mohammadian7/freqtrade
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

ruff format: Update a few test files

Browse Source
This commit is contained in:
Matthias
2024-05-12 15:29:14 +02:00
parent baa15f6ed6
commit 7090950db6
13 changed files with 1629 additions and 1283 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/freqai/conftest.py
+39 -46
View File
@@ -32,13 +32,12 @@ def is_arm() -> bool:
@pytest.fixture(autouse=True)
def patch_torch_initlogs(mocker) -> None:
if is_mac():
# Mock torch import completely
import sys
import types
module_name = 'torch'
module_name = "torch"
mocked_module = types.ModuleType(module_name)
sys.modules[module_name] = mocked_module
else:
@@ -80,25 +79,23 @@ def freqai_conf(default_conf, tmp_path):
"stratify_training_data": 0,
"indicator_periods_candles": [10],
"shuffle_after_split": False,
"buffer_train_data_candles": 0
"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')]
"config_files": [Path("config_examples", "config_freqai.example.json")],
}
)
freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
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"].update(
{"model_training_parameters": {"learning_rate": 0.00025, "gamma": 0.9, "verbose": 1}}
)
conf["freqai"]["rl_config"] = {
"train_cycles": 1,
"thread_count": 2,
@@ -107,31 +104,27 @@ def make_rl_config(conf):
"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
},
"drop_ohlc_from_features": False
}
"model_reward_parameters": {"rr": 1, "profit_aim": 0.02, "win_reward_factor": 2},
"drop_ohlc_from_features": False,
}
return conf
def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]:
return {
"learning_rate": 3e-4,
"trainer_kwargs": {
"n_steps": None,
"batch_size": 64,
"n_epochs": 1,
},
"model_kwargs": {
"hidden_dim": 32,
"dropout_percent": 0.2,
"n_layer": 1,
}
}
"learning_rate": 3e-4,
"trainer_kwargs": {
"n_steps": None,
"batch_size": 64,
"n_epochs": 1,
},
"model_kwargs": {
"hidden_dim": 32,
"dropout_percent": 0.2,
"n_layer": 1,
},
}
def get_patched_data_kitchen(mocker, freqaiconf):
@@ -178,14 +171,14 @@ def make_unfiltered_dataframe(mocker, freqai_conf):
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
)
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
)
strategy, corr_dataframes, base_dataframes, freqai.dk.pair
)
for i in range(5):
unfiltered_dataframe[f'constant_{i}'] = i
unfiltered_dataframe[f"constant_{i}"] = i
unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
@@ -212,23 +205,23 @@ def make_data_dictionary(mocker, freqai_conf):
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
)
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
)
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,
)
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)
@@ -247,8 +240,8 @@ def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
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')
strategy.analyze_pair("ADA/BTC", "5m")
return strategy.dp.get_analyzed_dataframe("ADA/BTC", "5m")
def get_freqai_analyzed_dataframe(mocker, freqaiconf):
@@ -264,7 +257,7 @@ def get_freqai_analyzed_dataframe(mocker, freqaiconf):
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')
return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
def get_ready_to_train(mocker, freqaiconf):
tests/freqai/test_models/ReinforcementLearner_test_3ac.py
+7 -6
View File
@@ -26,24 +26,25 @@ class ReinforcementLearner_test_3ac(ReinforcementLearner):
"""
def calculate_reward(self, action: int) -> float:
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
rew = np.sign(pnl) * (pnl + 1)
factor = 100.
factor = 100.0
# reward agent for entering trades
if (action in (Actions.Buy.value, Actions.Sell.value)
and self._position == Positions.Neutral):
if (
action in (Actions.Buy.value, Actions.Sell.value)
and self._position == Positions.Neutral
):
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
trade_duration = self._current_tick - self._last_trade_tick # type: ignore
if trade_duration <= max_trade_duration:
@@ -67,4 +68,4 @@ class ReinforcementLearner_test_3ac(ReinforcementLearner):
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
return float(rew * factor)
return 0.
return 0.0
tests/freqai/test_models/ReinforcementLearner_test_4ac.py
+13 -10
View File
@@ -26,24 +26,25 @@ class ReinforcementLearner_test_4ac(ReinforcementLearner):
"""
def calculate_reward(self, action: int) -> float:
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
pnl = self.get_unrealized_profit()
rew = np.sign(pnl) * (pnl + 1)
factor = 100.
factor = 100.0
# reward agent for entering trades
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
if (
action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral
):
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
trade_duration = self._current_tick - self._last_trade_tick # type: ignore
if trade_duration <= max_trade_duration:
@@ -52,20 +53,22 @@ class ReinforcementLearner_test_4ac(ReinforcementLearner):
factor *= 0.5
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
if (
self._position in (Positions.Short, Positions.Long)
and action == Actions.Neutral.value
):
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
return float(rew * factor)
# close short
if action == Actions.Exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
return float(rew * factor)
return 0.
return 0.0