_backtest_models": 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 = 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") 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") metadata = {"pair": "ADA/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] assert len(model_folders) == 6 # 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") 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") freqai.start_backtesting(df, metadata, freqai.dk) assert log_has_re( "Found backtesting prediction file ", caplog, ) 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) == 5 shutil.rmtree(Path(freqai.dk.full_path)) def test_follow_mode(mocker, freqai_conf): 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) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) metadata = {"pair": "ADA/BTC"} freqai.dd.set_pair_dict_info(metadata) data_load_timerange = TimeRange.parse_timerange("20180110-20180130") new_timerange = TimeRange.parse_timerange("20180120-20180130") freqai.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) 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 Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file() # start the follower and ask it to predict on existing files freqai_conf.get("freqai", {}).update({"follow_mode": "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.live) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m') freqai.start_live(df, metadata, strategy, freqai.dk) assert len(freqai.dk.return_dataframe.index) == 5702 shutil.rmtree(Path(freqai.dk.full_path)) def test_principal_component_analysis(mocker, freqai_conf): 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) 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.extract_data_and_train_model( new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl") 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) 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.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, tmpdir): 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'] = Path(tmpdir) download_all_data_for_training(strategy.dp, freqai_conf) assert log_has_re( "Downloading", caplog, )