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Miha Kralj
2024-10-13 17:18:31 -07:00
parent 2236f5f483
commit bbefc72d73
65 changed files with 669 additions and 1087 deletions
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lib/errors/Mda.cs
+17 -79
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@@ -1,65 +1,36 @@
namespace QuanTAlib;
/// <summary>
/// Represents a Mean Directional Accuracy calculator that measures the average accuracy
/// of predicted directional changes compared to actual directional changes.
/// </summary>
/// <remarks>
/// The Mda class calculates the Mean Directional Accuracy using a circular buffer
/// to efficiently manage the data points within the specified period.
/// Mean Directional Accuracy is useful in financial analysis for evaluating the performance
/// of forecasting models in predicting the direction of price movements.
/// </remarks>
public class Mda : AbstractBase
{
private readonly CircularBuffer _actualBuffer;
private readonly CircularBuffer _forecastBuffer;
private readonly CircularBuffer _predictedBuffer;
/// <summary>
/// Initializes a new instance of the Mda class with the specified period.
/// </summary>
/// <param name="period">The period over which to calculate the Mean Directional Accuracy.</param>
/// <exception cref="ArgumentOutOfRangeException">
/// Thrown when period is less than 2.
/// </exception>
public Mda(int period)
{
if (period < 2)
if (period < 1)
{
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 2.");
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
}
WarmupPeriod = 1;
WarmupPeriod = period;
_actualBuffer = new CircularBuffer(period);
_forecastBuffer = new CircularBuffer(period);
_predictedBuffer = new CircularBuffer(period);
Name = $"Mda(period={period})";
Init();
}
/// <summary>
/// Initializes a new instance of the Mda class with the specified source and period.
/// </summary>
/// <param name="source">The source object to subscribe to for value updates.</param>
/// <param name="period">The period over which to calculate the Mean Directional Accuracy.</param>
public Mda(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
}
/// <summary>
/// Initializes the Mda instance by clearing the buffers.
/// </summary>
public override void Init()
{
base.Init();
_actualBuffer.Clear();
_forecastBuffer.Clear();
_predictedBuffer.Clear();
}
/// <summary>
/// Manages the state of the Mda instance based on whether new values are being processed.
/// </summary>
/// <param name="isNew">Indicates whether the current inputs are new values.</param>
protected override void ManageState(bool isNew)
{
if (isNew)
@@ -69,20 +40,6 @@ public class Mda : AbstractBase
}
}
/// <summary>
/// Performs the Mean Directional Accuracy calculation for the current period.
/// </summary>
/// <returns>
/// The calculated Mean Directional Accuracy value for the current period.
/// </returns>
/// <remarks>
/// This method calculates the Mean Directional Accuracy using the formula:
/// MDA = (number of correct directional predictions / total number of predictions) * 100
/// A correct directional prediction is when the sign of the actual change matches
/// the sign of the predicted change.
/// The result is expressed as a percentage, where 100% indicates perfect directional accuracy
/// and 50% indicates performance no better than random guessing.
/// </remarks>
protected override double Calculation()
{
ManageState(Input.IsNew);
@@ -90,46 +47,27 @@ public class Mda : AbstractBase
double actual = Input.Value;
_actualBuffer.Add(actual, Input.IsNew);
double forecast = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
_forecastBuffer.Add(forecast, Input.IsNew);
double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
_predictedBuffer.Add(predicted, Input.IsNew);
double mda = 0;
if (_actualBuffer.Count > 1)
if (_actualBuffer.Count > 0)
{
var actualValues = _actualBuffer.GetSpan().ToArray();
var forecastValues = _forecastBuffer.GetSpan().ToArray();
var predictedValues = _predictedBuffer.GetSpan().ToArray();
int correctPredictions = 0;
int totalPredictions = actualValues.Length - 1;
for (int i = 1; i < actualValues.Length; i++)
double sumDirectionalAccuracy = 0;
for (int i = 1; i < _actualBuffer.Count; i++)
{
double actualChange = actualValues[i] - actualValues[i - 1];
double forecastChange = forecastValues[i] - actualValues[i - 1];
if ((actualChange >= 0 && forecastChange >= 0) || (actualChange < 0 && forecastChange < 0))
{
correctPredictions++;
}
double actualDirection = Math.Sign(actualValues[i] - actualValues[i - 1]);
double predictedDirection = Math.Sign(predictedValues[i] - predictedValues[i - 1]);
sumDirectionalAccuracy += (actualDirection == predictedDirection) ? 1 : 0;
}
mda = (double)correctPredictions / totalPredictions * 100;
mda = sumDirectionalAccuracy / (_actualBuffer.Count - 1);
}
IsHot = _actualBuffer.Count > 1; // MDA calc is valid from bar 2
IsHot = _index >= WarmupPeriod;
return mda;
}
/// <summary>
/// Calculates the Mean Directional Accuracy for the given actual and forecast values.
/// </summary>
/// <param name="actual">The actual value.</param>
/// <param name="forecast">The forecast value.</param>
/// <returns>The calculated Mean Directional Accuracy.</returns>
public double Calc(double actual, double forecast)
{
Input = new TValue(DateTime.Now, actual);
Input2 = new TValue(DateTime.Now, forecast);
return Calculation();
}
}