PNR applied to the time frequency of signals
We've been exploring PNR applied to price and to indicators including moving averages, oscillators and even QFL.
PNR helps us identifying how "rare" a value is and therefore spotting opportunities.
But what if we used PNR to determine how rare is that a signal hasn't triggered in the past X candles?
Introducing the time PNR option.
Example use:
1) As the source select any indicator that prints signals in the chart, for example an indicator that prints 1 every time RSI is crossing above 30. Any signal would do as long as it prints with certain frequency. The external source indicator must be in the same chart.
2) Set the low to 99 and the high to 100
3) Enable the time option.
4) Make sure that the time value matches the value that the signal prints (usually 1)
Notice how the indicator prints some triangles representing how long its taking for the signal to print another signal and thanks to the rank of percentiles you'll be able to tell based on PNR statistics if its taking "too long", if its deviating from the usual frequency of the signal. 🦜
PNR helps us identifying how "rare" a value is and therefore spotting opportunities.
But what if we used PNR to determine how rare is that a signal hasn't triggered in the past X candles?
Introducing the time PNR option.
Example use:
1) As the source select any indicator that prints signals in the chart, for example an indicator that prints 1 every time RSI is crossing above 30. Any signal would do as long as it prints with certain frequency. The external source indicator must be in the same chart.
2) Set the low to 99 and the high to 100
3) Enable the time option.
4) Make sure that the time value matches the value that the signal prints (usually 1)
Notice how the indicator prints some triangles representing how long its taking for the signal to print another signal and thanks to the rank of percentiles you'll be able to tell based on PNR statistics if its taking "too long", if its deviating from the usual frequency of the signal. 🦜
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