mirror of
https://github.com/wassname/pandas-ta.git
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James Sawyer
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from pandas_ta.overlap import ema
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from pandas_ta.utils import get_offset, verify_series
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def eri(high, low, close, length=None, offset=None, **kwargs):
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"""Indicator: Elder Ray Index (ERI)"""
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# Validate arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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length = int(length) if length and length > 0 else 13
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offset = get_offset(offset)
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# Calculate Result
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ema_ = ema(close, length)
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bull = high - ema_
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bear = low - ema_
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# Offset
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if offset != 0:
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bull = bull.shift(offset)
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bear = bear.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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bull.fillna(kwargs["fillna"], inplace=True)
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bear.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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bull.fillna(method=kwargs["fill_method"], inplace=True)
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bear.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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bull.name = f"BULLP_{length}"
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bear.name = f"BEARP_{length}"
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bull.category = bear.category = "momentum"
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# Prepare DataFrame to return
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data = {bull.name: bull, bear.name: bear}
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df = DataFrame(data)
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df.name = f"ERI_{length}"
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df.category = bull.category
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return df
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eri.__doc__ = """Elder Ray Index (ERI)
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Elder's Bulls Ray Index contains his Bull and Bear Powers. Which are useful ways
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to look at the price and see the strength behind the market. Bull Power
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measures the capability of buyers in the market, to lift prices above an average
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consensus of value.
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Bears Power measures the capability of sellers, to drag prices below an average
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consensus of value. Using them in tandem with a measure of trend allows you to
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identify favourable entry points. We hope you've found this to be a useful
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discussion of the Bulls and Bears Power indicators.
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Sources:
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https://admiralmarkets.com/education/articles/forex-indicators/bears-and-bulls-power-indicator
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Calculation:
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Default Inputs:
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length=13
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EMA = Exponential Moving Average
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BULLPOWER = high - EMA(close, length)
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BEARPOWER = low - EMA(close, length)
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Args:
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series): Series of 'close's
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length (int): It's period. Default: 14
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.DataFrame: bull power and bear power columns.
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"""
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