diff --git a/user_data/hyperopts/BinHV45HyperOpt.py b/user_data/hyperopts/BinHV45HyperOpt.py
new file mode 100644
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+++ b/user_data/hyperopts/BinHV45HyperOpt.py
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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class BinHV45HyperOpt(IHyperOpt):
+    """
+    Hyperopt file for optimizing BinHV45Strategy.
+    Uses ranges to find best parameter combination for bbdelta, closedelta and tail
+    of the buy strategy.
+
+    Sell strategy is ignored, because it's ignored in BinHV45Strategy as well.
+    This strategy therefor works without explicit sell signal therefor hyperopting
+    for 'roi' is recommend as well
+
+    Also, this is just ONE way to optimize this strategy - others might also include
+    disabling certain conditions completely. This file is just a starting point, feel free
+    to improve and PR.
+    """
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            conditions.append(dataframe['lower'].shift().gt(0))
+            conditions.append(dataframe['bbdelta'].gt(
+                dataframe['close'] * params['bbdelta'] / 1000))
+            conditions.append(dataframe['closedelta'].gt(
+                dataframe['close'] * params['closedelta'] / 1000))
+            conditions.append(dataframe['tail'].lt(dataframe['bbdelta'] * params['tail'] / 1000))
+            conditions.append(dataframe['close'].lt(dataframe['lower'].shift()))
+            conditions.append(dataframe['close'].le(dataframe['close'].shift()))
+
+            # Check that the candle had volume
+            conditions.append(dataframe['volume'] > 0)
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            Integer(1, 15, name='bbdelta'),
+            Integer(15, 20, name='closedelta'),
+            Integer(20, 30, name='tail'),
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            no sell signal
+            """
+            dataframe['sell'] = 0
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return []