Merge 705d1e4cc0 into 2fe67edab3

chore(deps-dev): bump types-requests from 2.32.0.20240907 to 2.32.0.20240914 in the types group

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
         additional_dependencies:
           - types-cachetools==5.5.0.20240820
           - types-filelock==3.2.7
-          - types-requests==2.32.0.20240907
+          - types-requests==2.32.0.20240914
           - types-tabulate==0.9.0.20240106
           - types-python-dateutil==2.9.0.20240906
           - SQLAlchemy==2.0.34

freqtrade/exchange/exchange.py

@@ -3594,7 +3594,7 @@ class Exchange:
             Wherein, "+" or "-" depends on whether the contract goes long or short:
             "-" for long, and "+" for short.
 
-         okex: https://www.okex.com/support/hc/en-us/articles/
+         okex: https://www.okx.com/support/hc/en-us/articles/
             360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
 
         :param pair: Pair to calculate liquidation price for

freqtrade/freqai/RL/BaseEnvironment.py

@@ -47,19 +47,20 @@ class BaseEnvironment(gym.Env):
 
     def __init__(
         self,
-        df: DataFrame = DataFrame(),
-        prices: DataFrame = DataFrame(),
-        reward_kwargs: dict = {},
+        *,
+        df: DataFrame,
+        prices: DataFrame,
+        reward_kwargs: dict,
         window_size=10,
         starting_point=True,
         id: str = "baseenv-1",  # noqa: A002
         seed: int = 1,
-        config: dict = {},
+        config: dict,
         live: bool = False,
         fee: float = 0.0015,
         can_short: bool = False,
         pair: str = "",
-        df_raw: DataFrame = DataFrame(),
+        df_raw: DataFrame,
     ):
         """
         Initializes the training/eval environment.

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -488,7 +488,7 @@ def make_env(
     seed: int,
     train_df: DataFrame,
     price: DataFrame,
-    env_info: Dict[str, Any] = {},
+    env_info: Dict[str, Any],
 ) -> Callable:
     """
     Utility function for multiprocessed env.

freqtrade/freqai/data_kitchen.py

@@ -214,7 +214,7 @@ class FreqaiDataKitchen:
         self,
         unfiltered_df: DataFrame,
         training_feature_list: List,
-        label_list: List = list(),
+        label_list: Optional[List] = None,
         training_filter: bool = True,
     ) -> Tuple[DataFrame, DataFrame]:
         """
@@ -244,7 +244,7 @@ class FreqaiDataKitchen:
             # we don't care about total row number (total no. datapoints) in training, we only care
             # about removing any row with NaNs
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
-            labels = unfiltered_df.filter(label_list, axis=1)
+            labels = unfiltered_df.filter(label_list or [], axis=1)
             drop_index_labels = pd.isnull(labels).any(axis=1)
             drop_index_labels = (
                 drop_index_labels.replace(True, 1).replace(False, 0).infer_objects(copy=False)
@@ -654,8 +654,8 @@ class FreqaiDataKitchen:
         pair: str,
         tf: str,
         strategy: IStrategy,
-        corr_dataframes: dict = {},
-        base_dataframes: dict = {},
+        corr_dataframes: dict,
+        base_dataframes: dict,
         is_corr_pairs: bool = False,
     ) -> DataFrame:
         """
@@ -776,7 +776,7 @@ class FreqaiDataKitchen:
         corr_dataframes: dict = {},
         base_dataframes: dict = {},
         pair: str = "",
-        prediction_dataframe: DataFrame = pd.DataFrame(),
+        prediction_dataframe: Optional[DataFrame] = None,
         do_corr_pairs: bool = True,
     ) -> DataFrame:
         """
@@ -822,7 +822,7 @@ class FreqaiDataKitchen:
                 if tf not in corr_dataframes[p]:
                     corr_dataframes[p][tf] = pd.DataFrame()
 
-        if not prediction_dataframe.empty:
+        if prediction_dataframe is not None and not prediction_dataframe.empty:
             dataframe = prediction_dataframe.copy()
             base_dataframes[self.config["timeframe"]] = dataframe.copy()
         else:

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -25,7 +25,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         criterion: nn.Module,
         device: str,
         data_convertor: PyTorchDataConvertor,
-        model_meta_data: Dict[str, Any] = {},
+        model_meta_data: Optional[Dict[str, Any]] = None,
         window_size: int = 1,
         tb_logger: Any = None,
         **kwargs,
@@ -45,6 +45,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         :param n_epochs: The maximum number batches to use for evaluation.
         :param batch_size: The size of the batches to use during training.
         """
+        if model_meta_data is None:
+            model_meta_data = {}
         self.model = model
         self.optimizer = optimizer
         self.criterion = criterion

pyproject.toml

@@ -168,8 +168,6 @@ max-complexity = 12
 [tool.ruff.lint.per-file-ignores]
 "freqtrade/freqai/**/*.py" = [
   "S311",  # Standard pseudo-random generators are not suitable for cryptographic purposes
-  "B006",  # Bugbear - mutable default argument
-  "B008",  # bugbear - Do not perform function calls in argument defaults
 ]
 "tests/**/*.py" = [
   "S101",  # allow assert in tests

requirements-dev.txt

@@ -7,10 +7,10 @@
 -r docs/requirements-docs.txt
 
 coveralls==4.0.1
-ruff==0.6.4
+ruff==0.6.5
 mypy==1.11.2
 pre-commit==3.8.0
-pytest==8.3.2
+pytest==8.3.3
 pytest-asyncio==0.24.0
 pytest-cov==5.0.0
 pytest-mock==3.14.0
@@ -27,6 +27,6 @@ nbconvert==7.16.4
 # mypy types
 types-cachetools==5.5.0.20240820
 types-filelock==3.2.7
-types-requests==2.32.0.20240907
+types-requests==2.32.0.20240914
 types-tabulate==0.9.0.20240106
 types-python-dateutil==2.9.0.20240906

requirements-freqai.txt

@@ -3,7 +3,7 @@
 -r requirements-plot.txt
 
 # Required for freqai
-scikit-learn==1.5.1
+scikit-learn==1.5.2
 joblib==1.4.2
 catboost==1.2.7; 'arm' not in platform_machine
 # Pin Matplotlib - it's depended on by catboost

requirements-hyperopt.txt

@@ -4,6 +4,6 @@
 # Required for hyperopt
 scipy==1.14.1; python_version >= "3.10"
 scipy==1.13.1; python_version < "3.10"
-scikit-learn==1.5.1
+scikit-learn==1.5.2
 ft-scikit-optimize==0.9.2
 filelock==3.16.0

requirements-plot.txt

@@ -1,4 +1,4 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==5.24.0
+plotly==5.24.1

requirements.txt

@@ -4,7 +4,7 @@ bottleneck==1.4.0
 numexpr==2.10.1
 pandas-ta==0.3.14b
 
-ccxt==4.3.98
+ccxt==4.4.3
 cryptography==42.0.8; platform_machine == 'armv7l'
 cryptography==43.0.1; platform_machine != 'armv7l'
 aiohttp==3.10.5
@@ -15,7 +15,7 @@ httpx>=0.24.1
 humanize==4.10.0
 cachetools==5.5.0
 requests==2.32.3
-urllib3==2.2.2
+urllib3==2.2.3
 jsonschema==4.23.0
 TA-Lib==0.4.32
 technical==1.4.4
@@ -26,7 +26,7 @@ jinja2==3.1.4
 tables==3.9.1; python_version < "3.10"
 tables==3.10.1; python_version >= "3.10"
 joblib==1.4.2
-rich==13.8.0
+rich==13.8.1
 pyarrow==17.0.0; platform_machine != 'armv7l'
 
 # find first, C search in arrays
@@ -41,8 +41,8 @@ orjson==3.10.7
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.114.0
-pydantic==2.9.0
+fastapi==0.114.2
+pydantic==2.9.1
 uvicorn==0.30.6
 pyjwt==2.9.0
 aiofiles==24.1.0
@@ -53,7 +53,7 @@ questionary==2.0.1
 prompt-toolkit==3.0.36
 # Extensions to datetime library
 python-dateutil==2.9.0.post0
-pytz==2024.1
+pytz==2024.2
 
 #Futures
 schedule==1.2.2

tests/freqai/test_freqai_datakitchen.py

@@ -151,7 +151,9 @@ def test_get_pair_data_for_features_with_prealoaded_data(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     _, base_df = freqai.dd.get_base_and_corr_dataframes(timerange, "LTC/BTC", freqai.dk)
-    df = freqai.dk.get_pair_data_for_features("LTC/BTC", "5m", strategy, base_dataframes=base_df)
+    df = freqai.dk.get_pair_data_for_features(
+        "LTC/BTC", "5m", strategy, {}, base_dataframes=base_df
+    )
 
     assert df is base_df["5m"]
     assert not df.empty
@@ -171,7 +173,9 @@ def test_get_pair_data_for_features_without_preloaded_data(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     base_df = {"5m": pd.DataFrame()}
-    df = freqai.dk.get_pair_data_for_features("LTC/BTC", "5m", strategy, base_dataframes=base_df)
+    df = freqai.dk.get_pair_data_for_features(
+        "LTC/BTC", "5m", strategy, {}, base_dataframes=base_df
+    )
 
     assert df is not base_df["5m"]
     assert not df.empty