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Göstergeler

Fourier extrapolation of price - MetaTrader 5 için gösterge

Yayınlayan:
Vladimir
Görüntülemeler:
26147
Derecelendirme:
(64)
Yayınlandı:
2010.07.05 14:14
Güncellendi:
2016.11.22 07:32
Bu koda dayalı bir robota veya göstergeye mi ihtiyacınız var? Freelance üzerinden sipariş edin Freelance'e git

A multi-harmonic (or multi-tone) trigonometric model of a price series x[i], i=1..n, is given by:

x[i] = m + Sum( a[h]*Cos(w[h]*i) + b[h]*Sin(w[h]*i), h=1..H )

where:

  • x[i] - past price at i-th bar, total n past prices;
  • m - bias;
  • a[h] and b[h] - scaling coefficients of harmonics;
  • w[h] - frequency of a harmonic;
  • h - harmonic number;
  • H - total number of fitted harmonics.

Fitting this model means finding m, a[h], b[h], and w[h] that make the modeled values to be close to real values. Finding the harmonic frequencies w[h] is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.

This indicator uses the Quinn-Fernandes algorithm to find the harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic, the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.

The indicator has the following input parameters:

  • Npast - number of past bars, to which trigonometric series is fitted;
  • Nfut - number of predicted future bars;
  • Nharm - total number of harmonics in model;
  • FreqTOL - tolerance of frequency calculations.

The indicator plots two curves: the blue curve indicates modeled past values and the red curve indicates the modeled future values.

Fourier extrapolation of price

AR extrapolation of price AR extrapolation of price

This indicator uses an autoregresive model to extrapolate prices

Linear regression slope Linear regression slope

Linear regression slope normalized to SMA.

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Price prediction by Nearest Neighbor Price prediction by Nearest Neighbor

This indicator uses the Nearest Neighbor clustering technique, also called k-NN, to search for the most similar pattern in history and use its past prices as predictions of the current pattern future prices.