Source code for pandas_ta.momentum.eri

# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series

[docs]def eri(high, low, close, length=None, offset=None, **kwargs): """Indicator: Elder Ray Index (ERI)""" # Validate arguments length = int(length) if length and length > 0 else 13 high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) offset = get_offset(offset) if high is None or low is None or close is None: return # Calculate Result ema_ = ema(close, length) bull = high - ema_ bear = low - ema_ # Offset if offset != 0: bull = bull.shift(offset) bear = bear.shift(offset) # Handle fills if "fillna" in kwargs: bull.fillna(kwargs["fillna"], inplace=True) bear.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: bull.fillna(method=kwargs["fill_method"], inplace=True) bear.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"BULLP_{length}" = f"BEARP_{length}" bull.category = bear.category = "momentum" # Prepare DataFrame to return data = { bull, bear} df = DataFrame(data) = f"ERI_{length}" df.category = bull.category return df
eri.__doc__ = \ """Elder Ray Index (ERI) Elder's Bulls Ray Index contains his Bull and Bear Powers. Which are useful ways to look at the price and see the strength behind the market. Bull Power measures the capability of buyers in the market, to lift prices above an average consensus of value. Bears Power measures the capability of sellers, to drag prices below an average consensus of value. Using them in tandem with a measure of trend allows you to identify favourable entry points. We hope you've found this to be a useful discussion of the Bulls and Bears Power indicators. Sources: Calculation: Default Inputs: length=13 EMA = Exponential Moving Average BULLPOWER = high - EMA(close, length) BEARPOWER = low - EMA(close, length) Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's length (int): It's 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.DataFrame: bull power and bear power columns. """