update name

USDT/ADA-ETH-DOT.py

@@ -0,0 +1,76 @@
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from functools import reduce
+from pandas import DataFrame
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy
+
+__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"
+
+# CCI timerperiods and values
+cciBuyTP = 47
+cciBuyVal = -39
+cciSellTP = 42
+cciSellVal = 135
+
+# RSI timeperiods and values
+rsiBuyTP = 24
+rsiBuyVal = 30
+rsiSellTP = 15
+rsiSellVal = 69
+    
+class ADAETHDOT(IStrategy):
+    timeframe = '15m'
+    
+    stoploss = -0.291
+    
+    minimal_roi = {
+        "0": 0.238,
+        "115": 0.131,
+        "272": 0.048,
+        "613": 0
+    }
+    
+    def informative_pairs(self):
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    
+        dataframe['cci-'+str(cciBuyTP)] = ta.CCI(dataframe, timeperiod=cciBuyTP)
+        dataframe['cci-'+str(cciSellTP)] = ta.CCI(dataframe, timeperiod=cciSellTP)     
+
+        dataframe['rsi-'+str(rsiBuyTP)] = ta.RSI(dataframe, timeperiod=rsiBuyTP)
+        dataframe['rsi-'+str(rsiSellTP)] = ta.RSI(dataframe, timeperiod=rsiSellTP)
+        
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+        dataframe.loc[
+            (
+                (dataframe['cci-'+str(cciBuyTP)] < cciBuyVal) &
+                (dataframe['rsi-'+str(rsiBuyTP)] < rsiBuyVal)
+            ),
+            'buy'] = 1
+            
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+        dataframe.loc[
+            ( 
+                (dataframe['cci-'+str(cciSellTP)] > cciSellVal) & 
+                (dataframe['rsi-'+str(rsiSellTP)] > rsiSellVal)
+            ),
+            'sell'] = 1
+            
+        return dataframe

USDT/CAKE.md

@@ -10,3 +10,5 @@
 | [CAKE_6](USDT/CAKE_6.py) | 2021-03-27 21:30:00 | 2021-04-30 19:00:00 | 97.07% | 1.10% | -1,301,216.89221 | SortinoHyperOptLoss | 38580 | |
 | [CAKE_7](USDT/CAKE_7.py) | 2021-03-31 21:30:00 | 2021-05-01 17:40:00 | 77.63% | 1.32% | -406,221.09900 | SortinoHyperOptLoss | 7723 | 31/28/0 |
 | [CAKE_8](USDT/CAKE_8.py) | 2021-03-31 21:30:00 | 2021-05-03 07:15:00 | 80.36% | 1.30% | -403,721.30770 | SortinoHyperOptLoss | 33420 | 32/30/0 |
+| [CAKE_9](USDT/CAKE_9.py) | 2021-03-31 21:30:00 | 2021-05-07 16:25:00 | 78.31% | 0.99% | -28,686.06459 | SortinoHyperOptLoss | 5963 | 46/33/0 |
+| [CAKE_10](USDT/CAKE_10.py) | 2021-03-31 21:30:00 | 2021-05-09 07:35:00 | 82.37% | 1.16% | -370,765.94923 | SortinoHyperOptLoss | 19839 | 37/34/0 |

USDT/CAKE_10.py

@@ -0,0 +1,386 @@
+# 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 import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class CAKE_10(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".
+    # ROI table:
+    minimal_roi = {
+        "0": 0.195,
+        "38": 0.037,
+        "96": 0.018,
+        "115": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.24852
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.334
+    trailing_stop_positive_offset = 0.394
+    trailing_only_offset_is_reached = True
+
+    unfilledtimeout = {
+        'buy': 10,
+        'sell': 30
+    }
+
+    # Optimal timeframe for the strategy.
+    timeframe = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # 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)
+
+        # # 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)
+        #dataframe['ema120'] = ta.EMA(dataframe, timeperiod=120)
+        #dataframe['ema150'] = ta.EMA(dataframe, timeperiod=150)
+        #dataframe['ema180'] = ta.EMA(dataframe, timeperiod=180)
+        #dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
+
+        # # 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 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[
+            (
+                #(dataframe['adx'] > 22) &
+                (dataframe['mfi'] < 21) &
+                (dataframe['fastd'] < 44) &
+                (dataframe['close'] < dataframe['bb_lowerband'])
+                #qtpylib.crossed_above(dataframe['close'], dataframe['sar'])
+            ),
+            '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[
+            (
+                (dataframe['adx'] < 100) &
+                #(dataframe['cci'] > 446) &
+                (dataframe['fastd'] > 93) &
+                #(dataframe['mfi'] > 86) &
+                (dataframe['rsi'] > 97) &
+                #qtpylib.crossed_above(dataframe['sar'], dataframe['close'])
+                (dataframe['close'] > dataframe['bb_upperband'])
+                #(qtpylib.crossed_above(dataframe['macdsignal'], dataframe['macd']))
+
+                #(qtpylib.crossed_above(dataframe['sar'], dataframe['close']))
+                #(qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+                #(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
+            ),
+            'sell'] = 1
+        return dataframe
+

USDT/CAKE_9.py

@@ -0,0 +1,385 @@
+# 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 import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class CAKE_9(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 = {
+        "0": 0.111,
+        "18": 0.069,
+        "35": 0.011,
+        "51": 0
+    }
+
+
+    # Stoploss:
+    stoploss = -0.24852
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.238
+    trailing_stop_positive_offset = 0.287
+    trailing_only_offset_is_reached = True
+
+    unfilledtimeout = {
+        'buy': 10,
+        'sell': 30
+    }
+
+    # Optimal timeframe for the strategy.
+    timeframe = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # 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)
+
+        # # 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)
+        #dataframe['ema120'] = ta.EMA(dataframe, timeperiod=120)
+        #dataframe['ema150'] = ta.EMA(dataframe, timeperiod=150)
+        #dataframe['ema180'] = ta.EMA(dataframe, timeperiod=180)
+        #dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
+
+        # # 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 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[
+            (
+                #(dataframe['adx'] > 22) &
+                (dataframe['mfi'] < 21) &
+                (dataframe['fastd'] < 44) &
+                (dataframe['close'] < dataframe['bb_lowerband'])
+                #qtpylib.crossed_above(dataframe['close'], dataframe['sar'])
+            ),
+            '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[
+            (
+                #(dataframe['adx'] < 57) &
+                (dataframe['cci'] > 446) &
+                (dataframe['fastd'] > 74) &
+                (dataframe['mfi'] > 86) &
+                qtpylib.crossed_above(dataframe['sar'], dataframe['close'])
+                #(dataframe['rsi'] > 82) &
+                #(dataframe['close'] > dataframe['bb_upperband'])
+                #(qtpylib.crossed_above(dataframe['macdsignal'], dataframe['macd']))
+
+                #(qtpylib.crossed_above(dataframe['sar'], dataframe['close']))
+                #(qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+                #(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
+            ),
+            'sell'] = 1
+        return dataframe