OPEN-SOURCE SCRIPT

Nearest Neighbor Extrapolation of Price [Loxx]

I wasn't going to post this because I don't like how this calculates by puling in the Open price, but I'm posting it anyway. This does work in it's current form but there is a. better way to do this. I'll revisit this in the future.

Anyway...

The k-Nearest Neighbor algorithm (k-NN) searches for k past patterns (neighbors) that are most similar to the current pattern and computes the future prices based on weighted voting of those neighbors. This indicator finds only one nearest neighbor. So, in essence, it is a 1-NN algorithm. It uses the Pearson correlation coefficient between the current pattern and all past patterns as the measure of distance between them. Also, this version of the nearest neighbor indicator gives larger weights to most recent prices while searching for the closest pattern in the past. It uses a weighted correlation coefficient, whose weight decays linearly from newer to older prices within a price pattern.

This indicator also includes an error window that shows whether the calculation is valid. If it's green and says "Passed", then the calculation is valid, otherwise it'll show a red background and and error message.

Inputs
  • Npast - number of past bars in a pattern;
  • Nfut -number of future bars in a pattern (must be < Npast).
  • lastbar - How many bars back to start forecast? Useful to show past prediction accuracy
  • barsbark - This prevents Pine from trying to calculate on all past bars


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correlationestimationextrapolationextrapolatorforecastforecastingpearsoncorrelationpredictionTrend Analysis

Script de código abierto

Siguiendo fielmente el espíritu de TradingView, el autor de este script lo ha publicado en código abierto, permitiendo que otros traders puedan entenderlo y verificarlo. ¡Olé por el autor! Puede utilizarlo de forma gratuita, pero tenga en cuenta que la reutilización de este código en la publicación se rige por las Normas internas. Puede añadir este script a sus favoritos y usarlo en un gráfico.

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