mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
74 lines
3.6 KiB
Plaintext
74 lines
3.6 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("Relative Squared Error (RSE)", "RSE")
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//@function Calculates Relative Squared Error between two sources using SMA for averaging
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//@param source1 First series to compare (actual)
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//@param source2 Second series to compare (predicted)
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//@param period Lookback period for error averaging
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//@returns RSE value averaged over the specified period using SMA
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rse(series float source1, series float source2, simple int period) =>
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if period <= 0
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runtime.error("Period must be greater than 0")
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int p = math.min(math.max(1, period), 4000)
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var float sum_source1 = 0.0
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var float[] buffer_source1 = array.new_float(p, na)
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var int head_source1 = 0
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var int valid_count_source1 = 0
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float oldest_source1 = array.get(buffer_source1, head_source1)
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if not na(oldest_source1)
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sum_source1 := sum_source1 - oldest_source1
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valid_count_source1 := valid_count_source1 - 1
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if not na(source1)
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sum_source1 := sum_source1 + source1
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valid_count_source1 := valid_count_source1 + 1
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array.set(buffer_source1, head_source1, source1)
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head_source1 := (head_source1 + 1) % p
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float mean_source1 = valid_count_source1 > 0 ? sum_source1 / valid_count_source1 : source1
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float squared_error = math.pow(source1 - source2, 2)
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float squared_baseline_error = math.pow(source1 - mean_source1, 2)
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var float sum_squared_error = 0.0
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var float[] buffer_squared_error = array.new_float(p, na)
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var int head_squared_error = 0
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var int valid_count_squared_error = 0
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float oldest_squared_error = array.get(buffer_squared_error, head_squared_error)
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if not na(oldest_squared_error)
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sum_squared_error := sum_squared_error - oldest_squared_error
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valid_count_squared_error := valid_count_squared_error - 1
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if not na(squared_error)
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sum_squared_error := sum_squared_error + squared_error
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valid_count_squared_error := valid_count_squared_error + 1
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array.set(buffer_squared_error, head_squared_error, squared_error)
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head_squared_error := (head_squared_error + 1) % p
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var float sum_baseline_error = 0.0
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var float[] buffer_baseline_error = array.new_float(p, na)
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var int head_baseline_error = 0
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var int valid_count_baseline_error = 0
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float oldest_baseline_error = array.get(buffer_baseline_error, head_baseline_error)
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if not na(oldest_baseline_error)
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sum_baseline_error := sum_baseline_error - oldest_baseline_error
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valid_count_baseline_error := valid_count_baseline_error - 1
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if not na(squared_baseline_error)
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sum_baseline_error := sum_baseline_error + squared_baseline_error
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valid_count_baseline_error := valid_count_baseline_error + 1
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array.set(buffer_baseline_error, head_baseline_error, squared_baseline_error)
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head_baseline_error := (head_baseline_error + 1) % p
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float total_squared_error = valid_count_squared_error > 0 ? sum_squared_error : squared_error
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float total_baseline_error = valid_count_baseline_error > 0 ? sum_baseline_error : squared_baseline_error
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total_baseline_error != 0 ? total_squared_error / total_baseline_error : 1.0
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// ---------- Main loop ----------
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// Inputs
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i_source1 = input.source(close, "Source")
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i_period = input.int(100, "Period", minval=1)
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i_source2 = ta.ema(i_source1, i_period)
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// Calculation
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error = rse(i_source1, i_source2, i_period)
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// Plot
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plot(error, "RSE", color=color.new(color.red, 60), linewidth=2, style = plot.style_area)
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plot(i_source2, "EMA", color=color.yellow, linewidth=1, style = plot.style_line, force_overlay = true)
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