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120 lines
4.0 KiB
C#
120 lines
4.0 KiB
C#
using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// MPE: Mean Percentage Error
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/// A percentage-based error metric that measures the average percentage difference
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/// between actual and predicted values. Like ME, it allows positive and negative
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/// errors to cancel out, but expresses the bias in percentage terms.
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/// </summary>
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/// <remarks>
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/// The MPE calculation process:
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/// 1. Calculates percentage error for each point
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/// 2. Sums all percentage errors (allowing cancellation)
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/// 3. Divides by the number of observations
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///
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/// Key characteristics:
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/// - Scale-independent (percentage-based)
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/// - Can detect systematic bias
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/// - Positive MPE indicates underprediction
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/// - Negative MPE indicates overprediction
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/// - Cannot handle zero actual values
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/// - Errors can cancel out
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///
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/// Formula:
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/// MPE = (1/n) * Σ((actual - predicted) / actual) * 100%
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///
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/// Sources:
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/// https://en.wikipedia.org/wiki/Mean_percentage_error
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/// https://www.statisticshowto.com/mean-percentage-error/
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///
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/// Note: Similar to MAPE but allows error cancellation
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Mpe : AbstractBase
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{
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private readonly CircularBuffer _actualBuffer;
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private readonly CircularBuffer _predictedBuffer;
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/// <param name="period">The number of points over which to calculate the MPE.</param>
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/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public Mpe(int period)
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{
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if (period < 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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}
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WarmupPeriod = period;
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_actualBuffer = new CircularBuffer(period);
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_predictedBuffer = new CircularBuffer(period);
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Name = $"Mpe(period={period})";
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Init();
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}
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/// <param name="source">The data source object that publishes updates.</param>
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/// <param name="period">The number of points over which to calculate the MPE.</param>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public Mpe(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public override void Init()
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{
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base.Init();
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_actualBuffer.Clear();
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_predictedBuffer.Clear();
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_lastValidValue = Input.Value;
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_index++;
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}
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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private static double CalculatePercentageError(double actual, double predicted)
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{
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return actual != 0 ? (actual - predicted) / actual : 0;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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double actual = Input.Value;
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_actualBuffer.Add(actual, Input.IsNew);
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// If no predicted value provided, use mean of actual values
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double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
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_predictedBuffer.Add(predicted, Input.IsNew);
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double mpe = 0;
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if (_actualBuffer.Count > 0)
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{
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ReadOnlySpan<double> actualValues = _actualBuffer.GetSpan();
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ReadOnlySpan<double> predictedValues = _predictedBuffer.GetSpan();
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double sumPercentageError = 0;
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for (int i = 0; i < actualValues.Length; i++)
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{
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sumPercentageError += CalculatePercentageError(actualValues[i], predictedValues[i]);
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}
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mpe = sumPercentageError / actualValues.Length;
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}
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IsHot = _index >= WarmupPeriod;
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return mpe;
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}
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}
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