Cleanup legacy strategy

it's just a test and does not need the commented elements

tests/strategy/legacy_strategy.py

@@ -6,8 +6,6 @@ from pandas import DataFrame
 
 # Add your lib to import here
 import talib.abstract as ta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-import numpy  # noqa
 
 
 # This class is a sample. Feel free to customize it.
@@ -17,7 +15,6 @@ class TestStrategyLegacy(IStrategy):
     removed in a future update.
     Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py
     for a uptodate version of this template.
-
     """
 
     # Minimal ROI designed for the strategy.
@@ -51,156 +48,9 @@ class TestStrategyLegacy(IStrategy):
         # 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']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
-
-        # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
-
-        # Minus Directional Indicator / Movement
-        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # Plus Directional Indicator / Movement
-        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # ROC
-        dataframe['roc'] = ta.ROC(dataframe)
-
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-
-        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-        rsi = 0.1 * (dataframe['rsi'] - 50)
-        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
-
-        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
-
-        # Stoch
-        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']
-        dataframe['fastk_rsi'] = stoch_rsi['fastk']
-        """
-
-        # Overlap Studies
-        # ------------------------------------
-
-        # 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['ema3'] = ta.EMA(dataframe, timeperiod=3)
-        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-        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)
-        """
-
         # TEMA - Triple Exponential Moving Average
         dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
 
-        # Cycle Indicator
-        # ------------------------------------
-        # Hilbert Transform Indicator - SineWave
-        hilbert = ta.HT_SINE(dataframe)
-        dataframe['htsine'] = hilbert['sine']
-        dataframe['htleadsine'] = hilbert['leadsine']
-
-        # Pattern Recognition - Bullish candlestick patterns
-        # ------------------------------------
-        """
-        # Hammer: values [0, 100]
-        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
-        # Inverted Hammer: values [0, 100]
-        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
-        # Dragonfly Doji: values [0, 100]
-        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
-        # Piercing Line: values [0, 100]
-        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
-        # Morningstar: values [0, 100]
-        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
-        # Three White Soldiers: values [0, 100]
-        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-        """
-
-        # Pattern Recognition - Bearish candlestick patterns
-        # ------------------------------------
-        """
-        # Hanging Man: values [0, 100]
-        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
-        # Shooting Star: values [0, 100]
-        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
-        # Gravestone Doji: values [0, 100]
-        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
-        # Dark Cloud Cover: values [0, 100]
-        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
-        # Evening Doji Star: values [0, 100]
-        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
-        # Evening Star: values [0, 100]
-        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-        """
-
-        # Pattern Recognition - Bullish/Bearish candlestick patterns
-        # ------------------------------------
-        """
-        # Three Line Strike: values [0, -100, 100]
-        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
-        # Spinning Top: values [0, -100, 100]
-        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
-        # Engulfing: values [0, -100, 100]
-        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
-        # Harami: values [0, -100, 100]
-        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
-        # Three Outside Up/Down: values [0, -100, 100]
-        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
-        # Three Inside Up/Down: values [0, -100, 100]
-        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-        """
-
-        # Chart type
-        # ------------------------------------
-        """
-        # Heikinashi stategy
-        heikinashi = qtpylib.heikinashi(dataframe)
-        dataframe['ha_open'] = heikinashi['open']
-        dataframe['ha_close'] = heikinashi['close']
-        dataframe['ha_high'] = heikinashi['high']
-        dataframe['ha_low'] = heikinashi['low']
-        """
-
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@@ -212,8 +62,8 @@ class TestStrategyLegacy(IStrategy):
         dataframe.loc[
             (
                 (dataframe['adx'] > 30) &
-                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &
-                (dataframe['tema'] > dataframe['tema'].shift(1))
+                (dataframe['volume'] > 0)
             ),
             'buy'] = 1
 
@@ -228,8 +78,8 @@ class TestStrategyLegacy(IStrategy):
         dataframe.loc[
             (
                 (dataframe['adx'] > 70) &
-                (dataframe['tema'] > dataframe['bb_middleband']) &
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &
-                (dataframe['tema'] < dataframe['tema'].shift(1))
+                (dataframe['volume'] > 0)
             ),
             'sell'] = 1
         return dataframe