Parameterize backtesting test
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@ -173,9 +173,54 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize('model', [
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'LightGBMRegressor',
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'XGBoostRegressor',
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'CatboostRegressor',
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'ReinforcementLearner',
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'ReinforcementLearner_multiproc',
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'ReinforcementLearner_test_4ac'
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])
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def test_start_backtesting(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180120-20180130"})
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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if is_arm() and model == 'CatboostRegressor':
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pytest.skip("CatBoost is not supported on ARM")
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if is_mac():
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pytest.skip("Reinforcement learning module not available on intel based Mac OS")
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model_save_ext = 'joblib'
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_strat"})
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if 'ReinforcementLearner' in model:
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model_save_ext = 'zip'
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freqai_conf.update({"strategy": "freqai_rl_test_strat"})
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freqai_conf["freqai"].update({"model_training_parameters": {
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"learning_rate": 0.00025,
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"gamma": 0.9,
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"verbose": 1
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}})
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freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'})
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freqai_conf["freqai"]["rl_config"] = {
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"train_cycles": 1,
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"thread_count": 2,
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"max_trade_duration_candles": 300,
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"model_type": "PPO",
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"policy_type": "MlpPolicy",
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"max_training_drawdown_pct": 0.5,
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"model_reward_parameters": {
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"rr": 1,
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"profit_aim": 0.02,
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"win_reward_factor": 2
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}}
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if 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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