我一直在绞尽脑汁尝试将这个公式转换为C#,但没有成功.

REMA Formula.

我擅长做杂技,但不擅长数学.

Where 0 < λ ≤ 1 is a decay factor.
When λ < 1, the exponentially weighted moving average assigns greater weights to the prices.

Contrary to the regular exponential moving average that gives greater
weights to the most recent prices, the reverse exponential moving
average assigns greater weights to the oldest prices and
decreases the importance of the most recent prices.

解决方法:

高效的REMA(),带有错误检查和功能；特定于上下文的返回值

有效率吗？是的,避免重复重复的昂贵操作(或依靠编译器优化技巧)

进行错误检查？是的,对于Q / A程序几乎是必须的.

特定于上下文的？是,返回{-1. | REMA(k,lambda)},它允许调用者处理输入错误的特殊情况.

double[] fTimeSeriesPRICE;     // a forward-stepping storage ( vs. a MT4 reversed-stepping model )

public double REMA( int k, double lambda )
{   
    if (  lambda <= 0.0        // lambda shall not fall on/under  0.0
       || lambda >  1.0        //        shall not grow beyond    1.0
       || k      <= 0          // depth  shall not be negative or 0
       )
       return -1.0;            // context-protecting RET value

    int aCurrentPricePTR  = fTimeSeriesPRICE.Length - 1;
    int aMaxIterableDEPTH = Math.Min( aCurrentPricePTR, k );

    double numerator   = 0.0;
    double denominator = 0.0;  // REMA formula-expansion contains +1 at the end, never less, never negative
    double lambdator   = 1.0;  // lambda ^ ( ( k - j ) == 0 ) == 1.0

    for ( int aReverseSteppingPTR  = 0;
              aReverseSteppingPTR <= aMaxIterableDEPTH;
              aReverseSteppingPTR++
              )
    {   numerator   += lambdator * fTimeSeriesPRICE[aCurrentPricePTR - aReverseSteppingPTR];
        denominator += lambdator;
        lambdator   *= lambda;
    }
    return numerator / denominator; // numerically fair, denominator never < +1.0
}