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https://github.com/mihakralj/QuanTAlib.git
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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.
75 lines
3.4 KiB
Plaintext
75 lines
3.4 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Relative Absolute Error (RAE)", "RAE")
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//@function Calculates Relative Absolute 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 RAE value averaged over the specified period using SMA
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rae(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 abs_error = math.abs(source1 - source2)
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float abs_baseline_error = math.abs(source1 - mean_source1)
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var float sum_abs_error = 0.0
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var float[] buffer_abs_error = array.new_float(p, na)
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var int head_abs_error = 0
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var int valid_count_abs_error = 0
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float oldest_abs_error = array.get(buffer_abs_error, head_abs_error)
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if not na(oldest_abs_error)
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sum_abs_error := sum_abs_error - oldest_abs_error
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valid_count_abs_error := valid_count_abs_error - 1
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if not na(abs_error)
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sum_abs_error := sum_abs_error + abs_error
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valid_count_abs_error := valid_count_abs_error + 1
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array.set(buffer_abs_error, head_abs_error, abs_error)
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head_abs_error := (head_abs_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(abs_baseline_error)
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sum_baseline_error := sum_baseline_error + abs_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, abs_baseline_error)
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head_baseline_error := (head_baseline_error + 1) % p
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float total_abs_error = valid_count_abs_error > 0 ? sum_abs_error : abs_error
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float total_baseline_error = valid_count_baseline_error > 0 ? sum_baseline_error : abs_baseline_error
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total_baseline_error != 0 ? total_abs_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 = rae(i_source1, i_source2, i_period)
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// Plot
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plot(error, "RAE", 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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