diff --git a/user_data/strategies/default_strategy.py b/user_data/strategies/default_strategy.py
new file mode 100644
index 000000000..acedfae01
--- /dev/null
+++ b/user_data/strategies/default_strategy.py
@@ -0,0 +1,338 @@
+
+# --- 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
+# --------------------------------
+
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy # noqa
+
+import random
+
+# Update this variable if you change the class name
+class_name = 'DefaultStrategy'
+
+
+# This class is a sample. Feel free to customize it.
+
+
+
+
+def Select():
+    param = []
+    random_items = []
+    param.append(str('[' + 'uptrend_long_ema' + '[' + 'enabled' + ']'))
+    param.append(str('[' + 'macd_below_zero' +  '][' + 'enabled' + ']'))
+    param.append(str('[' + 'uptrend_short_ema' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'mfi' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'fastd' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'adx' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'rsi' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'over_sar' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'green_candle' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'uptrend_sma' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'closebb' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'temabb' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'fastdt' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'ao' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'ema3' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'macd' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'closesar' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'htsine' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'has' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'plusdi' '][' + 'enabled'+ ']'))
+    howmany = random.randint(1,20)
+    random_items = random.choices(population=param, k=howmany)
+    print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
+    print('The Parameters Enabled Are As Follows!!!:  ' + str(random_items))
+    print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
+    return random_items
+
+
+
+
+class DefaultStrategy(IStrategy):
+    """
+    This is a test strategy to inspire you.
+    More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
+
+    You can:
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
+    populate_sell_trend, hyperopt_space, buy_strategy_generator
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    ticker_interval = 5
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.10
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+
+        # Awesome oscillator
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        dataframe['cci'] = ta.CCI(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # 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)
+
+        # 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'] = (numpy.exp(2 * rsi) - 1) / (numpy.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
+        # ------------------------------------
+
+        """
+        # Previous Bollinger bands
+        # Because ta.BBANDS implementation is broken with small numbers, it actually
+        # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
+        # and use middle band instead.
+
+        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+        """
+
+        # 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']
+
+
+        # 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['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+
+        # TEMA - Triple Exponential Moving Average
+        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']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+
+        # Chart type
+        # ------------------------------------
+
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+
+
+        return dataframe
+
+    params = Select()
+    valm = random.randint(1,100)
+    valfast = random.randint(1,100)
+    valadx = random.randint(1,100)
+    valrsi = random.randint(1,100)
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+
+        conditions = []
+        # GUARDS AND TRENDS
+        if 'uptrend_long_ema' in str(self.params):
+            conditions.append(dataframe['ema50'] > dataframe['ema100'])
+        if 'macd_below_zero' in str(self.params):
+            conditions.append(dataframe['macd'] < 0)
+        if 'uptrend_short_ema' in str(self.params):
+            conditions.append(dataframe['ema5'] > dataframe['ema10'])
+        if 'mfi' in str(self.params):
+            print('MFI Value  :' + str(self.valm))
+            conditions.append(dataframe['mfi'] < self.valm)
+        if 'fastd' in str(self.params):
+            print('FASTD Value  :' + str(self.valfast))
+            conditions.append(dataframe['fastd'] < self.valfast)
+        if 'adx' in str(self.params):
+            print('ADX Value  :' + str(self.valadx))
+            conditions.append(dataframe['adx'] > self.valadx)
+        if 'rsi' in str(self.params):
+            print('RSI Value  :' + str(self.valrsi))
+            conditions.append(dataframe['rsi'] < self.valrsi)
+        if 'over_sar' in str(self.params):
+            conditions.append(dataframe['close'] > dataframe['sar'])
+        if 'green_candle' in str(self.params):
+            conditions.append(dataframe['close'] > dataframe['open'])
+        if 'uptrend_sma' in str(self.params):
+            prevsma = dataframe['sma'].shift(1)
+            conditions.append(dataframe['sma'] > prevsma)
+        if 'closebb' in str(self.params):
+            conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+        if 'temabb' in str(self.params):
+            conditions.append(dataframe['tema'] < dataframe['bb_lowerband'])
+        if 'fastdt' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['fastd'], 10.0))
+        if 'ao' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['ao'], 0.0))
+        if 'ema3' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema10']))
+        if 'macd' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
+        if 'closesar' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['close'], dataframe['sar']))
+        if 'htsine' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine']))
+        if 'has' in str(self.params):
+            conditions.append((qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & (dataframe['ha_low'] == dataframe['ha_open']))
+        if 'plusdi' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['plus_di'], dataframe['minus_di']))
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+            ),
+            'sell'] = 1
+        return dataframe
+