Custom Indicator Arnaud Legoux Moving Average (Vectorised or Nan-Vec): Help Needed

Ender Kina

Have anyone done an indicator on this that they would be willing to share? I'm interested in both Normal and Vectorised approaches.
(https://stackoverflow.com/questions/46990098/arnaud-legoux-moving-average-and-numpy)
I am not experienced in sub-classing/inheritance in Python
Any help would be highly appreciated. Thx in advance!

Ender Kina

Anybody has any custom indicator examples which includes numpy calculations? I would be appretiated if you share so i can figure out how to convert the code below.

def NPALMA(pnp_array, a) :

    length = a
    # just some number (6.0 is useful)
    sigma = 6
    # sensisitivity (close to 1) or smoothness (close to 0)
    offset = 0.85

    asize = length - 1
    m = offset * asize
    s = length  / sigma
    dss = 2 * s * s

    alma = np.zeros(pnp_array.shape)
    wtd_sum = np.zeros(pnp_array.shape)

    for l in range(len(pnp_array)):
        if l >= asize:
            for i in range(length):
                im = i - m
                wtd = np.exp( -(im * im) / dss)
                alma[l] += pnp_array[l - length + i] * wtd
                wtd_sum[l] += wtd
            alma[l] = alma[l] / wtd_sum[l]

    return alma

Mark Lester Bolotaolo

@Ender-Kina were you able to solve this?

Mariano Volpedo

@Ender-Kina this my ALMA implementation as backtrader indicator

class ALMA(bt.Indicator):
    lines = ('alma',)

    params = dict(
                    period=40,
                    sigma=6,
                    offset=1,
                )
    
    '''
    ALMA - Arnaud Legoux Moving Average,
    http://www.financial-hacker.com/trend-delusion-or-reality/
    https://github.com/darwinsys/Trading_Strategies/blob/master/ML/Features.py
      ''' 
    def __init__(self):

        self.asize = self.p.period - 1
        self.m = self.p.offset * self.asize
        self.s = self.p.period  / self.p.sigma
        self.dss = 2 * self.s * self.s

    def next(self):
        try:
            wtd_sum = 0
            self.l.alma[0] = 0
            if len(self) >= self.asize:
                for i in range(self.p.period):
                    im = i - self.m
                    wtd = np.exp( -(im * im) / self.dss)
                    self.l.alma[0] += self.data[0 - self.p.period + i] * wtd
                    wtd_sum += wtd
                self.l.alma[0] = self.l.alma[0] / wtd_sum
                print(self.l.alma[0])

        except TypeError:
            self.l.alma[0] = 0
            return