diff --git a/user_data/strategies/1. defaultBB +6609%.py b/user_data/strategies/1. defaultBB +6609%.py
new file mode 100644
index 000000000..474776834
--- /dev/null
+++ b/user_data/strategies/1. defaultBB +6609%.py	
@@ -0,0 +1,346 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class defaultB(IStrategy):
+    """
+    This is a strategy template to get you started.
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - 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
+    """
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        "4320": 0.0018,
+        #"300": 0.01,
+        #"60": 0.04, # 0.01
+        #"30": 0.06, # 0.02
+        "0": 0.30 # 0.04
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.0521 # -0.05
+
+    # Trailing stoploss
+    trailing_stop = True
+    trailing_only_offset_is_reached = True
+
+    # trailing_stop_positive = 0.005
+    trailing_stop_positive = 0.0005
+    trailing_stop_positive_offset = 0.034
+
+    # Optimal timeframe for the strategy.
+    timeframe = '30m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values canconfig222 be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = False
+    sell_profit_only = True
+    ignore_roi_if_buy_signal = True
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 200
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    plot_config = {
+        # Main plot indicators (Moving averages, ...)
+        'main_plot': {
+            'tema': {},
+            'sar': {'color': 'white'},
+        },
+        'subplots': {
+            # Subplots - each dict defines one additional plot
+            "MACD": {
+                'macd': {'color': 'blue'},
+                'macdsignal': {'color': 'orange'},
+            },
+            "RSI": {
+                'rsi': {'color': 'red'},
+            }
+        }
+    }
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicators
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe['adx50'] = ta.ADX(dataframe, timeperiod=50)
+
+
+        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
+        dataframe['cci'] = ta.CCI(dataframe)
+        dataframe['cmo'] = ta.CMO(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+        dataframe['rsi25'] = ta.RSI(dataframe, timeperiod=25)
+        dataframe['rsi50'] = ta.RSI(dataframe, timeperiod=50)
+        dataframe['rsi100'] = ta.RSI(dataframe, timeperiod=100)
+        dataframe['rsi200'] = ta.RSI(dataframe, timeperiod=200)
+        dataframe['rsi400'] = ta.RSI(dataframe, timeperiod=400)
+        dataframe['rsi1000'] = ta.RSI(dataframe, timeperiod=1000)
+
+
+        # Stochastic Fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+        dataframe['mfi20'] = ta.MFI(dataframe, timeperiod=20)
+        dataframe['mfi50'] = ta.MFI(dataframe, timeperiod=50)
+        dataframe['mfi100'] = ta.MFI(dataframe, timeperiod=100)
+        dataframe['mfi200'] = ta.MFI(dataframe, timeperiod=200)
+        dataframe['mfi400'] = ta.MFI(dataframe, timeperiod=400)
+        dataframe['mfi1000'] = ta.MFI(dataframe, timeperiod=1000)
+
+        # # ROC
+        dataframe['roc'] = ta.ROC(dataframe)
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # 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"]
+        )
+
+        # # 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)
+        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=100)
+        dataframe['ema400'] = ta.EMA(dataframe, timeperiod=100)
+
+
+        # Parabolic SAR
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        dataframe['tema3'] = ta.TEMA(dataframe, timeperiod=3)
+        dataframe['tema5'] = ta.TEMA(dataframe, timeperiod=5)
+        dataframe['tema14'] = ta.TEMA(dataframe, timeperiod=14)
+        dataframe['tema21'] = ta.TEMA(dataframe, timeperiod=21)
+        dataframe['tema50'] = ta.TEMA(dataframe, timeperiod=50)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # # Chart type
+        # # ------------------------------------
+        # helps with pattern recognition
+        dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
+        dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close')
+        dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close')
+        dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close')
+
+        # Retrieve best bid and best ask from the orderbook
+        # ------------------------------------
+        """
+        # first check if dataprovider is available
+        if self.dp:
+            if self.dp.runmode in ('live', 'dry_run'):
+                ob = self.dp.orderbook(metadata['pair'], 1)
+                dataframe['best_bid'] = ob['bids'][0][0]
+                dataframe['best_ask'] = ob['asks'][0][0]
+        """
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[ # Breakout: TEMA crosses over longer TEMA
+            (
+                (qtpylib.crossed_above(dataframe['tema5'], dataframe['tema14'])) &
+                (dataframe['adx'] > 25)
+            ),
+            'buy'] = 1
+        dataframe.loc[ # Breakout: RSI plus momentum
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 60)) &
+                (dataframe['adx'] > 30)
+            ),
+            'buy'] = 1
+        dataframe.loc[ # Breakout: crossing middle of Bollinger Bands
+            (
+                (qtpylib.crossed_above(dataframe['close'], dataframe['bb_middleband'])) &
+                (dataframe['adx'] > 30) &
+                (dataframe['adx'] > dataframe['adx'].shift(1)) &
+                (dataframe['adx'].shift(1) > dataframe['adx'].shift(2)) &
+                (dataframe['adx'].shift(2) > dataframe['adx'].shift(3)) &
+                (dataframe['close'] > dataframe['ema21']) &
+                (dataframe['close'] > dataframe['ema50']) &
+                (dataframe['roc'] > 0) &
+                (dataframe['ema50'] > 0) &
+                (dataframe['ema21'] > dataframe['ema50'])
+            ),
+            'buy'] = 1
+        dataframe.loc[ # Breakout: Increasing momentum and positive price action
+            (
+                (qtpylib.crossed_above(dataframe['adx'], 25)) &
+                (dataframe['ema21'] > dataframe['ema50'])
+            ),
+            'buy'] = 1
+        dataframe.loc[ # BTFD: CCI bounce
+                # Doesn't like BTC Dominance tracking sideways
+                # Enable by uncommenting the indicators
+            (
+                #(qtpylib.crossed_above(dataframe['cci'], -100))
+            ),
+            'buy'] = 1
+        dataframe.loc[ # BTFD: Price Crosses into the Bollinger Bands from below
+                # Doesn't like BTC Dominance tracking sideways
+                # Enable by uncommenting the indicators
+            (
+                #(qtpylib.crossed_above(dataframe['close'], dataframe['bb_lowerband']))
+            ),
+            'buy'] = 1
+        dataframe.loc[ # BTFD: V-shaped bottom.
+                # Doesn't like BTC Dominance tracking sideways
+                # Enable by uncommenting the indicators
+            (
+                #(dataframe['average'].shift(5) > dataframe['average'].shift(4)) &
+                #(dataframe['average'].shift(4) > dataframe['average'].shift(3)) &
+                #(dataframe['average'].shift(3) > dataframe['average'].shift(2)) &
+                #(dataframe['average'].shift(2) > dataframe['average'].shift(1)) &
+                #(dataframe['average'].shift(1) < dataframe['average'].shift(0)) &
+                #(dataframe['low'].shift(1) < dataframe['bb_middleband']) &
+                #(dataframe['cci'].shift(1) < -100)
+            ),
+            'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+		(qtpylib.crossed_above(dataframe['mfi'], 95))  # Signal: RSI crosses above 70
+            ),
+            'sell'] = 1
+        dataframe.loc[
+            (
+		(dataframe['mfi'] > 90) &  # Signal: RSI crosses above 70
+		(dataframe['tema'] < dataframe['tema'].shift(3)) & # Guard: negative trend
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'sell'] = 1
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband'])) &
+                (dataframe['mfi'] > 60)  # High MFI
+            ),
+            'sell'] = 1
+        dataframe.loc[
+            (
+                (qtpylib.crossed_below(dataframe['adx'], 28)) &
+                (dataframe['rsi'] > 60)  #  High RSI
+            ),
+            'sell'] = 1
+        dataframe.loc[
+            (
+                (qtpylib.crossed_below(dataframe['adx'], 28)) &
+                (dataframe['mfi'] > 60)  # High MFI
+            ),
+            'sell'] = 1
+        return dataframe
+