Computing FIR Filters Using Arrays [WMA Example]Over the years, many FIR filters have been proposed by the Pine community, with the standard way of computing them being `for` loops. The arrival of arrays allows for a new, more efficient way to compute them.
This script provides a template showing how you can compute FIR filters using Pine arrays.
FIR Filters
FIR stands for "Finite Impulse Response", and is associated with types of filters whose impulse response reaches a steady state.
FIR filters are calculated using convolution, or more simply put, using a weighted sum between a set of filter coefficients and past input values over a finite window.
In Pine, FIR filters are generally computed inside a `for` loop executing three processes:
1- Computing the coefficients.
2- Summing all the computed coefficients.
3- Performing the weighted sum between the inputs values and the computed coefficients.
Then we divide the result of our weighted sum by the sum of the coefficients obtained in step 2.
Because the computations inside the `for` loop execute on each bar, execution time can be significant when the calculation of coefficients is complex. This is where arrays are handy, as we can compute the coefficients just once, store them into an array, and use them in a weighted sum without the need to recalculate them over and over. This drastically reduces the computation time required to calculate a FIR filter.
The new `array.sum()` function helps eliminate step 2, thus further decreasing computation time.
How to Use This Template
All you need to do is to put the code that computes your coefficients in the first `for` loop (variable `w`). If the code that computes your coefficients contains more than one line, just make sure your final coefficient is placed in variable `w` (or change the `value` argument in `array.push()`). Another option is to declare a function that computes the coefficients and use it instead of variable `w`.
Look first. Then leap.
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Percentile Nearest Rank Using Arrays [LuxAlgo]The new array feature is extremely powerful, as it will allow pinescript users to do more complex things, or compute existing calculations more efficiently, it will also be possible to shine some light to some already existing functions, one of them being percentile_nearest_rank .
We have been working on this new feature with our pal alexgrover, and made this script which computes a rolling percentile using the nearest rank method.
Settings
Length: Window of the rolling percentile, determine the number of past data to be used.
Percentage: Return the current value if Percentage % of the data fall below that value, the setting is in a range (0,100).
Src: Input source of the indicator.
Usage
A rolling percentile can have many usages when it comes to technical analysis, this is due to its ability to return the value of three common rolling statistics, the rolling median, which can be obtained using a percentage equal to 50, the rolling maximum, obtained with a percentage equal to 100, and the rolling minimum, obtained with a percentage equal to 0.
When we use our rolling percentile as a rolling median, we can obtain a robust estimation of the underlying trend in the price, while using it as a rolling maximum/minimum can allow us to determine if the market is trending, and at which direction. The rolling maximum/minimum is a rolling statistic used to calculate the well known stochastic oscillator and Donchian channel indicator.
We can also compute rolling quartiles, which can be obtained using a percentage of 25 or 75, with one of 25 returning the lower quartile and 75 the upper quartile.
In blue the upper rolling quartile (%75), in orange the lower rolling quartile (%25), both using a window size of 100.
Details
In order to compute a rolling percentile nearest rank, we must first take the most recent length closing prices, then order them in ascending order, we then return the value of the ordered observations at index (percentage/100*length) - 1 (we use - 1 because our array index starts at 0). Indicator
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