Merge branch 'pr/190' into development

pandas_ta/__init__.py

@@ -50,7 +50,7 @@ Category = {
     # Overlap
     "overlap": [
         "dema", "ema", "fwma", "hilo", "hl2", "hlc3", "hma", "ichimoku",
-        "kama", "linreg", "midpoint", "midprice", "ohlc4", "pwma", "rma",
+        "kama", "linreg", "mcg", "midpoint", "midprice", "ohlc4", "pwma", "rma",
         "sinwma", "sma", "ssf", "supertrend", "swma", "t3", "tema", "trima",
         "vidya", "vwap", "vwma", "wcp", "wma", "zlma"
     ],

pandas_ta/core.py

@@ -1021,6 +1021,11 @@ class AnalysisIndicators(BasePandasObject):
         result = linreg(close=close, length=length, offset=offset, adjust=adjust, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def mcg(self, length=None, offset=None, **kwargs):
+        close = self._get_column(kwargs.pop("close", "close"))
+        result = mcg(close=close, length=length, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def midpoint(self, length=None, offset=None, **kwargs):
         close = self._get_column(kwargs.pop("close", "close"))
         result = midpoint(close=close, length=length, offset=offset, **kwargs)

pandas_ta/overlap/__init__.py

@@ -10,6 +10,7 @@ from .kama import kama
 from .ichimoku import ichimoku
 from .linreg import linreg
 from .ma import ma
+from .mcg import mcg
 from .midpoint import midpoint
 from .midprice import midprice
 from .ohlc4 import ohlc4

pandas_ta/overlap/mcg.py

@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+from pandas_ta.utils import get_offset, verify_series
+
+
+def mcg(close, length: int = 10, offset: int = 0, c: float = 1, **kwargs):
+    """Indicator: McGinley Dynamic Indicator"""
+    # Validate arguments
+    close = verify_series(close)
+    length = int(length) if length > 0 else 10
+    c = c if 1 >= c > 0 else 1
+    offset = get_offset(offset)
+
+    # Calculate Result
+    close = close.copy()
+
+    def mcg_(series):
+        denom = (c * length * (series.iloc[1] / series.iloc[0]) ** 4)
+        series.iloc[1] = (series.iloc[0] + ((series.iloc[1] - series.iloc[0]) / denom))
+        return series.iloc[1]
+
+    mcg_cell = close[0:].rolling(2, min_periods=2).apply(mcg_, raw=False)
+    mcg_ds = close[:1].append(mcg_cell[1:])
+
+    # Offset
+    if offset != 0:
+        mcg_ds = mcg_ds.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        mcg_ds.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        mcg_ds.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name & Category
+    mcg_ds.name = f"McGinley_{length}"
+    mcg_ds.category = 'overlap'
+
+    return mcg_ds
+
+
+mcg.__doc__ = \
+"""McGinley Dynamic Indicator
+
+The McGinley Dynamic looks like a moving average line, yet it is actually a smoothing mechanism
+for prices that minimizes price separation, price whipsaws, and hugs prices much more closely. 
+Because of the calculation, the Dynamic Line speeds up in down markets as it follows prices 
+yet moves more slowly in up markets.
+
+Sources:
+    https://www.investopedia.com/articles/forex/09/mcginley-dynamic-indicator.asp
+
+Calculation:
+    Default Inputs:
+        length=10
+        offset=0
+        c=1
+        
+    def mcg_(series):
+        denom = (constant * length * (series.iloc[1] / series.iloc[0]) ** 4)
+        series.iloc[1] = (series.iloc[0] + ((series.iloc[1] - series.iloc[0]) / denom))
+        return series.iloc[1]
+    mcg_cell = close[0:].rolling(2, min_periods=2).apply(mcg_, raw=False)
+    mcg_ds = close[:1].append(mcg_cell[1:])
+
+Args:
+    close (pd.Series): Series of 'close's
+    length (int): Indicator's period.  Default: 10
+    offset (int): Number of periods to offset the result.  Default: 0
+    c (float): Multiplier for the denominator, sometimes set to 0.6. Default: 1
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.Series: New feature generated.
+"""