Decouple strategy from analyse.py

user_data/strategies/test_strategy.py

@@ -0,0 +1,129 @@
+
+# --- 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
+# --------------------------------
+
+# Add your lib to import here
+import talib.abstract as ta
+
+
+# Update this variable if you change the class name
+class_name = 'TestStrategy'
+
+
+class TestStrategy(IStrategy):
+    """
+    This is a test strategy to inspire you.
+    You can:
+    - 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
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 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
+
+    def populate_indicators(self, dataframe: DataFrame) -> 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.
+        """
+
+        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        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['adx'] > 30) &
+                (dataframe['tema'] <= dataframe['blower']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1))
+            ),
+            '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['adx'] > 70) &
+                (dataframe['tema'] > dataframe['blower']) &
+                (dataframe['tema'] < dataframe['tema'].shift(1))
+            ),
+            'sell'] = 1
+        return dataframe
+
+    def hyperopt_space(self) -> List[Dict]:
+        """
+        Define your Hyperopt space for the strategy
+        """
+        space = {
+            'adx': hp.choice('adx', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
+            ]),
+            'trigger': hp.choice('trigger', [
+                {'type': 'lower_bb'},
+            ]),
+            'stoploss': hp.uniform('stoploss', -0.5, -0.02),
+        }
+        return space
+
+    def buy_strategy_generator(self, params) -> None:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+            conditions = []
+            # GUARDS AND TRENDS
+            if params['adx']['enabled']:
+                conditions.append(dataframe['adx'] > params['adx']['value'])
+
+            # TRIGGERS
+            triggers = {
+                'lower_bb': dataframe['tema'] <= dataframe['blower'],
+            }
+            conditions.append(triggers.get(params['trigger']['type']))
+
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend