Merge 705d1e4cc0 into f50a633f87

docs/trade-object.md

@@ -130,20 +130,20 @@ Most properties here can be None as they are dependent on the exchange response.
 
 |  Attribute | DataType | Description |
 |------------|-------------|-------------|
-`trade` | Trade | Trade object this order is attached to
-`ft_pair` | string | Pair this order is for
-`ft_is_open` | boolean | is the order filled?
-`order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss
-`status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled
-`side` | string | Buy or Sell
-`price` | float | Price the order was placed at
-`average` | float | Average price the order filled at
-`amount` | float | Amount in base currency
-`filled` | float | Filled amount (in base currency)
-`remaining` | float | Remaining amount
-`cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*)
-`stake_amount` | float | Stake amount used for this order. *Added in 2023.7.*
-`order_date` | datetime | Order creation date **use `order_date_utc` instead**
-`order_date_utc` | datetime | Order creation date (in UTC)
-`order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead**
-`order_fill_date_utc` | datetime | Order fill date
+| `trade` | Trade | Trade object this order is attached to |
+| `ft_pair` | string | Pair this order is for |
+| `ft_is_open` | boolean | is the order filled? |
+| `order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss |
+| `status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled |
+| `side` | string | Buy or Sell |
+| `price` | float | Price the order was placed at |
+| `average` | float | Average price the order filled at |
+| `amount` | float | Amount in base currency |
+| `filled` | float | Filled amount (in base currency) |
+| `remaining` | float | Remaining amount |
+| `cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*) |
+| `stake_amount` | float | Stake amount used for this order. *Added in 2023.7.* |
+| `order_date` | datetime | Order creation date **use `order_date_utc` instead** |
+| `order_date_utc` | datetime | Order creation date (in UTC) |
+| `order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead** |
+| `order_fill_date_utc` | datetime | Order fill date |

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

freqtrade/freqtradebot.py

@@ -62,7 +62,7 @@ from freqtrade.rpc.rpc_types import (
 )
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
-from freqtrade.util import MeasureTime
+from freqtrade.util import FtPrecise, MeasureTime
 from freqtrade.util.migrations.binance_mig import migrate_binance_futures_names
 from freqtrade.wallets import Wallets
 
@@ -784,7 +784,14 @@ class FreqtradeBot(LoggingMixin):
         if stake_amount is not None and stake_amount < 0.0:
             # We should decrease our position
             amount = self.exchange.amount_to_contract_precision(
-                trade.pair, abs(float(stake_amount * trade.amount / trade.stake_amount))
+                trade.pair,
+                abs(
+                    float(
+                        FtPrecise(stake_amount)
+                        * FtPrecise(trade.amount)
+                        / FtPrecise(trade.stake_amount)
+                    )
+                ),
             )
 
             if amount == 0.0:

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

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