diff --git a/user_data/strategies/Quickie.py b/user_data/strategies/Quickie.py
index c8346b0c0..d40dffa0b 100644
--- a/user_data/strategies/Quickie.py
+++ b/user_data/strategies/Quickie.py
@@ -38,10 +38,11 @@ class Quickie(IStrategy):
 
     def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
         dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        dataframe['adx'] = ta.ADX(dataframe)
+
         dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
         dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50)
 
-        dataframe['adx'] = ta.ADX(dataframe)
 
         # required for graphing
         bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
diff --git a/user_data/strategies/Simple.py b/user_data/strategies/Simple.py
new file mode 100644
index 000000000..f76ad9ab6
--- /dev/null
+++ b/user_data/strategies/Simple.py
@@ -0,0 +1,82 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class Simple(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+
+    idea:
+        this strategy is based on the book, 'The Simple Strategy' and can be found in detail here:
+
+        https://www.amazon.com/Simple-Strategy-Powerful-Trading-Futures-ebook/dp/B00E66QPCG/ref=sr_1_1?ie=UTF8&qid=1525202675&sr=8-1&keywords=the+simple+strategy
+    """
+
+    # Minimal ROI designed for the strategy.
+    # since this strategy is planned around 5 minutes, we assume any time we have a 5% profit we should call it a day
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "0": 0.5
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.25
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
+
+        # required for graphing
+        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=12, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        dataframe.loc[
+            (
+                (
+                        (dataframe['macd'] > 0)  # over 0
+                        & (dataframe['macd'] > dataframe['macdsignal'])  # over signal
+                        & (dataframe['bb_upperband'] > dataframe['bb_upperband'].shift(1))  # pointed up
+                        & (dataframe['rsi'] > 70)  # optional filter, need to investigate
+                )
+            ),
+            'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+
+        #different strategy used for sell points, due to be able to duplicate it to 100%
+        dataframe.loc[
+            (
+                (
+                        (dataframe['adx'] > 70) &
+                        (dataframe['tema'] < dataframe['tema'].shift(1))
+                )
+            ),
+            'sell'] = 1
+        return dataframe