diff --git a/user_data/hyperopts/HO-Strategy005.py b/user_data/hyperopts/HO-Strategy005.py
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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+import numpy as np
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from pandas import DataFrame
+from typing import Dict, Any, Callable, List
+from functools import reduce
+from skopt.space import Categorical, Dimension, Integer, Real
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+__author__ = "Kevin Ossenbrueck"
+__github__ = "github.com/OtenMoten"
+__linkedin__ = "linkedin.com/in/kevin-ossenbrueck/?locale=en_US"
+__twitter__ = "twitter.com/ossenbrueck"
+__instagram__ = "instagram.com/kevin_ossenbrueck"
+__facebook__ = "facebook.com/kevin.ossenbrueck"
+__creator__ = ["github.com/xmatthias", "github.com/mishaker"]
+__credits__ = ["MontrealTradingGroup", "Udemy", "Mohsen Hassan", "Ilyass Tabiai"]
+__version__ = "3.0"
+__copyright__ = "GNU GPL"
+__status__ = "Live"
+
+"""
+I was inspired by: https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy005.py
+Therefore, I wrote this hyperopt to make it more better. Thank you xmatthias and mishaker!
+"""
+
+# Rolling volume range
+volumeAvgValueMin = 50
+volumeAvgValueMax = 300
+
+# RSI range
+rsiValueMin = 1
+rsiValueMax = 100
+
+# STOCH FAST range
+fastdValueMin = 1
+fastdValueMax = 100
+
+# MINUS DI range
+minusdiValueMin = 1
+minusdiValueMax = 100
+
+fishRsiNormaValueMin = 1
+fishRsiNormaValueMax = 100
+
+class HODobby(IHyperOpt):
+
+    """
+    If you trade on Binance then the API endopoint is "api.binance.com".
+    It's based in Tokyo. You can get a VPS in Tokyo on Vultr with 2ms latency.
+    I feel free to share my referral link (you get a bonus too):
+        > https://www.vultr.com/?ref=8806640
+    """
+    
+    ############### THIS STRATEGY IS DESIGNED FOR 5m TIMEFRAME ###############
+    
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+        # MACD
+        # tadoc.org/indicator/MACD.htm
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+
+        # MINUS DI
+        # tadoc.org/indicator/MINUS_DI.htm
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # RSI
+        # tadoc.org/indicator/RSI.htm
+        # tradingview.com/scripts/fishertransform/
+        # goo.gl/2JGGoy
+        dataframe['rsi'] = ta.RSI(dataframe)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI, values [-1.0, 1.0]
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0]
+
+        # STOCH FAST
+        # tadoc.org/indicator/STOCHF.htm
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # SAR 
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=50)
+            
+        return dataframe
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+   
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+            conditions = []
+                
+            # TRIGGER and GUARD
+            if 'buy-trigger' in params:
+                
+                conditions.append(dataframe['close'] > 0.00000200)
+                conditions.append(dataframe['volume'] > dataframe['volume'].rolling(params['volumeAVG-buy-value']).mean())
+                conditions.append(dataframe['close'] < dataframe['sma'])
+                conditions.append(dataframe['rsi'] > params['rsi-buy-value'])
+                conditions.append(dataframe['fastd'] > dataframe['fastk'])
+                conditions.append(dataframe['fastd'] > params['fastd-buy-value'])
+                conditions.append(dataframe['fisher_rsi_norma'] < params['fishRsiNorma-buy-value'])
+                
+            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]:
+        
+        buyTriggerList = ["True"]
+        
+        return [
+            Integer(volumeAvgValueMin, volumeAvgValueMax, name='volumeAVG-buy-value'),
+            Integer(rsiValueMin, rsiValueMax, name='rsi-buy-value'),
+            Integer(fastdValueMin, fastdValueMax, name='fastd-buy-value'),
+            Integer(fishRsiNormaValueMin, fishRsiNormaValueMax, name='fishRsiNorma-buy-value'),
+            Categorical(buyTriggerList, name='buy-trigger')
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+           
+            # TRIGGERS and GUARDS
+            # Solving a mistery: Which sell trigger is better? 
+            # The winner of both will be displayed in the output of the hyperopt.
+           
+            conditions = []
+                 
+            if 'sell-trigger' in params:
+                if params['sell-trigger'] == 'rsi-macd-minusdi':
+                    conditions.append(qtpylib.crossed_above(dataframe['rsi'], params['rsi-sell-value']))
+                    conditions.append(dataframe['macd'] < 0)
+                    conditions.append(dataframe['minus_di'] > params['minusdi-sell-value'])
+                    
+            if 'sell-trigger' in params:
+                if params['sell-trigger'] == 'sar-fisherRsi':
+                    conditions.append(dataframe['sar'] > dataframe['close'])
+                    conditions.append(dataframe['fisher_rsi'] > params['fishRsiNorma-sell-value'])
+                
+            if conditions:
+                dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+       
+        sellTriggerList = ["rsi-macd-minusdi", "sar-fisherRsi"]
+            
+        return [
+            Integer(rsiValueMin, rsiValueMax, name='rsi-sell-value'),
+            Integer(minusdiValueMin, minusdiValueMax, name='minusdi-sell-value'),
+            Integer(fishRsiNormaValueMin, fishRsiNormaValueMax, name='fishRsiNorma-sell-value'),
+            Categorical(sellTriggerList, name='sell-trigger')
+        ]