Source code for pandas_ta.momentum.coppock

# -*- 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 = 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: 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. """