mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
87 lines
3.1 KiB
Plaintext
87 lines
3.1 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("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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