using System.Runtime.CompilerServices;
namespace QuanTAlib;

/// <summary>
/// FWMA: Fibonacci Weighted Moving Average
/// A moving average that uses Fibonacci numbers as weights in its calculation. The weights
/// are arranged in reverse order so that recent prices receive higher weights corresponding
/// to larger Fibonacci numbers.
/// </summary>
/// <remarks>
/// The FWMA calculation process:
/// 1. Generates a Fibonacci sequence up to the specified period
/// 2. Reverses the sequence to give higher weights to recent prices
/// 3. Normalizes the weights to sum to 1
/// 4. Applies the weights through convolution
///
/// Key characteristics:
/// - Uses Fibonacci sequence for weight distribution
/// - Recent prices receive higher weights
/// - Natural progression of weights based on the golden ratio
/// - Implemented using efficient convolution operations
///
/// Implementation:
///     Original implementation based on Fibonacci sequence principles
/// </remarks>
public class Fwma : AbstractBase
{
    private readonly Convolution _convolution;

    /// <param name="period">The number of data points used in the FWMA calculation.</param>
    /// <exception cref="ArgumentException">Thrown when period is less than 1.</exception>
    public Fwma(int period)
    {
        if (period < 1)
        {
            throw new System.ArgumentException("Period must be greater than or equal to 1.", nameof(period));
        }
        double[] _kernel = GenerateKernel(period);
        _convolution = new Convolution(_kernel);
        Name = "Fwma";
        WarmupPeriod = period;
        Init();
    }

    /// <param name="source">The data source object that publishes updates.</param>
    /// <param name="period">The number of data points used in the FWMA calculation.</param>
    public Fwma(object source, int period) : this(period)
    {
        var pubEvent = source.GetType().GetEvent("Pub");
        pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
    }

    /// <summary>
    /// Generates the Fibonacci-based convolution kernel for the FWMA calculation.
    /// </summary>
    /// <param name="period">The period for which to generate the kernel.</param>
    /// <returns>An array of normalized Fibonacci-based weights for the convolution operation.</returns>
    [MethodImpl(MethodImplOptions.AggressiveInlining)]
    public static double[] GenerateKernel(int period)
    {
        double[] kernel = new double[period];
        double[] fibSeries = new double[period];

        // Generate Fibonacci series with running sum
        fibSeries[0] = fibSeries[1] = 1;
        double weightSum = 2.0; // Initial sum for first two Fibonacci numbers

        for (int i = 2; i < period; i++)
        {
            fibSeries[i] = fibSeries[i - 1] + fibSeries[i - 2];
            weightSum += fibSeries[i];
        }

        // Calculate inverse of weight sum for normalization
        double invWeightSum = 1.0 / weightSum;

        // Reverse and normalize the series in one pass
        for (int i = 0; i < period; i++)
        {
            kernel[i] = fibSeries[period - 1 - i] * invWeightSum;
        }

        return kernel;
    }

    [MethodImpl(MethodImplOptions.AggressiveInlining)]
    private new void Init()
    {
        base.Init();
        _convolution.Init();
    }

    [MethodImpl(MethodImplOptions.AggressiveInlining)]
    protected override void ManageState(bool isNew)
    {
        if (isNew)
        {
            _lastValidValue = Input.Value;
            _index++;
        }
    }

    protected override double Calculation()
    {
        ManageState(Input.IsNew);

        // Use Convolution for calculation
        var convolutionResult = _convolution.Calc(Input);
        IsHot = _index >= WarmupPeriod;

        return convolutionResult.Value;
    }
}