diff --git a/user_data/strategies/fixed_riskreward_loss.py b/user_data/strategies/fixed_riskreward_loss.py
new file mode 100644
index 0000000..4e2a29f
--- /dev/null
+++ b/user_data/strategies/fixed_riskreward_loss.py
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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# isort: skip_file
+# --- 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
+from datetime import datetime
+from freqtrade.persistence import Trade
+from freqtrade.state import RunMode
+import logging
+logger = logging.getLogger(__name__)
+
+class FixedRiskRewardLoss(IStrategy):
+    """
+    This strategy uses custom_stoploss() to enforce a fixed risk/reward ratio
+    by first calculating a dynamic initial stoploss via ATR - last negative peak
+
+    After that, we caculate that initial risk and multiply it with an risk_reward_ratio
+    Once this is reached, stoploss is set to it and sell signal is enabled
+
+    Also there is a break even ratio. Once this is reached, the stoploss is adjusted to minimize
+    losses by setting it to the buy rate + fees.
+    """
+
+    custom_info = {
+        'risk_reward_ratio': 3.5,
+        'set_to_break_even_at_profit': 1,
+    }
+    use_custom_stoploss = True
+    stoploss = -0.9
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        """
+            custom_stoploss using a risk/reward ratio
+        """
+        result = break_even_sl = takeprofit_sl = -1
+        custom_info_pair = self.custom_info.get(pair)
+        if custom_info_pair is not None:
+            # using current_time/open_date directly via custom_info_pair[trade.open_daten]
+            # would only work in backtesting/hyperopt.
+            # in live/dry-run, we have to search for nearest row before it
+            open_date_mask = custom_info_pair.index.unique().get_loc(trade.open_date_utc, method='ffill')
+            open_df = custom_info_pair.iloc[open_date_mask]
+
+            # trade might be open too long for us to find opening candle
+            if(len(open_df) != 1):
+                return -1 # won't update current stoploss
+
+            initial_sl_abs = open_df['stoploss_rate']
+
+            # calculate initial stoploss at open_date
+            initial_sl = initial_sl_abs/current_rate-1
+
+            # calculate take profit treshold
+            # by using the initial risk and multiplying it
+            risk_distance = trade.open_rate-initial_sl_abs
+            reward_distance = risk_distance*self.custom_info['risk_reward_ratio']
+            # take_profit tries to lock in profit once price gets over
+            # risk/reward ratio treshold
+            take_profit_price_abs = trade.open_rate+reward_distance
+            # take_profit gets triggerd at this profit
+            take_profit_pct = take_profit_price_abs/trade.open_rate-1
+
+            # break_even tries to set sl at open_rate+fees (0 loss)
+            break_even_profit_distance = risk_distance*self.custom_info['set_to_break_even_at_profit']
+            # break_even gets triggerd at this profit
+            break_even_profit_pct = (break_even_profit_distance+current_rate)/current_rate-1
+
+            result = initial_sl
+            if(current_profit >= break_even_profit_pct):
+                break_even_sl = (trade.open_rate*(1+trade.fee_open+trade.fee_close) / current_rate)-1
+                result = break_even_sl
+
+            if(current_profit >= take_profit_pct):
+                takeprofit_sl = take_profit_price_abs/current_rate-1
+                result = takeprofit_sl
+
+        return result
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['atr'] = ta.ATR(dataframe)
+        dataframe['stoploss_rate'] = dataframe['close']-(dataframe['atr']*2)
+        self.custom_info[metadata['pair']] = dataframe[['date', 'stoploss_rate']].copy().set_index('date')
+
+        # all "normal" indicators:
+        # e.g.
+        # dataframe['rsi'] = ta.RSI(dataframe)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Placeholder Strategy: buys when SAR is smaller then candle before
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        # Allways buys
+        dataframe.loc[:, 'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Placeholder Strategy: does nothing
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+
+        # Never sells
+        dataframe.loc[:, 'sell'] = 0
+        return dataframe