diff --git a/freqtrade/templates/sample_strategy.py b/freqtrade/templates/sample_strategy.py
index 92f6aefba..8a4b27c72 100644
--- a/freqtrade/templates/sample_strategy.py
+++ b/freqtrade/templates/sample_strategy.py
@@ -124,11 +124,16 @@ class SampleStrategy(IStrategy):
         # Momentum Indicators
         # ------------------------------------
 
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-
         # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
+        # dataframe['adx'] = ta.ADX(dataframe)
+
+        # # Plus Directional Indicator / Movement
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # # Minus Directional Indicator / Movement
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
 
         # # Aroon, Aroon Oscillator
         # aroon = ta.AROON(dataframe)
@@ -136,12 +141,53 @@ class SampleStrategy(IStrategy):
         # dataframe['aroondown'] = aroon['aroondown']
         # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
 
-        # # Awesome oscillator
+        # # Awesome Oscillator
         # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
 
-        # # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        # # Keltner Channel
+        # keltner = qtpylib.keltner_channel(dataframe)
+        # dataframe["kc_upperband"] = keltner["upper"]
+        # dataframe["kc_lowerband"] = keltner["lower"]
+        # dataframe["kc_middleband"] = keltner["mid"]
+        # dataframe["kc_percent"] = (
+        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
+        # )
+        # dataframe["kc_width"] = (
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
+        # )
+
+        # # Ultimate Oscillator
+        # dataframe['uo'] = ta.ULTOSC(dataframe)
+
+        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
         # dataframe['cci'] = ta.CCI(dataframe)
 
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # # Stochastic Slow
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
+
+        # Stochastic Fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # # Stochastic RSI
+        # stoch_rsi = ta.STOCHRSI(dataframe)
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
         # MACD
         macd = ta.MACD(dataframe)
         dataframe['macd'] = macd['macd']
@@ -151,71 +197,69 @@ class SampleStrategy(IStrategy):
         # MFI
         dataframe['mfi'] = ta.MFI(dataframe)
 
-        # # Minus Directional Indicator / Movement
-        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # # Plus Directional Indicator / Movement
-        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
         # # ROC
         # dataframe['roc'] = ta.ROC(dataframe)
 
-        # # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-        # rsi = 0.1 * (dataframe['rsi'] - 50)
-        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
-
-        # # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
-
-        # # Stoch
-        # stoch = ta.STOCH(dataframe)
-        # dataframe['slowd'] = stoch['slowd']
-        # dataframe['slowk'] = stoch['slowk']
-
-        # Stoch fast
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['fastk'] = stoch_fast['fastk']
-
-        # # Stoch RSI
-        # stoch_rsi = ta.STOCHRSI(dataframe)
-        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
-        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
-
         # Overlap Studies
         # ------------------------------------
 
-        # Bollinger bands
+        # Bollinger Bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
         dataframe['bb_lowerband'] = bollinger['lower']
         dataframe['bb_middleband'] = bollinger['mid']
         dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        )
+
+        # Bollinger Bands - Weighted (EMA based instead of SMA)
+        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
+        #     qtpylib.typical_price(dataframe), window=20, stds=2
+        # )
+        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+        # dataframe["wbb_percent"] = (
+        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+        # )
+        # dataframe["wbb_width"] = (
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
+        #     dataframe["wbb_middleband"]
+        # )
 
         # # EMA - Exponential Moving Average
         # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
         # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
         # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
         # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
         # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
 
         # # SMA - Simple Moving Average
-        # dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
+        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
+        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
+        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
+        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
+        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
 
-        # SAR Parabol
-        dataframe['sar'] = ta.SAR(dataframe)
+        # Parabolic SAR
+        # dataframe['sar'] = ta.SAR(dataframe)
 
         # TEMA - Triple Exponential Moving Average
-        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        # dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
 
         # Cycle Indicator
         # ------------------------------------
         # Hilbert Transform Indicator - SineWave
-        hilbert = ta.HT_SINE(dataframe)
-        dataframe['htsine'] = hilbert['sine']
-        dataframe['htleadsine'] = hilbert['leadsine']
+        # hilbert = ta.HT_SINE(dataframe)
+        # dataframe['htsine'] = hilbert['sine']
+        # dataframe['htleadsine'] = hilbert['leadsine']
 
         # Pattern Recognition - Bullish candlestick patterns
         # ------------------------------------
@@ -264,7 +308,7 @@ class SampleStrategy(IStrategy):
 
         # # Chart type
         # # ------------------------------------
-        # # Heikinashi stategy
+        # # Heikin Ashi Strategy
         # heikinashi = qtpylib.heikinashi(dataframe)
         # dataframe['ha_open'] = heikinashi['open']
         # dataframe['ha_close'] = heikinashi['close']