# -*- coding: utf-8 -*-
from .roc import roc
from pandas_ta.overlap import wma
from pandas_ta.utils import get_offset, verify_series
[docs]def coppock(close, length=None, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Coppock Curve (COPC)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
fast = int(fast) if fast and fast > 0 else 11
slow = int(slow) if slow and slow > 0 else 14
close = verify_series(close, max(length, fast, slow))
offset = get_offset(offset)
if close is None: return
# Calculate Result
total_roc = roc(close, fast) + roc(close, slow)
coppock = wma(total_roc, length)
# Offset
if offset != 0:
coppock = coppock.shift(offset)
# Handle fills
if "fillna" in kwargs:
coppock.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
coppock.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
coppock.name = f"COPC_{fast}_{slow}_{length}"
coppock.category = "momentum"
return coppock
coppock.__doc__ = \
"""Coppock Curve (COPC)
Coppock Curve (originally called the "Trendex Model") is a momentum indicator
is designed for use on a monthly time scale. Although designed for monthly
use, a daily calculation over the same period can be made, converting the
periods to 294-day and 231-day rate of changes, and a 210-day weighted
moving average.
Sources:
https://en.wikipedia.org/wiki/Coppock_curve
Calculation:
Default Inputs:
length=10, fast=11, slow=14
SMA = Simple Moving Average
MAD = Mean Absolute Deviation
tp = typical_price = hlc3 = (high + low + close) / 3
mean_tp = SMA(tp, length)
mad_tp = MAD(tp, length)
CCI = (tp - mean_tp) / (c * mad_tp)
Args:
close (pd.Series): Series of 'close's
length (int): WMA period. Default: 10
fast (int): Fast ROC period. Default: 11
slow (int): Slow ROC period. Default: 14
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.
"""