IHyperopt: all methods static, somef ixes for mypy

freqtrade/optimize/default_hyperopt.py

@@ -36,7 +36,8 @@ class DefaultHyperOpts(IHyperOpt):
         dataframe['sar'] = ta.SAR(dataframe)
         return dataframe
 
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
         """
         Define the buy strategy parameters to be used by hyperopt
         """

freqtrade/optimize/hyperopt_interface.py

@@ -4,9 +4,10 @@ This module defines the interface to apply for hyperopts
 """
 
 from abc import ABC, abstractmethod
-from typing import Dict, Any, Callable
+from typing import Dict, Any, Callable, List
 
 from pandas import DataFrame
+from skopt.space import Dimension
 
 
 class IHyperOpt(ABC):
@@ -20,40 +21,46 @@ class IHyperOpt(ABC):
         ticker_interval -> int: value of the ticker interval to use for the strategy
     """
 
+    @staticmethod
     @abstractmethod
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Populate indicators that will be used in the Buy and Sell strategy
         :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
         :return: a Dataframe with all mandatory indicators for the strategies
         """
 
+    @staticmethod
     @abstractmethod
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
         """
         Create a buy strategy generator
         """
 
+    @staticmethod
     @abstractmethod
-    def indicator_space(self) -> Dict[str, Any]:
+    def indicator_space() -> List[Dimension]:
         """
         Create an indicator space
         """
 
+    @staticmethod
     @abstractmethod
-    def generate_roi_table(self, params: Dict) -> Dict[int, float]:
+    def generate_roi_table(params: Dict) -> Dict[int, float]:
         """
         Create an roi table
         """
 
+    @staticmethod
     @abstractmethod
-    def stoploss_space(self) -> Dict[str, Any]:
+    def stoploss_space() -> List[Dimension]:
         """
         Create a stoploss space
         """
 
+    @staticmethod
     @abstractmethod
-    def roi_space(self) -> Dict[str, Any]:
+    def roi_space() -> List[Dimension]:
         """
         Create a roi space
         """

freqtrade/optimize/hyperopt_resolver.py

@@ -35,7 +35,7 @@ class HyperOptResolver(object):
         self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
 
     def _load_hyperopt(
-            self, hyperopt_name: str, extra_dir: Optional[str] = None) -> Optional[IHyperOpt]:
+            self, hyperopt_name: str, extra_dir: Optional[str] = None) -> IHyperOpt:
         """
         Search and loads the specified hyperopt.
         :param hyperopt_name: name of the module to import
@@ -75,7 +75,7 @@ class HyperOptResolver(object):
         # Generate spec based on absolute path
         spec = importlib.util.spec_from_file_location('user_data.hyperopts', module_path)
         module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
+        spec.loader.exec_module(module)  # type: ignore # importlib does not use typehints
 
         valid_hyperopts_gen = (
             obj for name, obj in inspect.getmembers(module, inspect.isclass)

user_data/hyperopts/sample_hyperopt.py

@@ -45,7 +45,8 @@ class SampleHyperOpts(IHyperOpt):
         dataframe['sar'] = ta.SAR(dataframe)
         return dataframe
 
-    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
         """
         Define the buy strategy parameters to be used by hyperopt
         """