CTYPTO/freqtrade
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2026-02-06 14:20:24 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: update doc samples to use lowercased tuple and dict

Browse Source
This commit is contained in:
Matthias
2024-11-11 20:24:01 +01:00
parent e8cd6b3be3
commit d9b8f46282
4 changed files with 11 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
docs/advanced-hyperopt.md
View File

@@ -37,8 +37,8 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
min_date: datetime,
max_date: datetime,
config: Config,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
processed: dict[str, DataFrame],
backtest_stats: dict[str, Any],
**kwargs,
) -> float:
"""
@@ -103,7 +103,7 @@ class MyAwesomeStrategy(IStrategy):
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
def generate_roi_table(params: Dict) -> Dict[int, float]:
def generate_roi_table(params: Dict) -> dict[int, float]:
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']

12
docs/freqai-configuration.md
View File

@@ -293,10 +293,10 @@ class MyCoolPyTorchClassifier(BasePyTorchClassifier):
super().__init__(**kwargs)
config = self.freqai_info.get("model_training_parameters", {})
self.learning_rate: float = config.get("learning_rate", 3e-4)
self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary holding all data for train, test,
@@ -359,10 +359,10 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
super().__init__(**kwargs)
config = self.freqai_info.get("model_training_parameters", {})
self.learning_rate: float = config.get("learning_rate", 3e-4)
self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
n_features = data_dictionary["train_features"].shape[-1]
model = PyTorchMLPModel(
input_dim=n_features,

2
docs/strategy-callbacks.md
View File

@@ -835,7 +835,7 @@ class DigDeeperStrategy(IStrategy):
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs
) -> Union[Optional[float], Tuple[Optional[float], Optional[str]]]:
) -> Union[Optional[float], tuple[Optional[float], Optional[str]]]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be
increased or decreased.

2
docs/strategy_migration.md
View File

@@ -780,7 +780,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
) -> tuple[DataFrame, npt.NDArray[np.int_]]:
# ... your custom stuff