mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-02 11:37:42 +00:00
Miha Kralj
24e86d762a
- Updated BBWN, BBWP, CCV, CV, CVI, EWMA, GKV, HLV, HV, Jvolty, JVOLTYN, MASSI, NATR, RSV, RV, RVI, TR, UI, VOV, VR, YZV indicators with documentation links. - Added documentation links for Aberration, Acceleration Bands, Andrews' Pitchfork, Adaptive Price Zone, ATR Bands, Bollinger Bands, Center of Gravity, Donchian Channels, Decay Min-Max Channel, Detrended Synthetic Price, EACP, EBSW, HOMOD, Jurik Volatility Bands, Keltner Channel, MA Envelope, Min-Max Channel, Price Channel, Regression Channels, Standard Deviation Channel, Stoller Average Range Channel, Super Trend Bands, Ultimate Bands, Ultimate Channel, VWAP Bands, and VWAP with Standard Deviation Bands.
68 lines
2.5 KiB
Plaintext
68 lines
2.5 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Entropy (ENTROPY)", "ENTROPY", overlay=false)
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//@function Calculate normalized Shannon entropy of a series over a lookback period.
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//@param source series<float> Input data series. NA values are ignored.
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//@param length int Lookback period (>= 1).
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//@returns series<float> Normalized entropy value [0, 1], or na if insufficient data.
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//@optimized for performance and dirty data
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entropy(source, length) =>
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if length < 1
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runtime.error("Length must be >= 1")
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var float validMin = na
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var float validMax = na
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var int validCount = 0
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for i = 0 to length - 1
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val = source[i]
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if not na(val)
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if na(validMin)
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validMin := val
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validMax := val
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else
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validMin := math.min(validMin, val)
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validMax := math.max(validMax, val)
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validCount += 1
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var float normalizedEntropy = na
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if validCount < 2
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normalizedEntropy := na
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else
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valueRange = validMax - validMin
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if valueRange <= 1e-10
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normalizedEntropy := 0.0
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else
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bins = math.min(math.max(validCount, 2), 100)
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int[] freq = array.new_int(bins, 0)
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float sumOfValidPoints = 0.0
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for i = 0 to length - 1
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val = source[i]
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if not na(val)
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normVal = (val - validMin) / valueRange
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bucket = math.floor(math.min(math.max(normVal, 0.0), 1.0 - 1e-10) * bins)
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safeBucket = math.max(0, math.min(bucket, bins - 1))
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array.set(freq, safeBucket, array.get(freq, safeBucket) + 1)
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sumOfValidPoints += 1
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float entropySumComponent = 0.0
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if sumOfValidPoints > 0
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for i = 0 to bins - 1
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count = array.get(freq, i)
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if count > 0
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p = count / sumOfValidPoints
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entropySumComponent += -p * math.log(p)
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maxEntropy = math.log(bins)
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normalizedEntropy := maxEntropy > 1e-10 ? entropySumComponent / maxEntropy : 0.0
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normalizedEntropy
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// ---------- Main loop ----------
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// Inputs
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i_source = input.source(close, "Source")
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i_length = input.int(14, "Length", minval=1)
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// Calculation
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entropyValue = entropy(i_source, i_length)
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// Plot
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plot(entropyValue, "Entropy", color=color.blue, color=color.yellow, linewidth=2)
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