Stationary Extrapolated Levels Oscillator

17 de jun. de 2019

Introduction

The oscillator version of the stationary extrapolated levels indicator. The methodology behind the extrapolated levels where to minimize the risk of making a decision based only on a forecast, therefore the indicator plotted levels in order to determine possible reversal points, signals where generated when the detrended series crossed over/under those levels.

The Indicator

First we detrend the price, this is because forecasting the trend is often harder than a series without trend (stationarity > non-stationarity), then we forecast the detrended price with a linear extrapolation over a period of length and apply a max/min filter twice to the forecast, the max/min filters are just the highest and lowest function in pine. So the max/min filter have lag length/2, by applying it two times we have a lag of length which is the period of the forecast. Because we use highest and lowest we can apply min-max normalization in the form of :

x' = (x - min(x, min'))/(max(x,max') - min(x, min'))

where x is the detrended price, max' the highest of the forecast of x and min' the lowest of the forecast of x. This result in a scaled oscillator in a range of (1,0),

When the indicator is equal to 1 or 0 there are high chances of reversals, more in depth this mean that the detrended price have crossed the highest/lowest of the forecast, when the indicator is equal to 0 or 1 for a long time this mean that the forecast was quite inaccurate, you can minimize risk by focusing on the cross between the detrended price and the 0.8/0.2 levels.

Conclusion

I've shown an oscillator version of my previous "Stationary extrapolated levels" indicator, the method involving taking the highest and lowest of the forecast is a great way to minimize the risk involved by time-series forecasting driven decisions. So i hope you find an use to it.

Thanks for reading !

17 de dez. de 2019

Notas de Lançamento

- v4
- additional changes