mirror of
https://github.com/freqtrade/freqtrade.git
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69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
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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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