# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta import Imports
from pandas_ta.overlap import ma
from pandas_ta.statistics import stdev
from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
[docs]def bbands(close, length=None, std=None, ddof=0, mamode=None, talib=None, offset=None, **kwargs):
"""Indicator: Bollinger Bands (BBANDS)"""
# Validate arguments
length = int(length) if length and length > 0 else 5
std = float(std) if std and std > 0 else 2.0
mamode = mamode if isinstance(mamode, str) else "sma"
ddof = int(ddof) if ddof >= 0 and ddof < length else 1
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import BBANDS
upper, mid, lower = BBANDS(close, length, std, std, tal_ma(mamode))
else:
standard_deviation = stdev(close=close, length=length, ddof=ddof)
deviations = std * standard_deviation
# deviations = std * standard_deviation.loc[standard_deviation.first_valid_index():,]
mid = ma(mamode, close, length=length, **kwargs)
lower = mid - deviations
upper = mid + deviations
ulr = non_zero_range(upper, lower)
bandwidth = 100 * ulr / mid
percent = non_zero_range(close, lower) / ulr
# Offset
if offset != 0:
lower = lower.shift(offset)
mid = mid.shift(offset)
upper = upper.shift(offset)
bandwidth = bandwidth.shift(offset)
percent = bandwidth.shift(offset)
# Handle fills
if "fillna" in kwargs:
lower.fillna(kwargs["fillna"], inplace=True)
mid.fillna(kwargs["fillna"], inplace=True)
upper.fillna(kwargs["fillna"], inplace=True)
bandwidth.fillna(kwargs["fillna"], inplace=True)
percent.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
lower.fillna(method=kwargs["fill_method"], inplace=True)
mid.fillna(method=kwargs["fill_method"], inplace=True)
upper.fillna(method=kwargs["fill_method"], inplace=True)
bandwidth.fillna(method=kwargs["fill_method"], inplace=True)
percent.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
lower.name = f"BBL_{length}_{std}"
mid.name = f"BBM_{length}_{std}"
upper.name = f"BBU_{length}_{std}"
bandwidth.name = f"BBB_{length}_{std}"
percent.name = f"BBP_{length}_{std}"
upper.category = lower.category = "volatility"
mid.category = bandwidth.category = upper.category
# Prepare DataFrame to return
data = {
lower.name: lower, mid.name: mid, upper.name: upper,
bandwidth.name: bandwidth, percent.name: percent
}
bbandsdf = DataFrame(data)
bbandsdf.name = f"BBANDS_{length}_{std}"
bbandsdf.category = mid.category
return bbandsdf
bbands.__doc__ = \
"""Bollinger Bands (BBANDS)
A popular volatility indicator by John Bollinger.
Sources:
https://www.tradingview.com/wiki/Bollinger_Bands_(BB)
Calculation:
Default Inputs:
length=5, std=2, mamode="sma", ddof=0
EMA = Exponential Moving Average
SMA = Simple Moving Average
STDEV = Standard Deviation
stdev = STDEV(close, length, ddof)
if "ema":
MID = EMA(close, length)
else:
MID = SMA(close, length)
LOWER = MID - std * stdev
UPPER = MID + std * stdev
BANDWIDTH = 100 * (UPPER - LOWER) / MID
PERCENT = (close - LOWER) / (UPPER - LOWER)
Args:
close (pd.Series): Series of 'close's
length (int): The short period. Default: 5
std (int): The long period. Default: 2
ddof (int): Degrees of Freedom to use. Default: 0
mamode (str): See ```help(ta.ma)```. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
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: lower, mid, upper, bandwidth, and percent columns.
"""