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https://github.com/freqtrade/freqtrade.git
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chore: fix flake8-builtins overrides
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parent
deeabbca12
commit
4a4e6bc952
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@ -52,7 +52,7 @@ class BaseEnvironment(gym.Env):
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reward_kwargs: dict = {},
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reward_kwargs: dict = {},
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window_size=10,
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window_size=10,
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starting_point=True,
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starting_point=True,
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id: str = "baseenv-1",
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id: str = "baseenv-1", # noqa: A002
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seed: int = 1,
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seed: int = 1,
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config: dict = {},
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config: dict = {},
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live: bool = False,
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live: bool = False,
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@ -238,9 +238,9 @@ class FreqaiDataDrawer:
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metadata, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE
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metadata, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE
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)
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)
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def np_encoder(self, object):
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def np_encoder(self, obj):
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if isinstance(object, np.generic):
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if isinstance(obj, np.generic):
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return object.item()
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return obj.item()
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def get_pair_dict_info(self, pair: str) -> Tuple[str, int]:
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def get_pair_dict_info(self, pair: str) -> Tuple[str, int]:
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"""
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"""
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@ -448,8 +448,8 @@ class FreqaiDataDrawer:
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delete_dict: Dict[str, Any] = {}
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delete_dict: Dict[str, Any] = {}
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for dir in model_folders:
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for directory in model_folders:
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result = pattern.match(str(dir.name))
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result = pattern.match(str(directory.name))
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if result is None:
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if result is None:
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continue
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continue
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coin = result.group(1)
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coin = result.group(1)
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@ -458,10 +458,10 @@ class FreqaiDataDrawer:
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if coin not in delete_dict:
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if coin not in delete_dict:
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delete_dict[coin] = {}
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delete_dict[coin] = {}
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delete_dict[coin]["num_folders"] = 1
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delete_dict[coin]["num_folders"] = 1
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delete_dict[coin]["timestamps"] = {int(timestamp): dir}
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delete_dict[coin]["timestamps"] = {int(timestamp): directory}
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else:
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else:
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delete_dict[coin]["num_folders"] += 1
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delete_dict[coin]["num_folders"] += 1
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delete_dict[coin]["timestamps"][int(timestamp)] = dir
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delete_dict[coin]["timestamps"][int(timestamp)] = directory
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for coin in delete_dict:
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for coin in delete_dict:
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if delete_dict[coin]["num_folders"] > num_keep:
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if delete_dict[coin]["num_folders"] > num_keep:
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@ -612,9 +612,9 @@ class FreqaiDataDrawer:
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elif self.model_type == "pytorch":
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elif self.model_type == "pytorch":
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import torch
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import torch
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zip = torch.load(dk.data_path / f"{dk.model_filename}_model.zip")
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zipfile = torch.load(dk.data_path / f"{dk.model_filename}_model.zip")
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model = zip["pytrainer"]
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model = zipfile["pytrainer"]
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model = model.load_from_checkpoint(zip)
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model = model.load_from_checkpoint(zipfile)
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if not model:
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if not model:
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raise OperationalException(
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raise OperationalException(
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@ -134,6 +134,7 @@ extend-select = [
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"W", # pycodestyle
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"W", # pycodestyle
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"UP", # pyupgrade
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"UP", # pyupgrade
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"I", # isort
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"I", # isort
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"A", # flake8-builtins
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"TID", # flake8-tidy-imports
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"TID", # flake8-tidy-imports
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# "EXE", # flake8-executable
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# "EXE", # flake8-executable
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# "C4", # flake8-comprehensions
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# "C4", # flake8-comprehensions
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