Using the indicator:
The longer the Entropy measurement the more information you are capturing, so the analogy is, the shorter the signal, the less information you have available to utilize. You'll run into your Nyquist frequencies below a length of 5. I've found values between 9 and 22 work well to gather enough measurements. You also have an averaging summation that measures the weight or importance of the information over the summation period. This is also used for highlighting when you have an information signal above the 5% level (2 sigma) and then can be adjusted using the Percent Rank Variable. Finally, you can plot the individual signals (Price or ) to get another set of measurements to utilize. As can be seen in the chart below, the moves before price (but hopefully you already knew that)
At its core, this is taking the Binary Entropy measurement (using a Bernoulli distribution) for price and . I've subtracted the from the price so that you can use it like a , also for shorter time frames (7, 9, 11) you can get divergences on the histogram. These divergences are primarily due to the weekly nature of the markets (5 days, 10 days is two weeks,...so 9 is measuring the last day of the past two weeks...so 11 is measuring the current day and the past two weeks).
Here are a couple of other examples, assuming you just love BTC , Stocks, or FOREX. I fashioned up a strategy to show the potential of the indicator.
FOREX - (for everyone hopped up on 40X leverage)
You could also add this code to paint bars for a set of rules:
BarColors = input(title="Painting bars", type=input.bool, defval=true)
signal = sma(info2,3)
nColor = BarColors ? signal > 0 and signal >= range ? color.green : signal < 0 and signal <= -range ? color.red : color.blue : na