ruff format: Update a few test files
This commit is contained in:
Matthias
+39
-46
@@ -32,13 +32,12 @@ def is_arm() -> bool:
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@pytest.fixture(autouse=True)
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def patch_torch_initlogs(mocker) -> None:
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if is_mac():
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# Mock torch import completely
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import sys
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import types
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module_name = 'torch'
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module_name = "torch"
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mocked_module = types.ModuleType(module_name)
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sys.modules[module_name] = mocked_module
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else:
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@@ -80,25 +79,23 @@ def freqai_conf(default_conf, tmp_path):
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"stratify_training_data": 0,
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"indicator_periods_candles": [10],
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"shuffle_after_split": False,
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"buffer_train_data_candles": 0
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"buffer_train_data_candles": 0,
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},
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"data_split_parameters": {"test_size": 0.33, "shuffle": False},
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"model_training_parameters": {"n_estimators": 100},
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},
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"config_files": [Path('config_examples', 'config_freqai.example.json')]
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"config_files": [Path("config_examples", "config_freqai.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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freqaiconf["exchange"].update({"pair_whitelist": ["ADA/BTC", "DASH/BTC", "ETH/BTC", "LTC/BTC"]})
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return freqaiconf
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def make_rl_config(conf):
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conf.update({"strategy": "freqai_rl_test_strat"})
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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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conf["freqai"].update(
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{"model_training_parameters": {"learning_rate": 0.00025, "gamma": 0.9, "verbose": 1}}
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)
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conf["freqai"]["rl_config"] = {
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"train_cycles": 1,
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"thread_count": 2,
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@@ -107,31 +104,27 @@ def make_rl_config(conf):
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"policy_type": "MlpPolicy",
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"max_training_drawdown_pct": 0.5,
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"net_arch": [32, 32],
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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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"drop_ohlc_from_features": False
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}
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"model_reward_parameters": {"rr": 1, "profit_aim": 0.02, "win_reward_factor": 2},
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"drop_ohlc_from_features": False,
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}
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return conf
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def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]:
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return {
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"learning_rate": 3e-4,
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"trainer_kwargs": {
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"n_steps": None,
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"batch_size": 64,
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"n_epochs": 1,
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},
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"model_kwargs": {
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"hidden_dim": 32,
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"dropout_percent": 0.2,
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"n_layer": 1,
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}
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}
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"learning_rate": 3e-4,
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"trainer_kwargs": {
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"n_steps": None,
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"batch_size": 64,
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"n_epochs": 1,
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},
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"model_kwargs": {
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"hidden_dim": 32,
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"dropout_percent": 0.2,
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"n_layer": 1,
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},
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}
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def get_patched_data_kitchen(mocker, freqaiconf):
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@@ -178,14 +171,14 @@ def make_unfiltered_dataframe(mocker, freqai_conf):
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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for i in range(5):
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unfiltered_dataframe[f'constant_{i}'] = i
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unfiltered_dataframe[f"constant_{i}"] = i
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unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
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@@ -212,23 +205,23 @@ def make_data_dictionary(mocker, freqai_conf):
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
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freqai.dk.find_features(unfiltered_dataframe)
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features_filtered, labels_filtered = freqai.dk.filter_features(
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
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@@ -247,8 +240,8 @@ def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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strategy.analyze_pair('ADA/BTC', '5m')
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return strategy.dp.get_analyzed_dataframe('ADA/BTC', '5m')
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strategy.analyze_pair("ADA/BTC", "5m")
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return strategy.dp.get_analyzed_dataframe("ADA/BTC", "5m")
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def get_freqai_analyzed_dataframe(mocker, freqaiconf):
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@@ -264,7 +257,7 @@ def get_freqai_analyzed_dataframe(mocker, freqaiconf):
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
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return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
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return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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def get_ready_to_train(mocker, freqaiconf):
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