diff --git a/user_data/strategies/futures/VolatilitySystem.py b/user_data/strategies/futures/VolatilitySystem.py
new file mode 100644
index 0000000..15ce247
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+++ b/user_data/strategies/futures/VolatilitySystem.py
@@ -0,0 +1,157 @@
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these libs ---
+from datetime import datetime
+from typing import Optional
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+import talib.abstract as ta
+from freqtrade.persistence import Trade
+from freqtrade.strategy import (CategoricalParameter, DecimalParameter,
+                                IntParameter, IStrategy)
+from freqtrade.exchange import date_minus_candles
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+from technical.util import resample_to_interval, resampled_merge
+
+
+class VolatilitySystem(IStrategy):
+    """
+    Volatility System strategy.
+    Based on https://www.tradingview.com/script/3hhs0XbR/
+
+    Leverage is optional but the lower the better to limit liquidations
+    """
+    can_short = True
+
+    minimal_roi = {
+        "0": 100
+    }
+
+    stoploss = -1
+
+    # Optimal ticker interval for the strategy
+    timeframe = '1h'
+
+    plot_config = {
+        # Main plot indicators (Moving averages, ...)
+        'main_plot': {
+        },
+        'subplots': {
+            "Volatility system": {
+                "atr": {"color": "white"},
+                "abs_close_change": {"color": "red"},
+            }
+        }
+    }
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let easyprofiler do the work for you in finding out which indicators
+        are worth adding.
+        """
+        resample_int = 60 * 3
+        resampled = resample_to_interval(dataframe, resample_int)
+        # Average True Range (ATR)
+        resampled['atr'] = ta.ATR(resampled, timeperiod=14) * 2.0
+        # Absolute close change
+        resampled['close_change'] = resampled['close'].diff()
+        resampled['abs_close_change'] = resampled['close_change'].abs()
+
+        dataframe = resampled_merge(dataframe, resampled, fill_na=True)
+        dataframe['atr'] = dataframe[f'resample_{resample_int}_atr']
+        dataframe['close_change'] = dataframe[f'resample_{resample_int}_close_change']
+        dataframe['abs_close_change'] = dataframe[f'resample_{resample_int}_abs_close_change']
+
+        # Average True Range (ATR)
+        # dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) * 2.0
+        # Absolute close change
+        # dataframe['close_change'] = dataframe['close'].diff()
+        # dataframe['abs_close_change'] = dataframe['close_change'].abs()
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy and sell signals for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy and sell columns
+        """
+        # Use qtpylib.crossed_above to get only one signal, otherwise the signal is active
+        # for the whole "long" timeframe.
+        dataframe.loc[
+            # qtpylib.crossed_above(dataframe['close_change'] * 1, dataframe['atr']),
+            (dataframe['close_change'] * 1 > dataframe['atr'].shift(1)),
+            'enter_long'] = 1
+        dataframe.loc[
+            # qtpylib.crossed_above(dataframe['close_change'] * -1, dataframe['atr']),
+            (dataframe['close_change'] * -1 > dataframe['atr'].shift(1)),
+            'enter_short'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        use sell/buy signals as long/short indicators
+        """
+        dataframe.loc[
+            dataframe['enter_long'] == 1,
+            'exit_short'] = 1
+        dataframe.loc[
+            dataframe['enter_short'] == 1,
+            'exit_long'] = 1
+        return dataframe
+
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
+                            leverage: float, entry_tag: Optional[str], side: str,
+                            **kwargs) -> float:
+        # 50% stake amount on initial entry
+        return proposed_stake / 2
+
+    position_adjustment_enable = True
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float,
+                              min_stake: Optional[float], max_stake: float,
+                              current_entry_rate: float, current_exit_rate: float,
+                              current_entry_profit: float, current_exit_profit: float,
+                              **kwargs) -> Optional[float]:
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        if len(dataframe) > 2:
+            last_candle = dataframe.iloc[-1].squeeze()
+            previous_candle = dataframe.iloc[-2].squeeze()
+            signal_name = 'enter_long' if not trade.is_short else 'enter_short'
+            prior_date = date_minus_candles(self.timeframe, 1, current_time)
+            # Only enlarge position on new signal.
+            if (
+                last_candle[signal_name] == 1
+                and previous_candle[signal_name] != 1
+                and trade.nr_of_successful_entries < 2
+                and trade.orders[-1].order_date_utc < prior_date
+            ):
+                return trade.stake_amount
+        return None
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                 proposed_leverage: float, max_leverage: float, side: str,
+                 **kwargs) -> float:
+        """
+        Customize leverage for each new trade. This method is only called in futures mode.
+
+        :param pair: Pair that's currently analyzed
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param proposed_leverage: A leverage proposed by the bot.
+        :param max_leverage: Max leverage allowed on this pair
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :return: A leverage amount, which is between 1.0 and max_leverage.
+        """
+        return 2.0