Add possibility to override estimator from within hyperopt

docs/advanced-hyperopt.md

@@ -98,6 +98,38 @@ class MyAwesomeStrategy(IStrategy):
 !!! Note
     All overrides are optional and can be mixed/matched as necessary.
 
+### Overriding Base estimator
+
+You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator():
+            return "RF"
+
+```
+
+Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the [scikit-optimize documentation](https://scikit-optimize.github.io/)), or "an instance of a class that inherits from `RegressorMixin` (from sklearn) and where the `predict` method has an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`".
+
+Some research will be necessary to find additional Regressors.
+
+Example for `ExtraTreesRegressor` ("ET") with additional parameters:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator():
+            from skopt.learning import ExtraTreesRegressor
+            # Corresponds to "ET" - but allows additional parameters.
+            return ExtraTreesRegressor(n_estimators=100)
+
+```
+
+!!! Note
+    While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
+    If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
+
 ## Space options
 
 For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:

freqtrade/optimize/hyperopt.py

@@ -365,10 +365,14 @@ def _get_results_dict(self, backtesting_results, min_date, max_date,
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
+        estimator = self.custom_hyperopt.generate_estimator()
+        logger.info(f"Using estimator {estimator}.")
+        # TODO: Impact of changing acq_optimizer to "sampling" is unclear
+        # (other than that it fails with other optimizers when using custom sklearn regressors)
         return Optimizer(
             dimensions,
-            base_estimator="ET",
-            acq_optimizer="auto",
+            base_estimator=estimator,
+            acq_optimizer="sampling",
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,

freqtrade/optimize/hyperopt_auto.py

@@ -12,7 +12,7 @@
 with suppress(ImportError):
     from skopt.space import Dimension
 
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
+from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
 
 
 def _format_exception_message(space: str) -> str:
@@ -79,3 +79,6 @@ def generate_trailing_params(self, params: Dict) -> Dict:
 
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
+
+    def generate_estimator(self) -> EstimatorType:
+        return self._get_func('generate_estimator')()

freqtrade/optimize/hyperopt_interface.py

@@ -5,8 +5,9 @@
 import logging
 import math
 from abc import ABC
-from typing import Dict, List
+from typing import Dict, List, Union
 
+from sklearn.base import RegressorMixin
 from skopt.space import Categorical, Dimension, Integer
 
 from freqtrade.exchange import timeframe_to_minutes
@@ -17,6 +18,8 @@
 
 logger = logging.getLogger(__name__)
 
+EstimatorType = Union[RegressorMixin, str]
+
 
 class IHyperOpt(ABC):
     """
@@ -37,6 +40,14 @@ def __init__(self, config: dict) -> None:
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
+    def generate_estimator(self) -> EstimatorType:
+        """
+        Return base_estimator.
+        Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
+        inheriting from RegressorMixin (from sklearn).
+        """
+        return 'ET'
+
     def generate_roi_table(self, params: Dict) -> Dict[int, float]:
         """
         Create a ROI table.