Modify hyperoptable strategy to use relative importing

tests/strategy/strats/hyperoptable_strategy.py

@@ -1,14 +1,13 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 
-import talib.abstract as ta
 from pandas import DataFrame
+from strategy_test_v2 import StrategyTestV2
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
+from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter
-                                RealParameter)
 
 
-class HyperoptableStrategy(IStrategy):
+class HyperoptableStrategy(StrategyTestV2):
     """
     Default Strategy provided by freqtrade bot.
     Please do not modify this strategy, it's  intended for internal use only.
@@ -16,38 +15,6 @@ class HyperoptableStrategy(IStrategy):
     or strategy repository https://github.com/freqtrade/freqtrade-strategies
     for samples and inspiration.
     """
-    INTERFACE_VERSION = 2
-
-    # Minimal ROI designed for the strategy
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
-
-    # Optimal stoploss designed for the strategy
-    stoploss = -0.10
-
-    # Optimal ticker interval for the strategy
-    timeframe = '5m'
-
-    # Optional order type mapping
-    order_types = {
-        'buy': 'limit',
-        'sell': 'limit',
-        'stoploss': 'limit',
-        'stoploss_on_exchange': False
-    }
-
-    # Number of candles the strategy requires before producing valid signals
-    startup_candle_count: int = 20
-
-    # Optional time in force for orders
-    order_time_in_force = {
-        'buy': 'gtc',
-        'sell': 'gtc',
-    }
 
     buy_params = {
         'buy_rsi': 35,
@@ -91,55 +58,6 @@ class HyperoptableStrategy(IStrategy):
         """
         return []
 
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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.
-        :param dataframe: Dataframe with data from the exchange
-        :param metadata: Additional information, like the currently traded pair
-        :return: a Dataframe with all mandatory indicators for the strategies
-        """
-
-        # Momentum Indicator
-        # ------------------------------------
-
-        # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
-
-        # MACD
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
-
-        # Minus Directional Indicator / Movement
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # Plus Directional Indicator / Movement
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-
-        # Stoch fast
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['fastk'] = stoch_fast['fastk']
-
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_middleband'] = bollinger['mid']
-        dataframe['bb_upperband'] = bollinger['upper']
-
-        # EMA - Exponential Moving Average
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-
-        return dataframe
-
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Based on TA indicators, populates the buy signal for the given dataframe