Merge branch 'development'

README.md

@@ -42,9 +42,11 @@ All the indicators return a named Series or a DataFrame in uppercase underscore
     - __Aberration__ (aberration)
     - __BRAR__ (brar)
 * Corrected Indicators:
+    - __Absolute Price Oscillator__ (apo)
     - __Aroon & Aroon Oscillator__ (aroon)
         * Fixed indicator and included oscillator in returned dataframe
     - __Bollinger Bands__ (bbands)
+    - __Commodity Channel Index__ (cci)
     - __Chande Momentum Oscillator__ (cmo)
 
 ## What is a Pandas DataFrame Extension?

pandas_ta/momentum/apo.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-from ..overlap.ema import ema
+from ..overlap.sma import sma
 from ..utils import get_offset, verify_series
 
 def apo(close, fast=None, slow=None, offset=None, **kwargs):
@@ -14,9 +14,8 @@ def apo(close, fast=None, slow=None, offset=None, **kwargs):
     offset = get_offset(offset)
 
     # Calculate Result
-    fastma = ema(close, length=fast, **kwargs)
-    slowma = ema(close, length=slow, **kwargs)
-    # EMAs are equivalent with talib, only their difference is minutely off
+    fastma = sma(close, length=fast)
+    slowma = sma(close, length=slow)
     apo = fastma - slowma
 
     # Offset
@@ -45,13 +44,13 @@ momentum.  It is simply the difference of two Exponential Moving Averages
 (EMA) of two different periods.  Note: APO and MACD lines are equivalent.
 
 Sources:
-    https://www.investopedia.com/terms/p/ppo.asp
+    https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/
 
 Calculation:
     Default Inputs:
         fast=12, slow=26
-    EMA = Exponential Moving Average
-    APO = EMA(close, fast) - EMA(close, slow)
+    SMA = Simple Moving Average
+    APO = SMA(close, fast) - SMA(close, slow)
 
 Args:
     close (pd.Series): Series of 'close's

pandas_ta/momentum/cci.py

@@ -10,9 +10,8 @@ def cci(high, low, close, length=None, c=None, offset=None, **kwargs):
     high = verify_series(high)
     low = verify_series(low)
     close = verify_series(close)
-    length = int(length) if length and length > 0 else 20
+    length = int(length) if length and length > 0 else 14
     c = float(c) if c and c > 0 else 0.015
-    min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
     offset = get_offset(offset)
 
     # Calculate Result
@@ -52,7 +51,7 @@ Sources:
 
 Calculation:
     Default Inputs:
-        length=20, c=0.015
+        length=14, c=0.015
     SMA = Simple Moving Average
     MAD = Mean Absolute Deviation
     tp = typical_price = hlc3 = (high + low + close) / 3
@@ -64,7 +63,7 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 20
+    length (int): It's period.  Default: 14
     c (float):  Scaling Constant.  Default: 0.015
     offset (int): How many periods to offset the result.  Default: 0
 

pandas_ta/momentum/stoch.py

@@ -44,10 +44,10 @@ def stoch(high, low, close, fast_k=None, slow_k=None, slow_d=None, offset=None,
         slowd.fillna(method=kwargs['fill_method'], inplace=True)
 
     # Name and Categorize it
-    fastk.name = f"STOCHF_{fast_k}"
-    fastd.name = f"STOCHF_{slow_d}"
-    slowk.name = f"STOCH_{slow_k}"
-    slowd.name = f"STOCH_{slow_d}"
+    fastk.name = f"STOCHFk_{fast_k}"
+    fastd.name = f"STOCHFd_{slow_d}"
+    slowk.name = f"STOCHk_{slow_k}"
+    slowd.name = f"STOCHd_{slow_d}"
     fastk.category = fastd.category = slowk.category = slowd.category = 'momentum'
 
     # Prepare DataFrame to return

pandas_ta/trend/adx.py

@@ -15,10 +15,10 @@ def adx(high, low, close, length=None, drift=None, offset=None, **kwargs):
     offset = get_offset(offset)
 
     # Calculate Result
-    _atr = atr(high=high, low=low, close=close, length=length)
+    atr_ = atr(high=high, low=low, close=close, length=length)
 
-    up = high - high.shift(drift)
-    dn = low.shift(drift) - low
+    up = high - high.shift(drift) # high.diff(drift)
+    dn = low.shift(drift) - low # low.diff(-drift).shift(drift)
 
     pos = ((up > dn) & (up > 0)) * up
     neg = ((dn > up) & (dn > 0)) * dn
@@ -26,8 +26,9 @@ def adx(high, low, close, length=None, drift=None, offset=None, **kwargs):
     pos = pos.apply(zero)
     neg = neg.apply(zero)
 
-    dmp = (100 / _atr) * rma(close=pos, length=length)
-    dmn = (100 / _atr) * rma(close=neg, length=length)
+    k = 100 / atr_
+    dmp = k * rma(close=pos, length=length)
+    dmn = k * rma(close=neg, length=length)
 
     dx = 100 * (dmp - dmn).abs() / (dmp + dmn)
     adx = rma(close=dx, length=length)

setup.py

@@ -6,7 +6,7 @@ long_description = "An easy to use Python 3 Pandas Extension with 100+ Technical
 setup(
     name ="pandas_ta",
     packages =['pandas_ta', 'pandas_ta.momentum', 'pandas_ta.overlap', 'pandas_ta.performance', 'pandas_ta.statistics', 'pandas_ta.trend', 'pandas_ta.volatility', 'pandas_ta.volume'],
-    version ="0.1.52b",
+    version ="0.1.55b",
     description =long_description,
     long_description =long_description,
     author ="Kevin Johnson",

tests/test_indicator_momentum.py

@@ -70,7 +70,7 @@ class TestMomentum(TestCase):
         self.assertEqual(result.name, 'APO_12_26')
 
         try:
-            expected = tal.APO(self.close, 12, 26)
+            expected = tal.APO(self.close)
             pdt.assert_series_equal(result, expected, check_names=False)
         except AssertionError as ae:
             try:
@@ -107,7 +107,7 @@ class TestMomentum(TestCase):
     def test_cci(self):
         result = pandas_ta.cci(self.high, self.low, self.close)
         self.assertIsInstance(result, Series)
-        self.assertEqual(result.name, 'CCI_20_0.015')
+        self.assertEqual(result.name, 'CCI_14_0.015')
 
         try:
             expected = tal.CCI(self.high, self.low, self.close)
@@ -273,6 +273,11 @@ class TestMomentum(TestCase):
         self.assertEqual(result.name, 'ANGLEd_1')
 
     def test_stoch(self):
+        result = pandas_ta.stoch(self.high, self.low, self.close, fast_k=14, slow_k=14, slow_d=14)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, 'STOCH_14_14_14')
+        self.assertEqual(len(result.columns), 4)
+
         result = pandas_ta.stoch(self.high, self.low, self.close)
         self.assertIsInstance(result, DataFrame)
         self.assertEqual(result.name, 'STOCH_14_5_3')

tests/test_indicator_momentum_ext.py

@@ -51,7 +51,7 @@ class TestMomentumExtension(TestCase):
     def test_cci_ext(self):
         self.data.ta.cci(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(self.data.columns[-1], 'CCI_20_0.015')
+        self.assertEqual(self.data.columns[-1], 'CCI_14_0.015')
 
     def test_cg_ext(self):
         self.data.ta.cg(append=True)
@@ -134,7 +134,7 @@ class TestMomentumExtension(TestCase):
     def test_stoch_ext(self):
         self.data.ta.stoch(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(list(self.data.columns[-4:]), ['STOCHF_14', 'STOCHF_3', 'STOCH_5', 'STOCH_3'])
+        self.assertEqual(list(self.data.columns[-4:]), ['STOCHFk_14', 'STOCHFd_3', 'STOCHk_5', 'STOCHd_3'])
 
     def test_trix_ext(self):
         self.data.ta.trix(append=True)