From 01357845891ba0629f8352115a8725b07e965d5a Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Fri, 13 Sep 2019 19:49:34 +0200
Subject: [PATCH] remove unused indicators from default_strategy

---
 freqtrade/strategy/default_strategy.py | 38 ++++++++++++++------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/freqtrade/strategy/default_strategy.py b/freqtrade/strategy/default_strategy.py
index 4907f20ed..caf1bb82d 100644
--- a/freqtrade/strategy/default_strategy.py
+++ b/freqtrade/strategy/default_strategy.py
@@ -4,7 +4,6 @@ import talib.abstract as ta
 from pandas import DataFrame
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.indicator_helpers import fishers_inverse
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -75,7 +74,8 @@ class DefaultStrategy(IStrategy):
         dataframe['adx'] = ta.ADX(dataframe)
 
         # Awesome oscillator
-        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
         """
         # Commodity Channel Index: values Oversold:<-100, Overbought:>100
         dataframe['cci'] = ta.CCI(dataframe)
@@ -87,16 +87,15 @@ class DefaultStrategy(IStrategy):
         dataframe['macdhist'] = macd['macdhist']
 
         # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
+        # dataframe['mfi'] = ta.MFI(dataframe)
 
         # Minus Directional Indicator / Movement
-        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # 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_dm'] = ta.PLUS_DM(dataframe)
         dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
 
         """
         # ROC
@@ -106,15 +105,15 @@ class DefaultStrategy(IStrategy):
         dataframe['rsi'] = ta.RSI(dataframe)
 
         # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
+        # dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
 
         # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
 
         # Stoch
-        stoch = ta.STOCH(dataframe)
-        dataframe['slowd'] = stoch['slowd']
-        dataframe['slowk'] = stoch['slowk']
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
 
         # Stoch fast
         stoch_fast = ta.STOCHF(dataframe)
@@ -134,37 +133,39 @@ class DefaultStrategy(IStrategy):
         # Because ta.BBANDS implementation is broken with small numbers, it actually
         # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
         # and use middle band instead.
-        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+        # dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
 
         # 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)
+        """
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
 
         # SAR Parabol
-        dataframe['sar'] = ta.SAR(dataframe)
+        # 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)
+        # 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
         # ------------------------------------
         """
@@ -216,6 +217,7 @@ class DefaultStrategy(IStrategy):
         dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
         """
 
+        """
         # Chart type
         # ------------------------------------
         # Heikinashi stategy
@@ -224,7 +226,7 @@ class DefaultStrategy(IStrategy):
         dataframe['ha_close'] = heikinashi['close']
         dataframe['ha_high'] = heikinashi['high']
         dataframe['ha_low'] = heikinashi['low']
-
+        """
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: