From 30b27ae7363563b083426bdf86136dbacf004a90 Mon Sep 17 00:00:00 2001
From: Italo <45588475+italodamato@users.noreply.github.com>
Date: Tue, 25 Jan 2022 12:29:55 +0000
Subject: [PATCH] explicit dtype

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

diff --git a/docs/advanced-hyperopt.md b/docs/advanced-hyperopt.md
index dff8dde1d..9dbb86b2d 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(dimensions, **kwargs):
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             return "RF"
 
 ```
@@ -119,7 +119,7 @@ Example for `ExtraTreesRegressor` ("ET") with additional parameters:
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator(dimensions, **kwargs):
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             from skopt.learning import ExtraTreesRegressor
             # Corresponds to "ET" - but allows additional parameters.
             return ExtraTreesRegressor(n_estimators=100)
@@ -131,7 +131,7 @@ The `dimensions` parameter is the list of `skopt.space.Dimension` objects corres
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator(dimensions, **kwargs):
+        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)
diff --git a/freqtrade/optimize/hyperopt_interface.py b/freqtrade/optimize/hyperopt_interface.py
index 1611970db..01ffd7844 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, dimensions, **kwargs) -> EstimatorType:
+    def generate_estimator(self, dimensions: List[Dimension], **kwargs) -> EstimatorType:
         """
         Return base_estimator.
         Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class