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pandas-ta/pandas_ta/volume/efi.py
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Python

# -*- coding: utf-8 -*-
from ..utils import get_drift, get_offset, verify_series
def efi(close, volume, length=None, drift=None, mamode=None, offset=None, **kwargs):
"""Indicator: Elder's Force Index (EFI)"""
# Validate arguments
close = verify_series(close)
volume = verify_series(volume)
length = int(length) if length and length > 0 else 13
min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
drift = get_drift(drift)
mamode = mamode.lower() if mamode else None
offset = get_offset(offset)
# Calculate Result
pv_diff = close.diff(drift) * volume
if mamode == 'sma':
efi = pv_diff.rolling(length, min_periods=min_periods).mean()
else:
efi = pv_diff.ewm(span=length, min_periods=min_periods).mean()
# Offset
if offset != 0:
efi = efi.shift(offset)
# Handle fills
if 'fillna' in kwargs:
efi.fillna(kwargs['fillna'], inplace=True)
if 'fill_method' in kwargs:
efi.fillna(method=kwargs['fill_method'], inplace=True)
# Name and Categorize it
efi.name = f"EFI_{length}"
efi.category = 'volume'
return efi
efi.__doc__ = \
"""Elder's Force Index (EFI)
Elder's Force Index measures the power behind a price movement using price
and volume as well as potential reversals and price corrections.
Sources:
https://www.tradingview.com/wiki/Elder%27s_Force_Index_(EFI)
https://www.motivewave.com/studies/elders_force_index.htm
Calculation:
Default Inputs:
length=20, drift=1, mamode=None
EMA = Exponential Moving Average
SMA = Simple Moving Average
pv_diff = close.diff(drift) * volume
if mamode == 'sma':
EFI = SMA(pv_diff, length)
else:
EFI = EMA(pv_diff, length)
Args:
close (pd.Series): Series of 'close's
volume (pd.Series): Series of 'volume's
length (int): The short period. Default: 13
drift (int): The diff period. Default: 1
mamode (str): Two options: None or 'sma'. Default: None
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.
"""