Merge pull request #4596 from rokups/rk/hyper-strategy

Support for creating auto-hyperoptable strategies.

freqtrade/optimize/hyperopt.py

@@ -26,6 +26,7 @@ from freqtrade.data.history import get_timerange
 from freqtrade.misc import file_dump_json, plural
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
+from freqtrade.optimize.hyperopt_auto import HyperOptAuto
 from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
@@ -61,14 +62,18 @@ class Hyperopt:
     hyperopt = Hyperopt(config)
     hyperopt.start()
     """
+    custom_hyperopt: IHyperOpt
 
     def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
         self.backtesting = Backtesting(self.config)
 
-        self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
-        self.custom_hyperopt.__class__.strategy = self.backtesting.strategy
+        if not self.config.get('hyperopt'):
+            self.custom_hyperopt = HyperOptAuto(self.config)
+        else:
+            self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
+        self.custom_hyperopt.strategy = self.backtesting.strategy
 
         self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function

freqtrade/optimize/hyperopt_auto.py

@@ -0,0 +1,89 @@
+"""
+HyperOptAuto class.
+This module implements a convenience auto-hyperopt class, which can be used together with strategies
+ that implement IHyperStrategy interface.
+"""
+from contextlib import suppress
+from typing import Any, Callable, Dict, List
+
+from pandas import DataFrame
+
+
+with suppress(ImportError):
+    from skopt.space import Dimension
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+
+class HyperOptAuto(IHyperOpt):
+    """
+    This class delegates functionality to Strategy(IHyperStrategy) and Strategy.HyperOpt classes.
+     Most of the time Strategy.HyperOpt class would only implement indicator_space and
+     sell_indicator_space methods, but other hyperopt methods can be overridden as well.
+    """
+
+    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict):
+            for attr_name, attr in self.strategy.enumerate_parameters('buy'):
+                if attr.optimize:
+                    # noinspection PyProtectedMember
+                    attr._set_value(params[attr_name])
+            return self.strategy.populate_buy_trend(dataframe, metadata)
+
+        return populate_buy_trend
+
+    def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict):
+            for attr_name, attr in self.strategy.enumerate_parameters('sell'):
+                if attr.optimize:
+                    # noinspection PyProtectedMember
+                    attr._set_value(params[attr_name])
+            return self.strategy.populate_sell_trend(dataframe, metadata)
+
+        return populate_buy_trend
+
+    def _get_func(self, name) -> Callable:
+        """
+        Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
+        :param name: function name.
+        :return: a requested function.
+        """
+        hyperopt_cls = getattr(self.strategy, 'HyperOpt', None)
+        default_func = getattr(super(), name)
+        if hyperopt_cls:
+            return getattr(hyperopt_cls, name, default_func)
+        else:
+            return default_func
+
+    def _generate_indicator_space(self, category):
+        for attr_name, attr in self.strategy.enumerate_parameters(category):
+            if attr.optimize:
+                yield attr.get_space(attr_name)
+
+    def _get_indicator_space(self, category, fallback_method_name):
+        indicator_space = list(self._generate_indicator_space(category))
+        if len(indicator_space) > 0:
+            return indicator_space
+        else:
+            return self._get_func(fallback_method_name)()
+
+    def indicator_space(self) -> List['Dimension']:
+        return self._get_indicator_space('buy', 'indicator_space')
+
+    def sell_indicator_space(self) -> List['Dimension']:
+        return self._get_indicator_space('sell', 'sell_indicator_space')
+
+    def generate_roi_table(self, params: Dict) -> Dict[int, float]:
+        return self._get_func('generate_roi_table')(params)
+
+    def roi_space(self) -> List['Dimension']:
+        return self._get_func('roi_space')()
+
+    def stoploss_space(self) -> List['Dimension']:
+        return self._get_func('stoploss_space')()
+
+    def generate_trailing_params(self, params: Dict) -> Dict:
+        return self._get_func('generate_trailing_params')(params)
+
+    def trailing_space(self) -> List['Dimension']:
+        return self._get_func('trailing_space')()

freqtrade/optimize/hyperopt_interface.py

@@ -44,36 +44,31 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
-    @staticmethod
-    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
         """
         Create a buy strategy generator.
         """
         raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
 
-    @staticmethod
-    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+    def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
         """
         Create a sell strategy generator.
         """
         raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
 
-    @staticmethod
-    def indicator_space() -> List[Dimension]:
+    def indicator_space(self) -> List[Dimension]:
         """
         Create an indicator space.
         """
         raise OperationalException(_format_exception_message('indicator_space', 'buy'))
 
-    @staticmethod
-    def sell_indicator_space() -> List[Dimension]:
+    def sell_indicator_space(self) -> List[Dimension]:
         """
         Create a sell indicator space.
         """
         raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
 
-    @staticmethod
-    def generate_roi_table(params: Dict) -> Dict[int, float]:
+    def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         """
         Create a ROI table.
 
@@ -88,8 +83,7 @@ class IHyperOpt(ABC):
 
         return roi_table
 
-    @staticmethod
-    def roi_space() -> List[Dimension]:
+    def roi_space(self) -> List[Dimension]:
         """
         Create a ROI space.
 
@@ -109,7 +103,7 @@ class IHyperOpt(ABC):
         roi_t_alpha = 1.0
         roi_p_alpha = 1.0
 
-        timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
+        timeframe_min = timeframe_to_minutes(self.timeframe)
 
         # We define here limits for the ROI space parameters automagically adapted to the
         # timeframe used by the bot:
@@ -145,7 +139,7 @@ class IHyperOpt(ABC):
             'roi_p2': roi_limits['roi_p2_min'],
             'roi_p3': roi_limits['roi_p3_min'],
         }
-        logger.info(f"Min roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
+        logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 5)}")
         p = {
             'roi_t1': roi_limits['roi_t1_max'],
             'roi_t2': roi_limits['roi_t2_max'],
@@ -154,7 +148,7 @@ class IHyperOpt(ABC):
             'roi_p2': roi_limits['roi_p2_max'],
             'roi_p3': roi_limits['roi_p3_max'],
         }
-        logger.info(f"Max roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
+        logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 5)}")
 
         return [
             Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
@@ -165,8 +159,7 @@ class IHyperOpt(ABC):
             Real(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], name='roi_p3'),
         ]
 
-    @staticmethod
-    def stoploss_space() -> List[Dimension]:
+    def stoploss_space(self) -> List[Dimension]:
         """
         Create a stoploss space.
 
@@ -177,8 +170,7 @@ class IHyperOpt(ABC):
             Real(-0.35, -0.02, name='stoploss'),
         ]
 
-    @staticmethod
-    def generate_trailing_params(params: Dict) -> Dict:
+    def generate_trailing_params(self, params: Dict) -> Dict:
         """
         Create dict with trailing stop parameters.
         """
@@ -190,8 +182,7 @@ class IHyperOpt(ABC):
             'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
         }
 
-    @staticmethod
-    def trailing_space() -> List[Dimension]:
+    def trailing_space(self) -> List[Dimension]:
         """
         Create a trailing stoploss space.