# Source code for pandas_ta.overlap.fwma

```# -*- coding: utf-8 -*-
from pandas_ta.utils import fibonacci, get_offset, verify_series, weights

[docs]def fwma(close, length=None, asc=None, offset=None, **kwargs):
"""Indicator: Fibonacci's Weighted Moving Average (FWMA)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
asc = asc if asc else True
close = verify_series(close, length)
offset = get_offset(offset)

if close is None: return

# Calculate Result
fibs = fibonacci(n=length, weighted=True)
fwma = close.rolling(length, min_periods=length).apply(weights(fibs), raw=True)

# Offset
if offset != 0:
fwma = fwma.shift(offset)

# Handle fills
if "fillna" in kwargs:
fwma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
fwma.fillna(method=kwargs["fill_method"], inplace=True)

# Name & Category
fwma.name = f"FWMA_{length}"
fwma.category = "overlap"

return fwma

fwma.__doc__ = \
"""Fibonacci's Weighted Moving Average (FWMA)

Fibonacci's Weighted Moving Average is similar to a Weighted Moving Average
(WMA) where the weights are based on the Fibonacci Sequence.

Source: Kevin Johnson

Calculation:
Default Inputs:
length=10,

def weights(w):
def _compute(x):
return np.dot(w * x)
return _compute

fibs = utils.fibonacci(length - 1)
FWMA = close.rolling(length)_.apply(weights(fibs), raw=True)

Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 10
asc (bool): Recent values weigh more. 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.Series: New feature generated.
"""
```