ENH cfo indicator added MAINT linreg refactor

README.md

@@ -67,14 +67,15 @@ Thank you for your contribution!
 * _Linear Decay_ (**linear_decay**): Renamed to _Decay_ (**decay**) and with the option for Exponential decay using ```mode="exp"```. See: ```help(ta.decay)```
 
 ## __New Indicators__
-* _Squeeze_ (**squeeze**). A Momentum indicator. Both John Carter's TTM **and** Lazybear's TradingView versions are implemented. The default is John Carter's, or ```lazybear=False```. Set ```lazybear=True``` to enable Lazybear's.
-* _TTM Trend_ (**ttm_trend**). A trend indicator inspired from John Carter's book "Mastering the Trade".
+* _Chande Forecast Oscillator_ (**cfo**) It calculates the percentage difference between the actual price and the Time Series Forecast (the endpoint of a linear regression line).
+* _Gann High-Low Activator_ (**hilo**) The Gann High Low Activator Indicator was created by Robert Krausz in a 1998.
+* _Inside Bar_ (**cdl_inside**) An Inside Bar is a bar contained within it's previous bar's high and low See: ```help(ta.cdl_inside)```
 * _SMI Ergodic_ (**smi**) Developed by William Blau, the SMI Ergodic Indicator is the same as the True Strength Index (TSI) except the SMI includes a signal line and oscillator.
-* _Gann High-Low Activator_ (**hilo**) The Gann High Low Activator Indicator was created by Robert Krausz in a 1998
+* _Squeeze_ (**squeeze**). A Momentum indicator. Both John Carter's TTM **and** Lazybear's TradingView versions are implemented. The default is John Carter's, or ```lazybear=False```. Set ```lazybear=True``` to enable Lazybear's.
+* _Stochastic RSI_ (**stochrsi**) "Stochastic RSI and Dynamic Momentum Index" was created by Tushar Chande and Stanley Kroll. In line with Trading View's calculation. See: ```help(ta.stochrsi)```
+* _TTM Trend_ (**ttm_trend**). A trend indicator inspired from John Carter's book "Mastering the Trade".
 issue of Stocks & Commodities Magazine. It is a moving average based trend
 indicator consisting of two different simple moving averages.
-* _Stochastic RSI_ (**stochrsi**) "Stochastic RSI and Dynamic Momentum Index" was created by Tushar Chande and Stanley Kroll. In line with Trading View's calculation. See: ```help(ta.stochrsi)```
-* _Inside Bar_ (**cdl_inside**) An Inside Bar is a bar contained within it's previous bar's high and low See: ```help(ta.cdl_inside)```
 
 ## __Updated Indicators__
 * _Average True Range_ (**atr**): Added option to return **atr** as a percentage. See: ```help(ta.atr)```
@@ -365,7 +366,7 @@ print(bothhl2.name)  # "pre_HL2_post"
 * _Inside Bar_: **cdl_inside**
 * _Heikin-Ashi_: **ha**
 
-## _Momentum_ (33)
+## _Momentum_ (34)
 
 * _Awesome Oscillator_: **ao**
 * _Absolute Price Oscillator_: **apo**
@@ -373,6 +374,7 @@ print(bothhl2.name)  # "pre_HL2_post"
 * _Balance of Power_: **bop**
 * _BRAR_: **brar**
 * _Commodity Channel Index_: **cci**
+* _Chande Forecast Oscillator_: **cfo**
 * _Center of Gravity_: **cg**
 * _Chande Momentum Oscillator_: **cmo**
 * _Coppock Curve_: **coppock**

pandas_ta/__init__.py

@@ -25,7 +25,7 @@ Category = {
     "candles": ["cdl_doji", "cdl_inside", "ha"],
 
     # Momentum
-    "momentum": ["ao", "apo", "bias", "bop", "brar", "cci", "cg", "cmo", "coppock", "er", "eri", "fisher", "inertia", "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "pvo", "roc", "rsi", "rvgi", "slope", "smi", "squeeze", "stoch", "stochrsi", "trix", "tsi", "uo", "willr"],
+    "momentum": ["ao", "apo", "bias", "bop", "brar", "cci", "cfo", "cg", "cmo", "coppock", "er", "eri", "fisher", "inertia", "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "pvo", "roc", "rsi", "rvgi", "slope", "smi", "squeeze", "stoch", "stochrsi", "trix", "tsi", "uo", "willr"],
 
     # Overlap
     "overlap": ["dema", "ema", "fwma", "hilo", "hl2", "hlc3", "hma", "ichimoku", "kama", "linreg", "midpoint", "midprice", "ohlc4", "pwma", "rma", "sinwma", "sma", "supertrend", "swma", "t3", "tema", "trima", "vwap", "vwma", "wcp", "wma", "zlma"],

pandas_ta/core.py

@@ -23,7 +23,7 @@ from pandas_ta.volatility import *
 from pandas_ta.volume import *
 from pandas_ta.utils import *
 
-version = ".".join(("0", "2", "09b"))
+version = ".".join(("0", "2", "10b"))
 
 
 # Strategy DataClass
@@ -684,6 +684,11 @@ class AnalysisIndicators(BasePandasObject):
         result = cci(high=high, low=low, close=close, length=length, c=c, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def cfo(self, length=None, offset=None, **kwargs):
+        close  = self._get_column(kwargs.pop("close", "close"))
+        result = cfo(close=close, length=length, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def cg(self, length=None, offset=None, **kwargs):
         close  = self._get_column(kwargs.pop("close", "close"))
         result = cg(close=close, length=length, offset=offset, **kwargs)

pandas_ta/momentum/cfo.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
-from ..utils import get_drift, get_offset, verify_series
-from ..overlap.linreg import linreg
+from pandas_ta.overlap import linreg
+from pandas_ta.utils import get_drift, get_offset, verify_series
 
 def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
     """Indicator: Chande Forcast Oscillator (CFO)"""
@@ -12,8 +12,9 @@ def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
     offset = get_offset(offset)
 
     #Finding linear regression of Series
-    linreg_series = linreg(close,length=length)
-    cfo = ((close-linreg_series)/close *100)
+    cfo  = scalar * (close - linreg(close, length=length, tsf=True))
+    cfo /= close
+
     # Offset
     if offset != 0:
         cfo = cfo.shift(offset)
@@ -28,13 +29,13 @@ def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
     cfo.name = f"CFO_{length}"
     cfo.category = "momentum"
 
-    return cmo
+    return cfo
 
 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).
+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
@@ -42,15 +43,16 @@ Sources:
 Calculation:
     Default Inputs:
         length=9, drift=1, scalar=100
+    LINREG = Linear Regression
 
-    # Same Calculation as RSI except for this step
-    CFO = ( ( CLOSE- LINERREG ) / CLOSE * 100 )
+    CFO = scalar * (close - LINERREG(length, tdf=True)) / close
 
 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
+    length (int): The period. Default: 9
+    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)

pandas_ta/overlap/linreg.py

@@ -1,20 +1,20 @@
 # -*- coding: utf-8 -*-
 import math
-from ..utils import get_offset, verify_series
+from pandas_ta.utils import get_offset, verify_series
 
 def linreg(close, length=None, offset=None, **kwargs):
     """Indicator: Linear Regression"""
     # Validate arguments
     close = verify_series(close)
     length = int(length) if length and length > 0 else 14
-    min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
+    min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
     offset = get_offset(offset)
-    angle = kwargs.pop('angle', False)
-    intercept = kwargs.pop('intercept', False)
-    degrees = kwargs.pop('degrees', False)
-    r = kwargs.pop('r', False)
-    slope = kwargs.pop('slope', False)
-    tsf = kwargs.pop('tsf', False)
+    angle = kwargs.pop("angle", False)
+    intercept = kwargs.pop("intercept", False)
+    degrees = kwargs.pop("degrees", False)
+    r = kwargs.pop("r", False)
+    slope = kwargs.pop("slope", False)
+    tsf = kwargs.pop("tsf", False)
 
     # Calculate Result
     x = range(1, length + 1) # [1, 2, ..., n] from 1 to n keeps Sum(xy) low
@@ -54,10 +54,10 @@ def linreg(close, length=None, offset=None, **kwargs):
         linreg = linreg.shift(offset)
 
     # Handle fills
-    if 'fillna' in kwargs:
-        linreg.fillna(kwargs['fillna'], inplace=True)
-    if 'fill_method' in kwargs:
-        linreg.fillna(method=kwargs['fill_method'], inplace=True)
+    if "fillna" in kwargs:
+        linreg.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        linreg.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
     linreg.name = f"LR"
@@ -66,7 +66,7 @@ def linreg(close, length=None, offset=None, **kwargs):
     if angle:     linreg.name += "a"
     if r:         linreg.name += "r"
     linreg.name += f"_{length}"
-    linreg.category = 'overlap'
+    linreg.category = "overlap"
 
     return linreg
 
@@ -104,12 +104,12 @@ Args:
     offset (int): How many periods to offset the result.  Default: 0
 
 Kwargs:
-    angle (bool, optional): Default: False.  If True, returns the angle of the slope in radians
-    degrees (bool, optional): Default: False.  If True, returns the angle of the slope in degrees
-    intercept (bool, optional): Default: False.  If True, returns the angle of the slope in radians
-    r (bool, optional): Default: False.  If True, returns it's correlation 'r'
-    slope (bool, optional): Default: False.  If True, returns the slope
-    tsf (bool, optional): Default: False.  If True, returns the Time Series Forecast value.
+    angle (bool, optional): Default: False. If True, returns the angle of the slope in radians
+    degrees (bool, optional): Default: False. If True, returns the angle of the slope in degrees
+    intercept (bool, optional): Default: False. If True, returns the angle of the slope in radians
+    r (bool, optional): Default: False. If True, returns it's correlation 'r'
+    slope (bool, optional): Default: False. If True, returns the slope
+    tsf (bool, optional): Default: False. If True, returns the Time Series Forecast value.
     fillna (value, optional): pd.DataFrame.fillna(value)
     fill_method (value, optional): Type of fill method
 

tests/test_ext_indicator_momentum.py

@@ -49,6 +49,11 @@ class TestMomentumExtension(TestCase):
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(self.data.columns[-1], "CCI_14_0.015")
 
+    def test_cfo_ext(self):
+        self.data.ta.cfo(append=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(self.data.columns[-1], "CFO_9")
+
     def test_cg_ext(self):
         self.data.ta.cg(append=True)
         self.assertIsInstance(self.data, DataFrame)

tests/test_indicator_momentum.py

@@ -119,6 +119,11 @@ class TestMomentum(TestCase):
             except Exception as ex:
                 error_analysis(result, CORRELATION, ex)
 
+    def test_cfo(self):
+        result = pandas_ta.cfo(self.close)
+        self.assertIsInstance(result, Series)
+        self.assertEqual(result.name, "CFO_9")
+
     def test_cg(self):
         result = pandas_ta.cg(self.close)
         self.assertIsInstance(result, Series)