diff --git a/docs/hyperopt.md b/docs/hyperopt.md
index fdde74fb6..b8c3cc7ef 100644
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@@ -1,7 +1,7 @@
 # Hyperopt
 
 This page explains how to tune your strategy by finding the optimal
-parameters, a process called hyperparameter optimization. The bot uses algorithms included in the `scikit-optimize` package to accomplish this.
+parameters, a process called hyperparameter optimization. The bot uses algorithms included in the `optuna` package to accomplish this.
 The search will burn all your CPU cores, make your laptop sound like a fighter jet and still take a long time.
 
 In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses one of optuna's sampler algorithms (currently NSGAIIISampler) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
diff --git a/pyproject.toml b/pyproject.toml
index 8ee9818b8..bf40c456e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -79,7 +79,6 @@ plot = ["plotly>=4.0"]
 hyperopt = [
   "scipy",
   "scikit-learn",
-  "ft-scikit-optimize>=0.9.2",
   "filelock",
 ]
 freqai = [
diff --git a/requirements-hyperopt.txt b/requirements-hyperopt.txt
index cb98d6bb7..58a9d1b83 100644
--- a/requirements-hyperopt.txt
+++ b/requirements-hyperopt.txt
@@ -4,7 +4,6 @@
 # Required for hyperopt
 scipy==1.15.2
 scikit-learn==1.6.1
-ft-scikit-optimize==0.9.2
 filelock==3.18.0
 optuna==4.2.1
 cmaes==0.11.1