[GH-175] z-score based kdj is invalid. (#176)

The initial value of z-score is set to 0, which breaks the kdj
calucation.  We should use the first valid value to fill the gap.

README.md

@@ -4,7 +4,7 @@
 [![codecov](https://codecov.io/gh/jealous/stockstats/branch/master/graph/badge.svg?token=IFMD1pVJ7T)](https://codecov.io/gh/jealous/stockstats)
 [![pypi](https://img.shields.io/pypi/v/stockstats.svg)](https://pypi.python.org/pypi/stockstats)
 
-VERSION: 0.6.1
+VERSION: 0.6.2
 
 ## Introduction
 

stockstats.py

@@ -359,7 +359,7 @@ def _get_r(self, meta: _Meta):
         self[meta.name] = self.roc(self[meta.column], shift)
 
     @staticmethod
-    def _shift(series: pd.Series, window: int):
+    def s_shift(series: pd.Series, window: int):
         """ Shift the series
 
         When window is negative, shift the past period to current.
@@ -386,11 +386,11 @@ def _get_s(self, meta: _Meta):
         negative values meaning data in the past,
         positive values meaning data in the future.
         """
-        self[meta.name] = self._shift(self[meta.column], meta.int)
+        self[meta.name] = self.s_shift(self[meta.column], meta.int)
 
     def _get_log_ret(self, _: _Meta):
         close = self['close']
-        self['log-ret'] = np.log(close / self._shift(close, -1))
+        self['log-ret'] = np.log(close / self.s_shift(close, -1))
 
     def _get_c(self, meta: _Meta) -> pd.Series:
         """ get the count of column in range (shifts)
@@ -424,7 +424,7 @@ def _shifted_columns(self,
         col = self.get(column)
         res = pd.DataFrame()
         for i in shifts:
-            res[int(i)] = self._shift(col, i).values
+            res[int(i)] = self.s_shift(col, i).values
         return res
 
     def _get_max(self, meta: _Meta):
@@ -538,7 +538,7 @@ def _get_trix(self, meta: _Meta):
         single = self.ema(self[meta.column], window)
         double = self.ema(single, window)
         triple = self.ema(double, window)
-        prev_triple = self._shift(triple, -1)
+        prev_triple = self.s_shift(triple, -1)
         triple_change = self._delta(triple, -1)
         self[meta.name] = triple_change * 100 / prev_triple
 
@@ -585,7 +585,7 @@ def _get_cci(self, meta: _Meta):
         self[meta.name] = (tp - tp_sma) / (.015 * mad)
 
     def _tr(self):
-        prev_close = self._shift(self['close'], -1)
+        prev_close = self.s_shift(self['close'], -1)
         high = self['high']
         low = self['low']
         c1 = high - low
@@ -724,7 +724,10 @@ def _get_z(self, meta: _Meta):
         col = self[meta.column]
         mean = self.sma(col, window)
         std = self.mov_std(col, window)
-        self[meta.name] = ((col - mean) / std).fillna(0.0)
+        value = (col - mean) / std
+        if len(value) > 1:
+            value.iloc[0] = value.iloc[1]
+        self[meta.name] = value
 
     def _atr(self, window):
         tr = self._tr()
@@ -854,8 +857,8 @@ def _get_cr(self, meta: _Meta):
         """
         window = meta.int
         middle = self._tp()
-        last_middle = self._shift(middle, -1)
-        ym = self._shift(middle, -1)
+        last_middle = self.s_shift(middle, -1)
+        ym = self.s_shift(middle, -1)
         high = self['high']
         low = self['low']
         p1_m = pd.concat((last_middle, high), axis=1).min(axis=1)
@@ -871,7 +874,7 @@ def _get_cr(self, meta: _Meta):
 
     def _shifted_cr_sma(self, cr, window):
         cr_sma = self.sma(cr, window)
-        return self._shift(cr_sma, -int(window / 2.5 + 1))
+        return self.s_shift(cr_sma, -int(window / 2.5 + 1))
 
     def _tp(self):
         if 'amount' in self:
@@ -1336,7 +1339,7 @@ def _get_mfi(self, meta: _Meta):
         window = meta.int
         middle = self._tp()
         money_flow = (middle * self["volume"]).fillna(0.0)
-        shifted = self._shift(middle, -1)
+        shifted = self.s_shift(middle, -1)
         delta = (middle - shifted).fillna(0)
         pos_flow = money_flow.mask(delta < 0, 0)
         neg_flow = money_flow.mask(delta >= 0, 0)
@@ -1407,7 +1410,7 @@ def _get_cmo(self, meta: _Meta):
 
     def ker(self, column, window):
         col = self[column]
-        col_window_s = self._shift(col, -window)
+        col_window_s = self.s_shift(col, -window)
         window_diff = (col - col_window_s).abs()
         diff = self._col_diff(column).abs()
         volatility = self.mov_sum(diff, window)

test.py

@@ -264,6 +264,16 @@ def test_column_kdjj(self):
         assert_that(kdjk_3.loc[20110104], near_to(74.5614))
         assert_that(kdjk_3.loc[20110120], near_to(7.37))
 
+    def test_z_kdj(self):
+        stock = self.get_stock_90days()
+        for col in ['open', 'high', 'low', 'close', 'volume']:
+            stock[col] = stock[f'{col}_20_z']
+        _ = stock[['kdjk', 'kdjd', 'kdjj']]
+        row = stock.loc[20110104]
+        assert_that(row['kdjk'], near_to(66.666))
+        assert_that(row['kdjd'], near_to(55.555))
+        assert_that(row['kdjj'], near_to(88.888))
+
     def test_column_cross(self):
         stock = self.get_stock_30days()
         cross = stock['kdjk_3_x_kdjd_3']
@@ -865,7 +875,7 @@ def test_aroon(self):
     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[20040817, 'close_14_z'], near_to(-0.7071))
         assert_that(stock.loc[20040915, 'close_14_z'], near_to(2.005))
         assert_that(stock.loc[20041014, 'close_14_z'], near_to(-2.014))