Merge branch 'master' into addMinimalRoi

user_data/strategies/BreakEven.py

@@ -0,0 +1,67 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+
+class BreakEven(IStrategy):
+    """
+    author@: lenik
+
+    Sometimes I want to close the bot ASAP, but not have the positions floating around.
+
+    I can "/stopbuy" and wait for the positions to get closed by the bot rules, which is
+    waiting for some profit, etc -- this usually takes too long...
+    
+    What I would prefer is to close everything that is over 0% profit to avoid the losses.
+
+    Here's a simple strategy with empty buy/sell signals and "minimal_roi = { 0 : 0 }" that
+    sells everything already at profit and wait until the positions at loss will come to break
+    even point (or the small profit you provide in ROI table).
+    
+    You may restart the bot with the new strategy as a command-line parameter.
+
+    Another way would be to specify the original strategy in the config file, then change to
+    this one and simply "/reload_config" from the Telegram bot.
+
+    """
+
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "0": 0.01,      # at least 1% at first
+        "10": 0         # after 10min, everything goes 
+    }
+
+    # This is more radical version that sells everything above the profit level
+#    minimal_roi = {
+#        "0": 0
+#    }
+
+    # And this is basically "/forcesell all", that sells no matter what profit
+#    minimal_roi = {
+#        "0": -1
+#    }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.05
+
+    # Optimal timeframe for the strategy
+    timeframe = '5m'
+
+    # don't generate any buy or sell signals, everything is handled by ROI and stop_loss
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+            ),
+            'buy'] = 0
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+            ),
+            'sell'] = 0
+        return dataframe

user_data/strategies/berlinguyinca/CofiBitStrategy.py

@@ -2,6 +2,7 @@
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 import talib.abstract as ta
 from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy import IntParameter
 from pandas import DataFrame
 
 
@@ -12,6 +13,17 @@ class CofiBitStrategy(IStrategy):
     """
         taken from slack by user CofiBit
     """
+    
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_fastx": 25,
+        "buy_adx": 25,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_fastx": 75,
+    }
 
     # Minimal ROI designed for the strategy.
     # This attribute will be overridden if the config file contains "minimal_roi"
@@ -29,6 +41,10 @@ class CofiBitStrategy(IStrategy):
     # Optimal timeframe for the strategy
     timeframe = '5m'
 
+    buy_fastx = IntParameter(20, 30, default=25)
+    buy_adx = IntParameter(20, 30, default=25)
+    sell_fastx = IntParameter(70, 80, default=75)
+
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
         dataframe['fastd'] = stoch_fast['fastd']
@@ -50,10 +66,9 @@ class CofiBitStrategy(IStrategy):
             (
                 (dataframe['open'] < dataframe['ema_low']) &
                 (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
-                # (dataframe['fastk'] > dataframe['fastd']) &
-                (dataframe['fastk'] < 30) &
-                (dataframe['fastd'] < 30) &
-                (dataframe['adx'] > 30)
+                (dataframe['fastk'] < self.buy_fastx.value) &
+                (dataframe['fastd'] < self.buy_fastx.value) &
+                (dataframe['adx'] > self.buy_adx.value)
             ),
             'buy'] = 1
 
@@ -70,10 +85,8 @@ class CofiBitStrategy(IStrategy):
                 (dataframe['open'] >= dataframe['ema_high'])
             ) |
             (
-                # (dataframe['fastk'] > 70) &
-                # (dataframe['fastd'] > 70)
-                    (qtpylib.crossed_above(dataframe['fastk'], 70)) |
-                    (qtpylib.crossed_above(dataframe['fastd'], 70))
+                (qtpylib.crossed_above(dataframe['fastk'], self.sell_fastx.value)) |
+                (qtpylib.crossed_above(dataframe['fastd'], self.sell_fastx.value))
             ),
             'sell'] = 1
 

user_data/strategies/mabStra.py

@@ -1,7 +1,7 @@
 # Author: @Mablue (Masoud Azizi)
 # github: https://github.com/mablue/
 # IMPORTANT: DO NOT USE IT WITHOUT HYPEROPT:
-# freqtrade hyperopt --hyperopt mabStraHo --hyperopt-loss SharpeHyperOptLoss --spaces all --strategy mabStra --config config.json -e 100
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces all --strategy mabStra --config config.json -e 100
 
 # --- Do not remove these libs ---
 from freqtrade.strategy.hyper import IntParameter, DecimalParameter
@@ -11,10 +11,10 @@ from pandas import DataFrame
 
 # Add your lib to import here
 import talib.abstract as ta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
 
 
 class mabStra(IStrategy):
+
     # #################### RESULTS PASTE PLACE ####################
     # ROI table:
     minimal_roi = {