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.
87 lines
3.1 KiB
Plaintext
87 lines
3.1 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Hurst Exponent (HURST)", "HURST", overlay=false, precision=4)
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//@function Calculates the Hurst Exponent for a given series and lookback period.
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//@param source series float The input series.
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//@param length int The lookback period for Hurst Exponent calculation.
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//@returns series float The Hurst Exponent value.
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hurst(series float source, simple int length) =>
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if length <= 10
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runtime.error("Length must be greater than 10 for Hurst Exponent.")
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na
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log_returns = math.log(source / source[1])
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min_n = 10
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max_n = length / 2
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if max_n < min_n
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runtime.error("Length too short for sub-period division.")
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na
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array<float> log_n_values = array.new_float(0)
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array<float> log_rs_values = array.new_float(0)
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for n = min_n to max_n
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if n == 0
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continue
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num_sub_periods = math.floor(length / n)
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if num_sub_periods == 0
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continue
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rs_sum = 0.0
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for i = 0 to num_sub_periods - 1
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start_index = i * n
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float[] sub_period_returns = array.new_float(n)
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for j = 0 to n - 1
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array.set(sub_period_returns, j, log_returns[start_index + j])
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sub_period_sum = 0.0
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for k_val = 0 to n - 1
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sub_period_sum += array.get(sub_period_returns, k_val)
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sub_mean = sub_period_sum / n
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float[] cum_dev = array.new_float(n)
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current_sum = 0.0
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for j = 0 to n - 1
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current_sum += (array.get(sub_period_returns, j) - sub_mean)
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array.set(cum_dev, j, current_sum)
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range_val = array.max(cum_dev) - array.min(cum_dev)
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variance_sum = 0.0
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for j = 0 to n - 1
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variance_sum += math.pow(array.get(sub_period_returns, j) - sub_mean, 2)
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std_dev_val_corrected = math.sqrt(variance_sum / n)
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if std_dev_val_corrected > 0
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rs_sum += range_val / std_dev_val_corrected
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if num_sub_periods > 0
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avg_rs = rs_sum / num_sub_periods
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if avg_rs > 0
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array.push(log_n_values, math.log(n))
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array.push(log_rs_values, math.log(avg_rs))
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if array.size(log_n_values) < 2
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na
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else
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m = array.size(log_n_values)
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sum_x = 0.0
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sum_y = 0.0
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sum_xy = 0.0
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sum_x_sq = 0.0
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for i = 0 to m - 1
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xi = array.get(log_n_values, i)
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yi = array.get(log_rs_values, i)
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sum_x += xi
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sum_y += yi
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sum_xy += xi * yi
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sum_x_sq += xi * xi
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denominator = m * sum_x_sq - math.pow(sum_x, 2)
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if denominator == 0
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na
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else
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(m * sum_xy - sum_x * sum_y) / denominator
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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(100, "Length", minval=20, tooltip="Lookback period for Hurst Exponent calculation. Min 20.")
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// Calculation
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hurstValue = hurst(i_source, i_length)
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// Plot
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plot(hurstValue, "Hurst Exponent", color=color.yellow, linewidth=2)
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