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pandas-ta/pandas_ta/momentum/bias.py
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2020-10-01 16:18:01 +01:00

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Python

# -*- coding: utf-8 -*-
from ..overlap.ema import ema
from ..overlap.hma import hma
from ..overlap.sma import sma
from ..overlap.rma import rma
from ..overlap.wma import wma
from ..utils import get_offset, verify_series, zero
def bias(close, length=None, mamode=None, offset=None, **kwargs):
"""Indicator: Bias (BIAS)"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 26
mamode = mamode.lower() if mamode else None
offset = get_offset(offset)
# Calculate Result
if mamode is None or mamode == "sma":
ma = sma(close, length=length, **kwargs)
if mamode == "ema":
ma = ema(close, length=length, **kwargs)
if mamode == "hma":
ma = hma(close, length=length, **kwargs)
if mamode == "rma":
ma = rma(close, length=length, **kwargs)
if mamode == "wma":
ma = wma(close, length=length, **kwargs)
bias = (close / ma) - 1
# Offset
if offset != 0:
bias = bias.shift(offset)
# Handle fills
if "fillna" in kwargs:
bias.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
bias.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
bias.name = f"BIAS_{ma.name}"
bias.category = "momentum"
return bias
bias.__doc__ = """Bias (BIAS)
Rate of change between the source and a moving average.
Sources:
Few internet resources on definitive definition.
Request by Github user homily, issue #46
Calculation:
Default Inputs:
length=26, MA='sma'
BIAS = (close - MA(close, length)) / MA(close, length)
= (close / MA(close, length)) - 1
Args:
close (pd.Series): Series of 'close's
length (int): The period. Default: 26
mamode (str): Options: 'ema', 'hma', 'rma', 'sma', 'wma'. Default: 'sma'
drift (int): The short period. Default: 1
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.
"""