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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
45 lines
1.7 KiB
Plaintext
45 lines
1.7 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("Root Mean Squared Logarithmic Error (RMSLE)", "RMSLE")
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//@function Calculates Root Mean Squared Logarithmic 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 RMSLE value averaged over the specified period using SMA
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rmsle(series float source1, series float source2, simple int period) =>
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log_squared_error = math.pow(math.log(1 + source1) - math.log(1 + source2), 2)
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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[] buffer = array.new_float(p, na)
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var int head = 0
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var float sum = 0.0
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var int valid_count = 0
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float oldest = array.get(buffer, head)
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if not na(oldest)
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sum := sum - oldest
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valid_count := valid_count - 1
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if not na(log_squared_error)
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sum := sum + log_squared_error
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valid_count := valid_count + 1
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array.set(buffer, head, log_squared_error)
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head := (head + 1) % p
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float msle = valid_count > 0 ? sum / valid_count : log_squared_error
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math.sqrt(msle)
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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 = rmsle(i_source1, i_source2, i_period)
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// Plot
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plot(error, "RMSLE", 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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