wma#

wma(close, length=None, asc=None, talib=None, offset=None, **kwargs)[source]#

Weighted Moving Average (WMA)

The Weighted Moving Average where the weights are linearly increasing and the most recent data has the heaviest weight.

Sources:

https://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average

Calculation:

Default Inputs:: length=10, asc=True

total_weight = 0.5 * length * (length + 1) weights_ = [1, 2, …, length + 1] # Ascending weights = weights if asc else weights[::-1]

def linear_weights(w):

def _compute(x):: return (w * x).sum() / total_weight

return _compute

WMA = close.rolling(length)_.apply(linear_weights(weights), 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 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.Series: New feature generated.