ENH #216 alma indicator TST added DOC readme

README.md

@@ -14,7 +14,7 @@ Pandas TA - A Technical Analysis Library in Python 3
 ![Example Chart](/images/TA_Chart.png)
 
 
-_Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas library with more than 120 Indicators and Utility functions. Many commonly used indicators are included, such as: _Simple Moving Average_ (**sma**) _Moving Average Convergence Divergence_ (**macd**), _Hull Exponential Moving Average_ (**hma**), _Bollinger Bands_ (**bbands**), _On-Balance Volume_ (**obv**), _Aroon & Aroon Oscillator_ (**aroon**), _Squeeze_ (**squeeze**) and **_many more_**.
+_Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas library with more than 130 Indicators and Utility functions. Many commonly used indicators are included, such as: _Simple Moving Average_ (**sma**) _Moving Average Convergence Divergence_ (**macd**), _Hull Exponential Moving Average_ (**hma**), _Bollinger Bands_ (**bbands**), _On-Balance Volume_ (**obv**), _Aroon & Aroon Oscillator_ (**aroon**), _Squeeze_ (**squeeze**) and **_many more_**.
 
 <br/>
 
@@ -59,7 +59,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
 
 # **Features**
 
-* Has 120+ indicators and utility functions.
+* Has 130+ indicators and utility functions.
 * Indicators are tightly correlated with the de facto [TA Lib](https://mrjbq7.github.io/ta-lib/) if they share common indicators.
 * Have the need for speed? By using the DataFrame _strategy_ method, you get **multiprocessing** for free!
 * Easily add _prefixes_ or _suffixes_ or both to columns names. Useful for Custom Chained Strategies.
@@ -496,8 +496,9 @@ print(bothhl2.name)  # "pre_HL2_post"
 |:--------:|
 | ![Example MACD](/images/SPY_MACD.png) |
 
-### **Overlap** (30)
+### **Overlap** (31)
 
+* _Arnaud Legoux Moving Average_: **alma**
 * _Double Exponential Moving Average_: **dema**
 * _Exponential Moving Average_: **ema**
 * _Fibonacci's Weighted Moving Average_: **fwma**
@@ -683,6 +684,7 @@ result = ta.cagr(df.close)
 
 
 ## **New Indicators**
+* _Arnaud Legoux Moving Average_ (**alma**) uses the curve of the Normal (Gauss) distribution to allow regulating the smoothness and high sensitivity of the indicator. See: ```help(ta.alma)```
 * _Drawdown_ (**drawdown**) shows the peak-to-trough decline during a specific period for an investment,
 trading account, or fund. See: ```help(ta.drawdown)```
 * _Gann High-Low Activator_ (**hilo**) was created by Robert Krausz in a 1998. See: ```help(ta.hilo)```

pandas_ta/overlap/alma.py

@@ -18,8 +18,8 @@ def alma(close, length=None, sigma=None, distribution_offset=None, offset=None,
     m = (distribution_offset * (length - 1))
     s = length / sigma
     wtd = list(range(length))
-    for j in range(0, length):
-        wtd[j] = math.exp(-1 * ((j - m) * (j - m)) / (2 * s * s))
+    for i in range(0, length):
+        wtd[i] = math.exp(-1 * ((i - m) * (i - m)) / (2 * s * s))
 
     # Calculate Result
     result = [npNaN for _ in range(0, length - 1)] + [0]
@@ -49,7 +49,7 @@ def alma(close, length=None, sigma=None, distribution_offset=None, offset=None,
         alma.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name & Category
-    alma.name = f"ALMA_{length}"
+    alma.name = f"ALMA_{length}_{sigma}_{distribution_offset}"
     alma.category = "overlap"
 
     return alma
@@ -58,10 +58,11 @@ def alma(close, length=None, sigma=None, distribution_offset=None, offset=None,
 alma.__doc__ = \
 """Arnaud Legoux Moving Average (ALMA)
 
-The ALMA moving average uses the curve of the Normal (Gauss) distribution, which can be shifted  
-from 0 to 1. This allows regulating the smoothness and high sensitivity of the indicator. 
-Sigma is another parameter that is responsible for the shape of the curve coefficients. This moving average 
-reduces lag of the data in conjunction with smoothing to reduce noise.
+The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
+can be shifted from 0 to 1. This allows regulating the smoothness and high
+sensitivity of the indicator. Sigma is another parameter that is responsible for
+the shape of the curve coefficients. This moving average reduces lag of the data
+in conjunction with smoothing to reduce noise.
 
 Implemented for Pandas TA by rengel8 based on the source provided below.
 
@@ -75,7 +76,9 @@ Args:
     close (pd.Series): Series of 'close's
     length (int): It's period, window size. Default: 10
     sigma (float): Smoothing value. Default 6.0
-    distribution_offset (float): Value to offset the distribution min 0 (smoother), max 1 (more responsive). Default 0.85  
+    distribution_offset (float):
+        Value to offset the distribution min 0 (smoother),
+        max 1 (more responsive). Default 0.85
     offset (int): How many periods to offset the result.  Default: 0
 
 Kwargs:

setup.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from distutils.core import setup
 
-long_description = "An easy to use Python 3 Pandas Extension with 115+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib."
+long_description = "An easy to use Python 3 Pandas Extension with 130+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib."
 
 setup(
     name="pandas_ta",
@@ -17,7 +17,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "42b")),
+    version=".".join(("0", "2", "43b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_ext_indicator_overlap_ext.py

@@ -18,6 +18,11 @@ class TestOverlapExtension(TestCase):
     def tearDown(self): pass
 
 
+    def test_alma_ext(self):
+        self.data.ta.alma(append=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(self.data.columns[-1], "ALMA_10_6.0_0.85")
+
     def test_dema_ext(self):
         self.data.ta.dema(append=True)
         self.assertIsInstance(self.data, DataFrame)

tests/test_indicator_overlap.py

@@ -34,6 +34,11 @@ class TestOverlap(TestCase):
     def tearDown(self): pass
 
 
+    def test_alma(self):
+        result = pandas_ta.alma(self.close)# , length=None, sigma=None, distribution_offset=)
+        self.assertIsInstance(result, Series)
+        self.assertEqual(result.name, "ALMA_10_6.0_0.85")
+
     def test_dema(self):
         result = pandas_ta.dema(self.close)
         self.assertIsInstance(result, Series)