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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
35a6702b06
Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
54 lines
2.1 KiB
Plaintext
54 lines
2.1 KiB
Plaintext
// Licensed under the Apache License, Version 2.0
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// © mihakralj
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//@version=6
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indicator("Variable Index Dynamic Average (VIDYA)", "VIDYA", overlay=true)
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//@function Calculates VIDYA using adaptive smoothing based on market volatility
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//@param source Series to calculate VIDYA from
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//@param period Length of the smoothing period
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//@param std_period Length of the standard deviation period, defaults to same as period
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//@returns VIDYA value that adapts to market volatility
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//@optimized Uses volatility index calculation with O(n) complexity per bar due to lookback loops
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vidya(series float source, simple int period, simple int std_period=0) =>
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float alpha = 2.0 / (period + 1.0)
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var float vidya = na
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if not na(source)
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int p = std_period > 0 ? std_period : period
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float sum_p = 0.0
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float sumSq_p = 0.0
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float count_p = 0.0
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float sum_5 = 0.0
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float sumSq_5 = 0.0
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float count_5 = 0.0
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for i = 0 to math.max(p, 5) - 1
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if not na(source[i])
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float val = source[i]
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if i < p
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sum_p += val
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sumSq_p += val * val
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count_p += 1
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if i < 5
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sum_5 += val
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sumSq_5 += val * val
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count_5 += 1
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float std = count_p > 0 ? math.sqrt(math.max((sumSq_p / count_p) - (sum_p / count_p) * (sum_p / count_p), 0.0)) : 0.0
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float std_5 = count_5 > 0 ? math.sqrt(math.max((sumSq_5 / count_5) - (sum_5 / count_5) * (sum_5 / count_5), 0.0)) : 0.0
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float vol_idx = std > 0 ? std_5 / std : 1.0
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vol_idx := math.min(math.max(vol_idx, 0.0), 1.0)
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float sc = alpha * vol_idx
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vidya := na(vidya) ? source : source * sc + vidya * (1.0 - sc)
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vidya
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(10, "Period", minval=1)
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i_std_period = input.int(0, "Std Dev Period (0=use Period)", minval=0)
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i_source = input.source(close, "Source")
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// Calculation
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vidya_value = vidya(i_source, i_period, i_std_period)
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// Plot
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plot(vidya_value, "VIDYA", color=color.yellow, linewidth=2)
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