Added new indicator CFO

pandas_ta/momentum/__init__.py

@@ -7,6 +7,7 @@ from .brar import brar
 from .cci import cci
 from .cg import cg
 from .cmo import cmo
+from .cfo import cfo
 from .coppock import coppock
 from .er import er
 from .eri import eri
@@ -31,4 +32,4 @@ from .stochrsi import stochrsi
 from .trix import trix
 from .tsi import tsi
 from .uo import uo
-from .willr import willr
+from .willr import willr

pandas_ta/momentum/cfo.py

@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+from ..utils import get_drift, get_offset, verify_series
+from ..overlap.linreg import linreg
+
+def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
+    """Indicator: Chande Forcast Oscillator (CFO)"""
+    # Validate Arguments
+    close = verify_series(close)
+    length = int(length) if length and length > 0 else 9
+    scalar = float(scalar) if scalar else 100
+    drift = get_drift(drift)
+    offset = get_offset(offset)
+
+    #Finding linear regression of Series
+    linreg_series = linreg(close,length=length)
+    cfo = ((close-linreg_series)/close *100)
+    # Offset
+    if offset != 0:
+        cfo = cfo.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        cfo.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        cfo.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name and Categorize it
+    cfo.name = f"CFO_{length}"
+    cfo.category = "momentum"
+
+    return cmo
+
+cfo.__doc__ = \
+"""Chande Forcast Oscillator (CFO)
+
+The Forecast Oscillator calculates the percentage difference between the actual price
+and the Time Series Forecast (the endpoint of a linear regression line).
+
+Sources:
+    https://www.fmlabs.com/reference/default.htm?url=ForecastOscillator.htm
+
+Calculation:
+    Default Inputs:
+        length=9, drift=1, scalar=100
+
+    # Same Calculation as RSI except for this step
+    CFO = ( ( CLOSE- LINERREG ) / CLOSE * 100 )
+
+Args:
+    close (pd.Series): Series of 'close's
+    scalar (float): How much to magnify.  Default: 100
+    drift (int): The short period.  Default: 1
+    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.
+"""