add generic fishers inverse transformation with smoothing

freqtrade/indicator_helpers.py

@@ -1,5 +1,7 @@
 from math import exp, pi, sqrt, cos
 
+import numpy
+import talib as ta
 from pandas import Series
 
 
@@ -25,3 +27,14 @@ def ehlers_super_smoother(series: Series, smoothing: float = 6):
             coeff2 * filtered.iloc[i-1] + coeff3 * filtered.iloc[i-2]
 
     return filtered
+
+
+def fishers_inverse(series: Series, smoothing: float = 0):
+    """ Does a smoothed fishers inverse transformation.
+        Can be used with any oscillator that goes from 0 to 100 like RSI or MFI """
+    v1 = 0.1 * (series - 50)
+    if smoothing > 0:
+        v2 = ta.WMA(v1.values, timeperiod=smoothing)
+    else:
+        v2 = v1
+    return (numpy.exp(2 * v2)-1) / (numpy.exp(2 * v2) + 1)

freqtrade/strategy/default_strategy.py

@@ -4,7 +4,7 @@ import talib.abstract as ta
 from pandas import DataFrame
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.strategy.interface import IStrategy
-
+from freqtrade.indicator_helpers import fishers_inverse
 
 class_name = 'DefaultStrategy'
 
@@ -76,8 +76,8 @@ class DefaultStrategy(IStrategy):
         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)
+        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)
         # Stoch