[GH-137] Support Z-Score (#138)

Allow user to calculate the Z-Score of a column with column name
and window.

README.md

@@ -55,6 +55,7 @@ Supported statistics/indicators are:
 * WT: LazyBear's Wave Trend
 * Supertrend: with the Upper Band and Lower Band
 * Aroon: Aroon Oscillator
+* Z: Z-Score
 
 ## Installation
 
@@ -670,6 +671,27 @@ Examples:
 * `df['aroon']` returns Aroon oscillator with a window of 25
 * `df['aroon_14']` returns Aroon oscillator with a window of 14
 
+#### [Z-Score](https://www.investopedia.com/terms/z/zscore.asp)
+
+Z-score is a statistical measurement that describes a value's relationship to 
+the mean of a group of values. 
+
+There is no default column name or window for Z-Score.
+
+The statistical formula for a value's z-score is calculated using
+the following formula:
+
+```z = ( x - μ ) / σ```
+
+Where:
+
+* `z` = Z-score
+* `x` = the value being evaluated
+* `μ` = the mean
+* `σ` = the standard deviation
+
+Examples:
+* `df['close_75_z']` returns the Z-Score of close price with a window of 75
 
 ## Issues
 

stockstats.py

@@ -604,6 +604,18 @@ def _get_supertrend(self, window=None):
         self['supertrend'] = st
 
     def _get_aroon(self, window=None):
+        """ Aroon Oscillator
+
+        The Aroon Oscillator measures the strength of a trend and
+        the likelihood that it will continue.
+
+        The default window is 25.
+
+        * Aroon Oscillator = Aroon Up - Aroon Down
+        * Aroon Up = 100 * (n - periods since n-period high) / n
+        * Aroon Down = 100 * (n - periods since n-period low) / n
+        * n = window size
+        """
         if window is None:
             window = 25
             column_name = 'aroon'
@@ -628,6 +640,37 @@ def _window_pct(s):
         aroon_down = _window_pct(low_since)
         self[column_name] = aroon_up - aroon_down
 
+    def _get_z(self, column, window):
+        """ Z score
+
+        Z-score is a statistical measurement that describes a value's
+        relationship to the mean of a group of values.
+
+        The statistical formula for a value's z-score is calculated using
+        the following formula:
+
+        z = ( x - μ ) / σ
+
+        Where:
+
+        * z = Z-score
+        * x = the value being evaluated
+        * μ = the mean
+        * σ = the standard deviation
+        """
+        window = self.get_int_positive(window)
+        column_name = '{}_{}_z'.format(column, window)
+        col = self[column]
+        mean = col.rolling(
+            min_periods=1,
+            window=window,
+            center=False).mean()
+        std = col.rolling(
+            min_periods=1,
+            window=window,
+            center=False).std()
+        self[column_name] = ((col - mean) / std).fillna(0.0)
+
     def _atr(self, window):
         tr = self._tr()
         return self._smma(tr, window)

test.py

@@ -738,3 +738,10 @@ def test_aroon(self):
         _ = stock['aroon_5']
         assert_that(stock.loc[20040924, 'aroon_5'], equal_to(40))
         assert_that(stock.loc[20041020, 'aroon_5'], equal_to(-80))
+
+    def test_close_z(self):
+        stock = self._supor[:100]
+        _ = stock['close_14_z']
+        assert_that(stock.loc[20040817, 'close_14_z'], equal_to(0))
+        assert_that(stock.loc[20040915, 'close_14_z'], near_to(2.005))
+        assert_that(stock.loc[20041014, 'close_14_z'], near_to(-2.014))