hyperopt for AverageStrategy.py

user_data/hyperopts/AverageHyperopt.py

@@ -0,0 +1,151 @@
+import talib.abstract as ta
+from pandas import DataFrame
+from typing import Dict, Any, Callable, List
+from functools import reduce
+
+from skopt.space import Categorical, Dimension, Integer, Real
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+shortRangeBegin = 10
+shortRangeEnd = 20
+mediumRangeBegin = 100
+mediumRangeEnd = 120
+
+
+class AverageHyperopt(IHyperOpt):
+    """
+    Hyperopt file for optimizing AverageStrategy.
+    Uses ranges of EMA periods to find the best parameter combination.
+    """
+
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        for short in range(shortRangeBegin, shortRangeEnd):
+            dataframe[f'maShort({short})'] = ta.EMA(dataframe, timeperiod=short)
+
+        for medium in range(mediumRangeBegin, mediumRangeEnd):
+            dataframe[f'maMedium({medium})'] = ta.EMA(dataframe, timeperiod=medium)
+
+        return dataframe
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use
+            """
+            conditions = []
+            # TRIGGERS
+            if 'trigger' in params:
+                conditions.append(qtpylib.crossed_above(
+                    dataframe[f"maShort({params['trigger'][0]})"],
+                    dataframe[f"maMedium({params['trigger'][1]})"])
+                    )
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        buyTriggerList = []
+        for short in range(shortRangeBegin, shortRangeEnd):
+            for medium in range(mediumRangeBegin, mediumRangeEnd):
+                # The output will be (short, long)
+                buyTriggerList.append(
+                    (short, medium)
+                )
+        return [
+            Categorical(buyTriggerList, name='trigger')
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by hyperopt
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use
+            """
+            # print(params)
+            conditions = []
+
+            # TRIGGERS
+            if 'sell-trigger' in params:
+                conditions.append(qtpylib.crossed_above(
+                    dataframe[f"maMedium({params['sell-trigger'][1]})"],
+                    dataframe[f"maShort({params['sell-trigger'][0]})"])
+                    )
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters
+        """
+        sellTriggerList = []
+        for short in range(shortRangeBegin, shortRangeEnd):
+            for medium in range(mediumRangeBegin, mediumRangeEnd):
+                # The output will be (short, long)
+                sellTriggerList.append(
+                    (short, medium)
+                )
+
+        return [
+            Categorical(sellTriggerList, name='sell-trigger')
+        ]
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators. Should be a copy of from strategy
+        must align to populate_indicators in this file
+        Only used when --spaces does not include buy
+        """
+        dataframe.loc[
+            (
+                qtpylib.crossed_above(
+                    dataframe[f'maShort({shortRangeBegin})'],
+                    dataframe[f'maMedium({mediumRangeBegin})'])
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators. Should be a copy of from strategy
+        must align to populate_indicators in this file
+        Only used when --spaces does not include sell
+        """
+        dataframe.loc[
+            (
+                qtpylib.crossed_above(
+                    dataframe[f'maMedium({mediumRangeBegin})'],
+                    dataframe[f'maShort({shortRangeBegin})'])
+            ),
+            'sell'] = 1
+
+        return dataframe

user_data/strategies/berlinguyinca/AverageStrategy.py

@@ -1,7 +1,5 @@
 # --- Do not remove these libs ---
 from freqtrade.strategy.interface import IStrategy
-from typing import Dict, List
-from functools import reduce
 from pandas import DataFrame
 # --------------------------------
 
@@ -32,7 +30,6 @@ class AverageStrategy(IStrategy):
     ticker_interval = '4h'
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        macd = ta.MACD(dataframe)
 
         dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
         dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)