From 5a7412daffb716473696142db0acd2a061ee5e79 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 9 May 2025 07:53:41 +0000 Subject: [PATCH] Deployed 2a0dd4c to develop in en with MkDocs 1.6.1 and mike 2.1.3 --- en/develop/advanced-hyperopt/index.html | 59 ++++---- en/develop/faq/index.html | 4 +- en/develop/hyperopt/index.html | 4 +- en/develop/search/search_index.json | 2 +- en/develop/sitemap.xml | 180 ++++++++++++------------ en/develop/sitemap.xml.gz | Bin 857 -> 856 bytes 6 files changed, 122 insertions(+), 127 deletions(-) diff --git a/en/develop/advanced-hyperopt/index.html b/en/develop/advanced-hyperopt/index.html index cf82807ad..5650d6a25 100644 --- a/en/develop/advanced-hyperopt/index.html +++ b/en/develop/advanced-hyperopt/index.html @@ -1921,45 +1921,42 @@ For the sample below, you then need to add the command line parameter --hy

Parameters created this way will not show up in the list-strategies parameter count.

Overriding Base estimator

-

You can define your own estimator for Hyperopt by implementing generate_estimator() in the Hyperopt subclass.

+

You can define your own optuna sampler for Hyperopt by implementing generate_estimator() in the Hyperopt subclass.

class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
         def generate_estimator(dimensions: List['Dimension'], **kwargs):
-            return "RF"
-
-

Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the scikit-optimize documentation), 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:

-
class MyAwesomeStrategy(IStrategy):
-    class HyperOpt:
-        def generate_estimator(dimensions: List['Dimension'], **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:

-
class MyAwesomeStrategy(IStrategy):
-    class HyperOpt:
-        def generate_estimator(dimensions: List['Dimension'], **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)
+            return "NSGAIIISampler"
+

Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "CmaEsSampler", "NSGAIIISampler", "QMCSampler" (Details can be found in the optuna-samplers documentation), or "an instance of a class that inherits from optuna.samplers.BaseSampler".

+

Some research will be necessary to find additional Samplers (from optunahub) for example.

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.

+If you're unsure about this, best use one of the Defaults ("NSGAIIISampler" has proven to be the most versatile) without further parameters.

+
+Using AutoSampler from Optunahub +

AutoSampler docs

+

Install the necessary dependencies +

pip install optunahub cmaes torch scipy
+
+Implement generate_estimator() in your strategy

+
# ...
+from freqtrade.strategy.interface import IStrategy
+from typing import List
+import optunahub
+# ... 
+
+class my_strategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List["Dimension"], **kwargs):
+            if "random_state" in kwargs.keys():
+                return optunahub.load_module("samplers/auto_sampler").AutoSampler(seed=kwargs["random_state"])
+            else:
+                return optunahub.load_module("samplers/auto_sampler").AutoSampler()
+
+

Obviously the same approach will work for all other Samplers optuna supports.

+

Space options

For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types: