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add tests. add guardrails.

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This commit is contained in:
robcaulk
2022-09-15 00:46:35 +02:00
parent 48140bff91
commit 8aac644009
9 changed files with 84 additions and 37 deletions
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40
tests/freqai/test_freqai_interface.py
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@@ -21,15 +21,40 @@ def is_arm() -> bool:
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
'ReinforcementLearner',
'ReinforcementLearner_multiproc'
])
def test_extract_data_and_train_model_Regressors(mocker, freqai_conf, model):
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")
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
}}
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@@ -42,16 +67,19 @@ def test_extract_data_and_train_model_Regressors(mocker, freqai_conf, model):
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
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.joblib").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()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
# if 'ReinforcementLearner' not in model:
# assert Path(freqai.dk.data_path /
# f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
shutil.rmtree(Path(freqai.dk.full_path))
@@ -91,7 +119,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
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']) == 26
assert len(freqai.dk.data['training_features_list']) == 14
shutil.rmtree(Path(freqai.dk.full_path))