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.
51 lines
2.0 KiB
Plaintext
51 lines
2.0 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Weighted Root Mean Squared Error", "WRMSE", overlay=false)
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//@function Calculates Weighted Root Mean Squared Error
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//@param actual Series of actual values
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//@param predicted Series of predicted/forecast values
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//@param weight Series of weights for each observation
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//@param length Rolling window for calculation
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//@returns WRMSE value in same units as original data
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wrmse(series float actual, series float predicted, series float weight, simple int length) =>
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float epsilon = 1e-10
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// Compute weighted squared error for current bar
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float diff = nz(actual, 0.0) - nz(predicted, 0.0)
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float w = math.max(nz(weight, 1.0), 0.0) // Ensure non-negative weight
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float weightedSqError = w * diff * diff
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// Rolling sums
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float sumWeightedError = ta.sum(weightedSqError, length)
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float sumWeight = ta.sum(w, length)
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// WRMSE = √(Σ(w*e²) / Σ(w))
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float result = sumWeight > epsilon ? math.sqrt(sumWeightedError / sumWeight) : 0.0
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result
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//@function Calculates WRMSE with uniform weights (equivalent to RMSE)
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//@param actual Series of actual values
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//@param predicted Series of predicted/forecast values
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//@param length Rolling window for calculation
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//@returns WRMSE value (same as RMSE when weights are uniform)
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wrmse_uniform(series float actual, series float predicted, simple int length) =>
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wrmse(actual, predicted, 1.0, length)
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// ---------- Main loop ----------
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// Inputs
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i_length = input.int(14, "Length", minval=1)
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i_use_volume_weights = input.bool(false, "Use Volume as Weights", tooltip="When enabled, errors are weighted by volume")
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i_actual = input.source(close, "Actual")
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i_predicted = input.source(open, "Predicted")
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// Calculation
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weight = i_use_volume_weights ? volume : 1.0
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wrmse_value = wrmse(i_actual, i_predicted, weight, i_length)
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// Plot
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plot(wrmse_value, "WRMSE", color=color.yellow, linewidth=2)
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hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)
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