From f7a5b2cb71b8a766cdb003877f4b5f1137d5ef56 Mon Sep 17 00:00:00 2001
From: Italo <45588475+italodamato@users.noreply.github.com>
Date: Tue, 25 Jan 2022 11:43:40 +0000
Subject: [PATCH] explicit dimensions, added **kwargs, updated docs

---
 docs/advanced-hyperopt.md                | 25 ++++++++++++++++++++++--
 freqtrade/optimize/hyperopt.py           |  2 +-
 freqtrade/optimize/hyperopt_auto.py      |  4 ++--
 freqtrade/optimize/hyperopt_interface.py |  2 +-
 4 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/docs/advanced-hyperopt.md b/docs/advanced-hyperopt.md
index 9ac31bf16..dff8dde1d 100644
--- a/docs/advanced-hyperopt.md
+++ b/docs/advanced-hyperopt.md
@@ -105,7 +105,7 @@ You can define your own estimator for Hyperopt by implementing `generate_estimat
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions, **kwargs):
             return "RF"
 
 ```
@@ -119,13 +119,34 @@ Example for `ExtraTreesRegressor` ("ET") with additional parameters:
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions, **kwargs):
             from skopt.learning import ExtraTreesRegressor
             # Corresponds to "ET" - but allows additional parameters.
             return ExtraTreesRegressor(n_estimators=100)
 
 ```
 
+The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions, **kwargs):
+            from skopt.utils import cook_estimator
+            from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
+            kernel_bounds = (0.0001, 10000)
+            kernel = (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
+            )
+            kernel += (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
+            )
+
+            return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
+```
+
 !!! 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.
diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index 209edd157..9664e6f07 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -367,7 +367,7 @@ class Hyperopt:
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
-        estimator = self.custom_hyperopt.generate_estimator(dimensions)
+        estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
 
         acq_optimizer = "sampling"
         if isinstance(estimator, str):
diff --git a/freqtrade/optimize/hyperopt_auto.py b/freqtrade/optimize/hyperopt_auto.py
index e7843ff55..5bc0af42b 100644
--- a/freqtrade/optimize/hyperopt_auto.py
+++ b/freqtrade/optimize/hyperopt_auto.py
@@ -91,5 +91,5 @@ class HyperOptAuto(IHyperOpt):
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
 
-    def generate_estimator(self, dimensions: List['Dimension']) -> EstimatorType:
-        return self._get_func('generate_estimator')(dimensions)
+    def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
+        return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)
diff --git a/freqtrade/optimize/hyperopt_interface.py b/freqtrade/optimize/hyperopt_interface.py
index 53b4f087c..1611970db 100644
--- a/freqtrade/optimize/hyperopt_interface.py
+++ b/freqtrade/optimize/hyperopt_interface.py
@@ -40,7 +40,7 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
-    def generate_estimator(self) -> EstimatorType:
+    def generate_estimator(self, dimensions, **kwargs) -> EstimatorType:
         """
         Return base_estimator.
         Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class