diff --git a/strategies/SwingHighToSky.py b/strategies/SwingHighToSky.py
deleted file mode 100755
index 4289cde..0000000
--- a/strategies/SwingHighToSky.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""
-author      = "Kevin Ossenbrück"
-copyright   = "Free For Use"
-credits     = ["Bloom Trading, Mohsen Hassan"]
-license     = "MIT"
-version     = "1.0"
-maintainer  = "Kevin Ossenbrück"
-email       = "kevin.ossenbrueck@pm.de"
-status      = "Live"
-"""
-
-from freqtrade.strategy import IStrategy
-from freqtrade.strategy import IntParameter
-from functools import reduce
-from pandas import DataFrame
-
-import talib.abstract as ta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-import numpy
-
-
-
-# CCI timerperiods and values
-cciBuyTP = 72
-cciBuyVal = -175
-cciSellTP = 66
-cciSellVal = -106
-
-# RSI timeperiods and values
-rsiBuyTP = 36
-rsiBuyVal = 90
-rsiSellTP = 45
-rsiSellVal = 88
-
-
-class SwingHighToSky(IStrategy):
-    INTERFACE_VERSION = 3
-
-    timeframe = '15m'
-
-    stoploss = -0.34338
-
-    minimal_roi = {"0": 0.27058, "33": 0.0853, "64": 0.04093, "244": 0}
-
-    buy_cci = IntParameter(low=-200, high=200, default=100, space='buy', optimize=True)
-    buy_cciTime = IntParameter(low=10, high=80, default=20, space='buy', optimize=True)
-    buy_rsi = IntParameter(low=10, high=90, default=30, space='buy', optimize=True)
-    buy_rsiTime = IntParameter(low=10, high=80, default=26, space='buy', optimize=True)
-
-    sell_cci = IntParameter(low=-200, high=200, default=100, space='sell', optimize=True)
-    sell_cciTime = IntParameter(low=10, high=80, default=20, space='sell', optimize=True)
-    sell_rsi = IntParameter(low=10, high=90, default=30, space='sell', optimize=True)
-    sell_rsiTime = IntParameter(low=10, high=80, default=26, space='sell', optimize=True)
-
-    # Buy hyperspace params:
-    buy_params = {
-        "buy_cci": -175,
-        "buy_cciTime": 72,
-        "buy_rsi": 90,
-        "buy_rsiTime": 36,
-    }
-
-    # Sell hyperspace params:
-    sell_params = {
-        "sell_cci": -106,
-        "sell_cciTime": 66,
-        "sell_rsi": 88,
-        "sell_rsiTime": 45,
-    }
-
-    def informative_pairs(self):
-        return []
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        for val in self.buy_cciTime.range:
-            dataframe[f'cci-{val}'] = ta.CCI(dataframe, timeperiod=val)
-
-        for val in self.sell_cciTime.range:
-            dataframe[f'cci-sell-{val}'] = ta.CCI(dataframe, timeperiod=val)
-
-        for val in self.buy_rsiTime.range:
-            dataframe[f'rsi-{val}'] = ta.RSI(dataframe, timeperiod=val)
-
-        for val in self.sell_rsiTime.range:
-            dataframe[f'rsi-sell-{val}'] = ta.RSI(dataframe, timeperiod=val)
-
-        return dataframe
-
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        dataframe.loc[
-            (
-                (dataframe[f'cci-{self.buy_cciTime.value}'] < self.buy_cci.value) &
-                (dataframe[f'rsi-{self.buy_rsiTime.value}'] < self.buy_rsi.value)
-            ),
-            'enter_long'] = 1
-
-        return dataframe
-
-    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        dataframe.loc[
-            (
-                (dataframe[f'cci-sell-{self.sell_cciTime.value}'] > self.sell_cci.value) &
-                (dataframe[f'rsi-sell-{self.sell_rsiTime.value}'] > self.sell_rsi.value)
-            ),
-            'exit_long'] = 1
-
-        return dataframe
diff --git a/strategies/sample_strategy.py b/strategies/sample_strategy.py
deleted file mode 100755
index fd81570..0000000
--- a/strategies/sample_strategy.py
+++ /dev/null
@@ -1,404 +0,0 @@
-# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
-# flake8: noqa: F401
-# isort: skip_file
-# --- Do not remove these libs ---
-import numpy as np  # noqa
-import pandas as pd  # noqa
-from pandas import DataFrame
-from typing import Optional, Union
-
-from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
-                                IStrategy, IntParameter)
-
-# --------------------------------
-# Add your lib to import here
-import talib.abstract as ta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-
-
-# This class is a sample. Feel free to customize it.
-class SampleStrategy(IStrategy):
-    """
-    This is a sample strategy to inspire you.
-    More information in https://www.freqtrade.io/en/latest/strategy-customization/
-
-    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 methods: populate_indicators, populate_entry_trend, populate_exit_trend
-    You should keep:
-    - timeframe, minimal_roi, stoploss, trailing_*
-    """
-    # Strategy interface version - allow new iterations of the strategy interface.
-    # Check the documentation or the Sample strategy to get the latest version.
-    INTERFACE_VERSION = 3
-
-    # Can this strategy go short?
-    can_short: bool = False
-
-    # Minimal ROI designed for the strategy.
-    # This attribute will be overridden if the config file contains "minimal_roi".
-    minimal_roi = {
-        "60": 0.01,
-        "30": 0.02,
-        "0": 0.04
-    }
-
-    # Optimal stoploss designed for the strategy.
-    # This attribute will be overridden if the config file contains "stoploss".
-    stoploss = -0.10
-
-    # Trailing stoploss
-    trailing_stop = False
-    # trailing_only_offset_is_reached = False
-    # trailing_stop_positive = 0.01
-    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
-
-    # Optimal timeframe for the strategy.
-    timeframe = '5m'
-
-    # Run "populate_indicators()" only for new candle.
-    process_only_new_candles = True
-
-    # These values can be overridden in the config.
-    use_exit_signal = True
-    exit_profit_only = False
-    ignore_roi_if_entry_signal = False
-
-    # Hyperoptable parameters
-    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
-    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
-    short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
-    exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
-
-    # Number of candles the strategy requires before producing valid signals
-    startup_candle_count: int = 30
-
-    # Optional order type mapping.
-    order_types = {
-        'entry': 'limit',
-        'exit': 'limit',
-        'stoploss': 'market',
-        'stoploss_on_exchange': False
-    }
-
-    # Optional order time in force.
-    order_time_in_force = {
-        'entry': 'GTC',
-        'exit': 'GTC'
-    }
-
-    plot_config = {
-        'main_plot': {
-            'tema': {},
-            'sar': {'color': 'white'},
-        },
-        'subplots': {
-            "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)
-
-        # # 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)
-        # dataframe['aroonup'] = aroon['aroonup']
-        # dataframe['aroondown'] = aroon['aroondown']
-        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
-
-        # # Awesome Oscillator
-        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
-
-        # # 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
-        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
-        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
-        # 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']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
-
-        # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
-
-        # # 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"]
-        )
-
-        # 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['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)
-
-        # Parabolic SAR
-        dataframe['sar'] = ta.SAR(dataframe)
-
-        # 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
-        # # ------------------------------------
-        # # Heikin Ashi Strategy
-        # heikinashi = qtpylib.heikinashi(dataframe)
-        # dataframe['ha_open'] = heikinashi['open']
-        # dataframe['ha_close'] = heikinashi['close']
-        # dataframe['ha_high'] = heikinashi['high']
-        # dataframe['ha_low'] = heikinashi['low']
-
-        # Retrieve best bid and best ask from the orderbook
-        # ------------------------------------
-        """
-        # first check if dataprovider is available
-        if self.dp:
-            if self.dp.runmode.value 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        Based on TA indicators, populates the entry signal for the given dataframe
-        :param dataframe: DataFrame
-        :param metadata: Additional information, like the currently traded pair
-        :return: DataFrame with entry columns populated
-        """
-        dataframe.loc[
-            (
-                # Signal: RSI crosses above 30
-                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
-                (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
-                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            'enter_long'] = 1
-
-        dataframe.loc[
-            (
-                # Signal: RSI crosses above 70
-                (qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) &
-                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
-                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            'enter_short'] = 1
-
-        return dataframe
-
-    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        Based on TA indicators, populates the exit signal for the given dataframe
-        :param dataframe: DataFrame
-        :param metadata: Additional information, like the currently traded pair
-        :return: DataFrame with exit columns populated
-        """
-        dataframe.loc[
-            (
-                # Signal: RSI crosses above 70
-                (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &
-                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
-                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-
-            'exit_long'] = 1
-
-        dataframe.loc[
-            (
-                # Signal: RSI crosses above 30
-                (qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) &
-                # Guard: tema below BB middle
-                (dataframe['tema'] <= dataframe['bb_middleband']) &
-                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
-                (dataframe['volume'] > 0)  # Make sure Volume is not 0
-            ),
-            'exit_short'] = 1
-
-        return dataframe