from copy import deepcopy from pathlib import Path from unittest.mock import MagicMock import pytest from freqtrade.configuration import TimeRange from freqtrade.data.dataprovider import DataProvider from freqtrade.freqai.data_drawer import FreqaiDataDrawer from freqtrade.freqai.data_kitchen import FreqaiDataKitchen 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": "LightGBMRegressor", "freqaimodel_path": "freqai/prediction_models", "timerange": "20180110-20180115", "freqai": { "enabled": True, "purge_old_models": 2, "train_period_days": 2, "backtest_period_days": 10, "live_retrain_hours": 0, "expiration_hours": 1, "identifier": "uniqe-id100", "live_trained_timestamp": 0, "data_kitchen_thread_count": 2, "feature_parameters": { "include_timeframes": ["5m"], "include_corr_pairlist": ["ADA/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_periods_candles": [10], "shuffle_after_split": False, "buffer_train_data_candles": 0 }, "data_split_parameters": {"test_size": 0.33, "shuffle": False}, "model_training_parameters": {"n_estimators": 100}, }, "config_files": [Path('config_examples', 'config_freqai.example.json')] } ) freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']}) return freqaiconf def make_rl_config(conf): conf.update({"strategy": "freqai_rl_test_strat"}) conf["freqai"].update({"model_training_parameters": { "learning_rate": 0.00025, "gamma": 0.9, "verbose": 1 }}) 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, "net_arch": [32, 32], "model_reward_parameters": { "rr": 1, "profit_aim": 0.02, "win_reward_factor": 2 }} return conf def mock_pytorch_mlp_model_training_parameters(conf): return { "learning_rate": 3e-4, "trainer_kwargs": { "max_iters": 1, "batch_size": 64, "max_n_eval_batches": 1, }, "model_kwargs": { "hidden_dim": 32, "dropout_percent": 0.2, "n_layer": 1, } } def get_patched_data_kitchen(mocker, freqaiconf): 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 make_unfiltered_dataframe(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) freqai.dk.pair = "ADA/BTC" data_load_timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() new_timerange = TimeRange.parse_timerange("20180120-20180130") corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes( data_load_timerange, freqai.dk.pair, freqai.dk ) unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators( strategy, corr_dataframes, base_dataframes, freqai.dk.pair ) for i in range(5): unfiltered_dataframe[f'constant_{i}'] = i unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe) return freqai, unfiltered_dataframe def make_data_dictionary(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) freqai.dk.pair = "ADA/BTC" data_load_timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk) freqai.dd.pair_dict = MagicMock() new_timerange = TimeRange.parse_timerange("20180120-20180130") corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes( data_load_timerange, freqai.dk.pair, freqai.dk ) unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators( strategy, corr_dataframes, base_dataframes, freqai.dk.pair ) unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe) freqai.dk.find_features(unfiltered_dataframe) features_filtered, labels_filtered = freqai.dk.filter_features( unfiltered_dataframe, freqai.dk.training_features_list, freqai.dk.label_list, training_filter=True, ) data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered) data_dictionary = freqai.dk.normalize_data(data_dictionary) return freqai 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