ALMA

ALMA (new indicator)
Arnaud Legoux Moving Average

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

@@ -955,6 +955,11 @@ 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 +1,87 @@
+# -*- 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 j in range(0, length):
+        wtd[j] = math.exp(-1 * ((j - m) * (j - 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}"
+    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.
+"""