def bbands(close, length=None, std=None, mamode=None, offset=None, **kwargs):
"""Indicator: Bollinger Bands (BBANDS)"""
# Validate arguments
close = verify_series(close)
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"
offset = get_offset(offset)
# Calculate Result
standard_deviation = stdev(close=close, length=length)
deviations = std * standard_deviation
mid = ma(mamode, close, length=length, **kwargs)
lower = mid - deviations
upper = mid + deviations
bandwidth = 100 * (upper - lower) / mid
# Offset
if offset != 0:
lower = lower.shift(offset)
mid = mid.shift(offset)
upper = upper.shift(offset)
bandwidth = 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)
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)
# 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}"
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
}
bbandsdf = DataFrame(data)
bbandsdf.name = f"BBANDS_{length}_{std}"
bbandsdf.category = mid.category
return bbandsdf
def bbands(close, length=None, std=None, mamode=None, offset=None, **kwargs):
"""Indicator: Bollinger Bands (BBANDS)"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 5
min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
std = float(std) if std and std > 0 else 2.
mamode = mamode.lower() if mamode else "sma"
offset = get_offset(offset)
# Calculate Result
standard_deviation = stdev(close=close, length=length)
deviations = std * standard_deviation
if mamode is None or mamode == "sma":
mid = sma(close=close, length=length)
elif mamode == "ema":
mid = ema(close=close, length=length, **kwargs)
lower = mid - deviations
upper = mid + deviations
# Offset
if offset != 0:
lower = lower.shift(offset)
mid = mid.shift(offset)
upper = upper.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)
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)
# Name and Categorize it
lower.name = f"BBL_{length}_{std}"
mid.name = f"BBM_{length}_{std}"
upper.name = f"BBU_{length}_{std}"
mid.category = upper.category = lower.category = "volatility"
# Prepare DataFrame to return
data = {lower.name: lower, mid.name: mid, upper.name: upper}
bbandsdf = DataFrame(data)
bbandsdf.name = f"BBANDS_{length}_{std}"
bbandsdf.category = "volatility"
return bbandsdf
def _rvi(source, length, scalar, mode, drift):
"""RVI"""
std = stdev(source, length)
pos, neg = unsigned_differences(source, amount=drift)
pos_std = pos * std
neg_std = neg * std
pos_avg = ma(mode, pos_std, length=length)
neg_avg = ma(mode, neg_std, length=length)
result = scalar * pos_avg
result /= pos_avg + neg_avg
return result
def rvi_(source, length, scalar, mamode, drift):
"""RVI"""
std = stdev(source, length)
pos, neg = unsigned_differences(source, amount=drift)
pos_std = pos * std
neg_std = neg * std
if mamode == 'sma':
pos_avg = sma(pos_std, length)
neg_avg = sma(neg_std, length)
else: # 'ema'
pos_avg = ema(pos_std, length)
neg_avg = ema(neg_std, length)
result = scalar * pos_avg
result /= pos_avg + neg_avg
return result
def bbands(close,
length=None,
std=None,
mamode=None,
ddof=0,
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)
if close is None: return
# Calculate Result
if Imports["talib"]:
from talib import BBANDS
upper, mid, lower = BBANDS(close, length)
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