Merge branch 'pr/216' into development

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.
@@ -500,8 +500,9 @@ 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**
@@ -687,6 +688,7 @@ 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/__init__.py

@@ -49,7 +49,7 @@ Category = {
     ],
     # Overlap
     "overlap": [
-        "dema", "ema", "fwma", "hilo", "hl2", "hlc3", "hma", "ichimoku",
+        "alma", "dema", "ema", "fwma", "hilo", "hl2", "hlc3", "hma", "ichimoku",
         "kama", "linreg", "mcgd", "midpoint", "midprice", "ohlc4", "pwma", "rma",
         "sinwma", "sma", "ssf", "supertrend", "swma", "t3", "tema", "trima",
         "vidya", "vwap", "vwma", "wcp", "wma", "zlma"

pandas_ta/core.py

@@ -976,6 +976,11 @@ class AnalysisIndicators(BasePandasObject):
         return self._post_process(result, **kwargs)
 
     # Overlap
+    def alma(self, length=None, sigma=None, distribution_offset=None, offset=None, **kwargs):
+        close = self._get_column(kwargs.pop("close", "close"))
+        result = alma(close=close, length=length, sigma=sigma, distribution_offset=distribution_offset, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+        
     def dema(self, length=None, offset=None, **kwargs):
         close = self._get_column(kwargs.pop("close", "close"))
         result = dema(close=close, length=length, offset=offset, **kwargs)

pandas_ta/overlap/__init__.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from .alma import alma
 from .dema import dema
 from .ema import ema
 from .fwma import fwma

pandas_ta/overlap/alma.py

@@ -0,0 +1,90 @@
+# -*- coding: utf-8 -*-
+from numpy import NaN as npNaN
+from pandas import Series
+from pandas_ta.utils import get_offset, verify_series
+import math
+
+
+def alma(close, length=None, sigma=None, distribution_offset=None, offset=None, **kwargs):
+    """Indicator: Arnaud Legoux Moving Average (ALMA)"""
+    # Validate Arguments
+    close = verify_series(close)
+    length = int(length) if length and length > 0 else 10
+    sigma = float(sigma) if sigma and sigma > 0 else 6.0
+    distribution_offset = float(distribution_offset) if distribution_offset and distribution_offset > 0 else 0.85
+    offset = get_offset(offset)
+
+    # Pre-Calculations
+    m = (distribution_offset * (length - 1))
+    s = length / sigma
+    wtd = list(range(length))
+    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]
+    for i in range(length, close.size):
+        window_sum = 0
+        cum_sum = 0
+        for j in range(0, length):
+            # wtd = math.exp(-1 * ((j - m) * (j - m)) / (2 * s * s))        # moved to pre-calc for efficiency
+            window_sum = window_sum + wtd[j] * close[i - j]
+            cum_sum = cum_sum + wtd[j]
+        almean = window_sum / cum_sum
+        if i == length:
+            result.append(npNaN)                                            # additional one bar NaN as pre-roll
+        else:
+            result.append(almean)
+
+    alma = Series(result, index=close.index)
+
+    # Offset
+    if offset != 0:
+        alma = alma.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        alma.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        alma.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name & Category
+    alma.name = f"ALMA_{length}_{sigma}_{distribution_offset}"
+    alma.category = "overlap"
+
+    return alma
+
+
+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.
+
+Implemented for Pandas TA by rengel8 based on the source provided below.
+
+Sources:
+    https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
+
+Calculation:
+    refer to provided source
+
+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
+    offset (int): How many periods to offset the result.  Default: 0
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.Series: New feature generated.
+"""

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", "44b")),
+    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)