Optional support for defining hyperopt parameters in a strategy file and reusing common hyperopt/strategy parts.

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
@@ -67,8 +68,11 @@ class Hyperopt:
 
         self.backtesting = Backtesting(self.config)
 
+        if self.config['hyperopt'] == 'HyperOptAuto':
+            self.custom_hyperopt = HyperOptAuto(self.config)
+        else:
             self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
-        self.custom_hyperopt.__class__.strategy = self.backtesting.strategy
+        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 +1,83 @@
+"""
+HyperOptAuto class.
+This module implements a convenience auto-hyperopt class, which can be used together with strategies that implement
+IHyperStrategy interface.
+"""
+from typing import Any, Callable, Dict, List
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+
+# noinspection PyUnresolvedReferences
+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:
+        assert hasattr(self.strategy, 'enumerate_parameters'), 'Strategy must inherit from IHyperStrategy.'
+
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict):
+            for attr_name, attr in self.strategy.enumerate_parameters('buy'):
+                attr.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:
+        assert hasattr(self.strategy, 'enumerate_parameters'), 'Strategy must inherit from IHyperStrategy.'
+
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict):
+            for attr_name, attr in self.strategy.enumerate_parameters('sell'):
+                attr.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')
+        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):
+        assert hasattr(self.strategy, 'enumerate_parameters'), 'Strategy must inherit from IHyperStrategy.'
+
+        for attr_name, attr in self.strategy.enumerate_parameters(category):
+            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/strategy/__init__.py

@@ -2,4 +2,5 @@
 from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
                                 timeframe_to_prev_date, timeframe_to_seconds)
 from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.hyper import IHyperStrategy, Parameter
 from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open

freqtrade/strategy/hyper.py

@@ -0,0 +1,93 @@
+"""
+IHyperStrategy interface, hyperoptable Parameter class.
+This module defines a base class for auto-hyperoptable strategies.
+"""
+from abc import ABC
+from typing import Union, List, Iterator, Tuple
+
+from skopt.space import Integer, Real, Categorical
+
+from freqtrade.strategy.interface import IStrategy
+
+
+class Parameter(object):
+    """
+    Defines a parameter that can be optimized by hyperopt.
+    """
+    default: Union[int, float, str, bool]
+    space: List[Union[int, float, str, bool]]
+    category: str
+
+    def __init__(self, *, space: List[Union[int, float, str, bool]], default: Union[int, float, str, bool] = None,
+                 category: str = None, **kwargs):
+        """
+        Initialize hyperopt-optimizable parameter.
+        :param space: Optimization space. [min, max] for ints and floats or a list of strings for categorial parameters.
+        :param default: A default value. Required for ints and floats, optional for categorial parameters (first item
+         from the space will be used). Type of default value determines skopt space used for optimization.
+        :param category: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if parameter field
+         name is prefixed with 'buy_' or 'sell_'.
+        :param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
+        """
+        assert 'name' not in kwargs, 'Name is determined by parameter field name and can not be specified manually.'
+        self.value = default
+        self.space = space
+        self.category = category
+        self._space_params = kwargs
+        if default is None:
+            assert len(space) > 0
+            self.value = space[0]
+
+    def get_space(self, name: str) -> Union[Integer, Real, Categorical, None]:
+        """
+        Create skopt optimization space.
+        :param name: A name of parameter field.
+        :return: skopt space of this parameter, or None if parameter is not optimizable (i.e. space is set to None)
+        """
+        if not self.space:
+            return None
+        if isinstance(self.value, int):
+            assert len(self.space) == 2
+            return Integer(*self.space, name=name, **self._space_params)
+        if isinstance(self.value, float):
+            assert len(self.space) == 2
+            return Real(*self.space, name=name, **self._space_params)
+
+        assert len(self.space) > 0
+        return Categorical(self.space, name=name, **self._space_params)
+
+
+class IHyperStrategy(IStrategy, ABC):
+    """
+    A helper base class which allows HyperOptAuto class to reuse implementations of of buy/sell strategy logic.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self._load_params(getattr(self, 'buy_params', None))
+        self._load_params(getattr(self, 'sell_params', None))
+
+    def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, Parameter]]:
+        """
+        Find all optimizeable parameters and return (name, attr) iterator.
+        :param category:
+        :return:
+        """
+        assert category in ('buy', 'sell', None)
+        for attr_name in dir(self):
+            if not attr_name.startswith('__'):  # Ignore internals, not strictly necessary.
+                attr = getattr(self, attr_name)
+                if isinstance(attr, Parameter):
+                    if category is None or category == attr.category or attr_name.startswith(category + '_'):
+                        yield attr_name, attr
+
+    def _load_params(self, params: dict) -> None:
+        """
+        Set optimizeable parameter values.
+        :param params: Dictionary with new parameter values.
+        """
+        if not params:
+            return
+        for attr_name, attr in self.enumerate_parameters():
+            if attr_name in params:
+                attr.value = params[attr_name]