Align samples (hyperopt and strategy) to work together

user_data/hyperopts/sample_hyperopt.py

@@ -2,12 +2,11 @@
 
 from functools import reduce
 from typing import Any, Callable, Dict, List
-from datetime import datetime
 
-import numpy as np
+import numpy as np  # noqa
 import talib.abstract as ta
 from pandas import DataFrame
-from skopt.space import Categorical, Dimension, Integer, Real
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
@@ -34,34 +33,6 @@ class SampleHyperOpts(IHyperOpt):
     Sample implementation of these methods can be found in
     https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
     """
-    @staticmethod
-    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        Add several indicators needed for buy and sell strategies defined below.
-        """
-        # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
-        # MACD
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-        # Stochastic Fast
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        # Minus-DI
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_upperband'] = bollinger['upper']
-        # SAR
-        dataframe['sar'] = ta.SAR(dataframe)
-
-        return dataframe
 
     @staticmethod
     def buy_strategy_generator(params: Dict[str, Any]) -> Callable:

user_data/strategies/sample_strategy.py

@@ -107,16 +107,16 @@ class SampleStrategy(IStrategy):
         # RSI
         dataframe['rsi'] = ta.RSI(dataframe)
 
-        """
+
         # ADX
         dataframe['adx'] = ta.ADX(dataframe)
-
+        """
         # Awesome oscillator
         dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
 
         # Commodity Channel Index: values Oversold:<-100, Overbought:>100
         dataframe['cci'] = ta.CCI(dataframe)
-
+        """
         # MACD
         macd = ta.MACD(dataframe)
         dataframe['macd'] = macd['macd']
@@ -126,6 +126,7 @@ class SampleStrategy(IStrategy):
         # MFI
         dataframe['mfi'] = ta.MFI(dataframe)
 
+        """
         # Minus Directional Indicator / Movement
         dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
         dataframe['minus_di'] = ta.MINUS_DI(dataframe)
@@ -149,12 +150,13 @@ class SampleStrategy(IStrategy):
         stoch = ta.STOCH(dataframe)
         dataframe['slowd'] = stoch['slowd']
         dataframe['slowk'] = stoch['slowk']
-
+        """
         # Stoch fast
         stoch_fast = ta.STOCHF(dataframe)
         dataframe['fastd'] = stoch_fast['fastd']
         dataframe['fastk'] = stoch_fast['fastk']
 
+        """
         # Stoch RSI
         stoch_rsi = ta.STOCHRSI(dataframe)
         dataframe['fastd_rsi'] = stoch_rsi['fastd']
@@ -178,12 +180,11 @@ class SampleStrategy(IStrategy):
         dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
         dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
 
-        # SAR Parabol
-        dataframe['sar'] = ta.SAR(dataframe)
-
         # SMA - Simple Moving Average
         dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
         """
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
 
         # TEMA - Triple Exponential Moving Average
         dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)