Remove unnecessary methods, improve docstring

user_data/hyperopts/MACDStrategy_hyperopt.py

@@ -3,12 +3,11 @@
 import talib.abstract as ta
 from pandas import DataFrame
 from typing import Dict, Any, Callable, List
-from functools import reduce
 
-import numpy
+# import numpy as np
 from skopt.space import Categorical, Dimension, Integer, Real
 
-import freqtrade.vendor.qtpylib.indicators as qtpylib
+# import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
 
 class_name = 'MACDStrategy_hyperopt'
@@ -17,20 +16,22 @@ class_name = 'MACDStrategy_hyperopt'
 # This class is a sample. Feel free to customize it.
 class MACDStrategy_hyperopt(IHyperOpt):
     """
-    This is a test hyperopt to inspire you.
+    This is an Example hyperopt to inspire you. - corresponding to MACDStrategy in this repository.
+
+    To run this, best use the following command (adjust to your environment
+    ```
+    freqtrade hyperopt --strategy MACDStrategy --hyperopts MACDStrategy_hyperopt --spaces buy sell
+    ```
+    The idea is to optimize only the CCI value.
+    - Buy side: CCI between -700 and 0
+    - Sell side: CCI between 0 and 700
+
     More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
-     You can:
-    - Rename the class name (Do not forget to update class_name)
-    - Add any methods you want to build your hyperopt
-    - Add any lib you need to build your hyperopt
-     You must keep:
-    - the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
-    roi_space, generate_roi_table, stoploss_space
-    """
+     """
 
     @staticmethod
     def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-       
+
         macd = ta.MACD(dataframe)
         dataframe['macd'] = macd['macd']
         dataframe['macdsignal'] = macd['macdsignal']
@@ -97,42 +98,6 @@ class MACDStrategy_hyperopt(IHyperOpt):
             Integer(0, 700, name='sell-cci-value'),
         ]
 
-    @staticmethod
-    def generate_roi_table(params: Dict) -> Dict[int, float]:
-        """
-        Generate the ROI table that will be used by Hyperopt
-        """
-        roi_table = {}
-        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
-        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
-        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
-        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
-
-        return roi_table
-
-    @staticmethod
-    def stoploss_space() -> List[Dimension]:
-        """
-        Stoploss Value to search
-        """
-        return [
-            Real(-0.5, -0.02, name='stoploss'),
-        ]
-
-    @staticmethod
-    def roi_space() -> List[Dimension]:
-        """
-        Values to search for each ROI steps
-        """
-        return [
-            Integer(10, 120, name='roi_t1'),
-            Integer(10, 60, name='roi_t2'),
-            Integer(10, 40, name='roi_t3'),
-            Real(0.01, 0.04, name='roi_p1'),
-            Real(0.01, 0.07, name='roi_p2'),
-            Real(0.01, 0.20, name='roi_p3'),
-        ]
-
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Based on TA indicators. Should be a copy of from strategy