[HandlerName("T3Tilson MA")]
    [HandlerCategory("vvAverages")]
    public class T3Tilson : IDouble2DoubleHandler, IContextUses
    {
        /* _T3Average
         * By Bob Fulks, modified by Alex Matulich 4/2003
         * 
         * The T3 Average is essentially a low-pass filter, as are the traditional moving average
         * and exponential moving average. The T3 Average, however, exhibits a steeper rolloff, 
         * resulting in better filtering of high-frequency noise while better preserving the
         * low-frequency components of a time series.
         * 
         * This function is an EasyLanguage version of the algorithm described in the January 
         * 1998 issue of TASC, p57, "Smoothing Techniques for More Accurate Signals" by Tim 
         * Tillson. It is translated from the MetaStock code presented in the article. 
         * The function allows a variable length as input.
         * 
         * The variable b (also called "Hot") is a damping coefficient. The suggested value of
         * b is 0.7, but this value slightly amplifies low-frequency components. b=0.5 seems 
         * better for having a flat response at low frequencies. A smaller value of b will 
         * result in too much attenuation of low frequencies.
         * 
         * The Length parameter is divided by two to make the T3 Average's lag equivalent to 
         * the lag of the traditional moving averages. This way you can use the T3 Average as 
         * a drop-in replacement for Average or xAverage, and get the same lag but better 
         * noise filtering.
         * 
         * The variable "b" is substituted for the variable "a" used in the article since "a"
         * is a reserved word.
         */
        [HandlerParameter(true, "10", Min = "5", Max = "100", Step = "5")]
        public int Period { get; set; }
        [HandlerParameter(true, "1", Min = "0", Max = "2", Step = "0.1")]
        public double VolumeFactor { get; set; }

        public IList<double> Execute(IList<double> src)
        {
            var P = src;
            var gd1 = new double[P.Count];
            var EMA1 = Context.GetData("ema1", new[] { "ema1"+Period.ToString() }, () => EMA.EMA_TSLab(P, Period));
            var EMA2 = Context.GetData("ema2", new[] { "ema2"+Period.ToString() }, () => EMA.EMA_TSLab(EMA1, Period));

            for (int i = 0; i < P.Count; i++)
            {
                gd1[i] = EMA1[i] * (1 + VolumeFactor) - EMA2[i] * VolumeFactor;
            }

            var t3i1 = new double[P.Count];
            var EMA3 = EMA.EMA_TSLab(gd1, Period);
            var EMA4 = EMA.EMA_TSLab(EMA3, Period);

            for (int i = 0; i < P.Count; i++)
            {
                t3i1[i] = EMA3[i] * (1 + VolumeFactor) - EMA4[i] * VolumeFactor;
            }

            var t3i2 = new double[P.Count];
            var EMA5 = EMA.EMA_TSLab(t3i1, Period);
            var EMA6 = EMA.EMA_TSLab(EMA5, Period);

            for (int i = 0; i < P.Count; i++)
            {
                t3i2[i] = EMA5[i] * (1 + VolumeFactor) - EMA6[i] * VolumeFactor;
            }

            return t3i2;
        }

        public IContext Context { get; set; }
    }