Long strategy generates about 0.4% a trade, at an average time of 11min and a total returns of 0.01BTC over 20 days. Sell points are not perfect yet. It sell to early most of the time

.gitignore

@@ -6,7 +6,6 @@ config.json
 .hyperopt
 logfile.txt
 hyperopt_trials.pickle
-user_data/
 freqtrade-plot.html
 
 # Byte-compiled / optimized / DLL files

user_data/strategies/Long.py

@@ -0,0 +1,79 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class Long(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60":  0.05,
+        "30":  0.06,
+        "20":  0.07,
+        "0":  0.08
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.15
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+        dataframe['cci'] = ta.CCI(dataframe)
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=50)
+
+        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']
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['macd'] > dataframe['macdsignal']) &
+                (dataframe['macd'] > 0) &
+                (dataframe['cci'] <= 0.0)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['tema'] < dataframe['close'])
+            ),
+            'sell'] = 1
+        return dataframe

user_data/strategies/Quickie.py

@@ -0,0 +1,99 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class Quickie(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+
+    idea:
+        momentum based strategie. The main idea is that it closes trades very quickly, while avoiding excessive losses. Hence a rather moderate stop loss in this case
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60":  0.01,
+        "30":  0.03,
+        "20":  0.04,
+        "0":  0.05
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.3
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        dataframe['cci'] = ta.CCI(dataframe)
+        dataframe['willr'] = ta.WILLR(dataframe)
+
+        dataframe['smaSlow'] = ta.SMA(dataframe, timeperiod=7)
+        dataframe['smaFast'] = ta.SMA(dataframe, timeperiod=13)
+
+        # required for graphing
+        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']
+
+        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1.5)
+        dataframe['bb_lowerband_2'] = bollinger['lower']
+        dataframe['bb_middleband_2'] = bollinger['mid']
+        dataframe['bb_upperband_2'] = bollinger['upper']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                # we want to buy oversold assets
+                (dataframe['cci'] <= -50)
+
+                # some basic trend should have been established
+                & (dataframe['macd'] > dataframe['macdsignal'])
+
+                # which starts inside the band
+                & (dataframe['open'] > dataframe['bb_lowerband'])
+            )
+            ,
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (dataframe['close'] >= dataframe['bb_upperband']) |
+            (
+                (dataframe['macd'] < dataframe['macdsignal']) &
+                (dataframe['cci'] >= 100)
+            )
+            ,
+            'sell'] = 1
+        return dataframe

user_data/strategies/ZLC.py

@@ -0,0 +1,90 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class ZLC(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.03,
+        "20": 0.04,
+        "0": 0.01
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.3
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        dataframe['cci-slow'] = ta.CCI(dataframe, timeperiod=25)
+        dataframe['cci-fast'] = ta.CCI(dataframe, timeperiod=50)
+        dataframe['expo'] = ta.EMA(dataframe, timeperiod=35)
+
+        # required for graphing
+        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']
+
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                #don't buy on peak tops
+                        (dataframe['close'] < dataframe['bb_middleband'])
+                        # this is the main concept of evaluating buys
+                        & (dataframe['cci-fast'] > 0)
+                        & (dataframe['cci-slow'] > 0)
+                        & (dataframe['close'] > dataframe['expo'])
+
+            )
+            ,
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (dataframe['close'] >= dataframe['bb_upperband']) |
+            (
+                    (dataframe['cci-fast'] < 0)
+                    & (dataframe['cci-slow'] < 0)
+                    & (dataframe['close'] < dataframe['expo'])
+
+            )
+            ,
+            'sell'] = 0
+        return dataframe