diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json
index 12eb30128..9494ba0e1 100644
--- a/config_examples/config_freqai.example.json
+++ b/config_examples/config_freqai.example.json
@@ -77,7 +77,8 @@
             "indicator_periods_candles": [
                 10,
                 20
-            ]
+            ],
+            "plot_feature_importance": true
         },
         "data_split_parameters": {
             "test_size": 0.33,
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 78931bed4..0cc51fdab 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -20,6 +20,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.freqai.data_drawer import FreqaiDataDrawer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.utils import plot_feature_importance
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -555,6 +556,14 @@ class IFreqaiModel(ABC):
 
         model = self.train(unfiltered_dataframe, pair, dk)
 
+        if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
+            plot_feature_importance(
+                model=model,
+                feature_names=dk.training_features_list,
+                pair=pair,
+                train_dir=dk.data_path
+            )
+
         self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
         dk.set_new_model_names(pair, new_trained_timerange)
         self.dd.pair_dict[pair]["first"] = False
diff --git a/freqtrade/freqai/utils.py b/freqtrade/freqai/utils.py
index 6a70f050f..86d89d4b0 100644
--- a/freqtrade/freqai/utils.py
+++ b/freqtrade/freqai/utils.py
@@ -1,5 +1,13 @@
 import logging
 from datetime import datetime, timezone
+# for plot_feature_importance
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.io as pio
+from plotly.subplots import make_subplots
 
 from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
@@ -132,3 +140,66 @@ def get_required_data_timerange(
 #             trading_mode=config.get("trading_mode", "spot"),
 #             prepend=config.get("prepend_data", False),
 #         )
+
+
+def plot_feature_importance(model, feature_names, pair, train_dir, count_max=25) -> None:
+    """
+        Plot Best and Worst Features by importance for CatBoost model.
+        Called once per sub-train.
+
+        Required: pip install kaleido
+
+        Usage: plot_feature_importance(
+            model=model,
+            feature_names=dk.training_features_list,
+            pair=pair,
+            train_dir=dk.data_path)
+    """
+
+    # Gather feature importance from model
+    if "catboost.core" in str(model.__class__):
+        fi = model.get_feature_importance()
+
+    elif "lightgbm.sklearn" in str(model.__class__):
+        fi = model.feature_importances_
+
+    else:
+        raise NotImplementedError(f"Cannot extract feature importance for {model.__class__}")
+
+    # Data preparation
+    fi_df = pd.DataFrame({
+        "feature_names": np.array(feature_names),
+        "feature_importance": np.array(fi)
+    })
+    fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
+    fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
+
+    # Plotting
+    fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
+    fig.add_trace(
+        go.Bar(
+            x=fi_df_top["feature_importance"],
+            y=fi_df_top["feature_names"],
+            orientation='h', showlegend=False
+        ), row=1, col=1
+    )
+    fig.add_trace(
+        go.Bar(
+            x=fi_df_worst["feature_importance"],
+            y=fi_df_worst["feature_names"],
+            orientation='h', showlegend=False
+        ), row=1, col=2
+    )
+    fig.update_layout(
+        title_text=f"Best and Worst Features {pair}",
+        width=1000, height=600
+    )
+
+    # Create directory and save image
+    model_dir, train_name = str(train_dir).rsplit("/", 1)
+    fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}")
+    fi_dir.mkdir(parents=True, exist_ok=True)
+
+    pio.write_image(fig, f"{fi_dir}/{train_name}.png", format="png")
+
+    logger.info(f"Freqai saving feature importance plot {fi_dir}/{train_name}.png")
diff --git a/requirements-plot.txt b/requirements-plot.txt
index 80cd3f4f2..ef3cf9f24 100644
--- a/requirements-plot.txt
+++ b/requirements-plot.txt
@@ -2,3 +2,4 @@
 -r requirements.txt
 
 plotly==5.10.0
+kaleido==0.2.1