RSI与Wilder的RSI计算问题

Question

我很难得到一个平滑的RSI。下图来自freestockcharts.com。计算使用此代码。

public static double CalculateRsi(IEnumerable<double> closePrices)
{
    var prices = closePrices as double[] ?? closePrices.ToArray();

    double sumGain = 0;
    double sumLoss = 0;
    for (int i = 1; i < prices.Length; i++)
    {
        var difference = prices[i] - prices[i - 1];
        if (difference >= 0)
        {
            sumGain += difference;
        }
        else
        {
            sumLoss -= difference;
        }
    }

    if (sumGain == 0) return 0;
    if (Math.Abs(sumLoss) < Tolerance) return 100;

    var relativeStrength = sumGain / sumLoss;

    return 100.0 - (100.0 / (1 + relativeStrength));
}

https://stackoverflow.com/questions/...th-index-using-some-programming-language-js-c

这似乎是没有平滑的纯RSI。如何计算平滑的RSI？我尝试更改它以适应这两个站点的定义，但是输出不正确。它几乎是不平滑的。

(我没有足够的代表来发布链接)

tc2000 -> Indicators -> RSI_and_Wilder_s_RSI (Wilder's smoothing = Previous MA value + (1/n periods * (Close - Previous MA)))

priceactionlab -> wilders-cutlers-and-harris-relative-strength-index (RS = EMA(Gain(n), n)/EMA(Loss(n), n))

有人真的能用一些样本数据进行计算吗？

Wilder's RSI vs RSI

​

Answer 1

为了计算RSI，您需要一个句号来计算它。As noted on Wikipedia, 14 is used quite often.

因此，计算步骤如下：

周期1- 13，RSI =0

第14期：

AverageGain = TotalGain / PeriodCount;
AverageLoss = TotalLoss / PeriodCount;
RS = AverageGain / AverageLoss;
RSI = 100 - 100 / (1 + RS);

第15期-至第(N)期：

if (Period(N)Change > 0
  AverageGain(N) = ((AverageGain(N - 1) * (PeriodCount - 1)) + Period(N)Change) / PeriodCount;
else
  AverageGain(N) = (AverageGain(N - 1) * (PeriodCount - 1)) / PeriodCount;

if (this.Change < 0)
  AverageLoss(N) = ((AverageLoss(N - 1) * (PeriodCount - 1)) + Math.Abs(Period(N)Change)) / PeriodCount;
else
  AverageLoss(N) = (AverageLoss(N - 1) * (PeriodCount - 1)) / PeriodCount;

RS = AverageGain / AverageLoss;
RSI = 100 - (100 / (1 + RS));

此后，为了平滑这些值，您需要对RSI值应用特定时间段的移动平均值。为此，请从最后一个索引到第一个索引遍历RSI值，并根据前面的x个平滑周期计算当前时段的平均值。

完成后，只需反转值列表即可获得预期的顺序：

List<double> SmoothedRSI(IEnumerable<double> rsiValues, int smoothingPeriod)
{
  if (rsiValues.Count() <= smoothingPeriod)
    throw new Exception("Smoothing period too large or too few RSI values passed.");

  List<double> results = new List<double>();
  List<double> reversedRSIValues = rsiValues.Reverse().ToList();

  for (int i = 1; i < reversedRSIValues.Count() - smoothingPeriod - 1; i++)
    results.Add(reversedRSIValues.Subset(i, i + smoothingPeriod).Average());

  return results.Reverse().ToList();
}

子集方法只是一个简单的扩展方法，如下所示：

public static List<double> Subset(this List<double> values, int start, int end)
{
  List<double> results = new List<double>();

  for (int i = start; i <= end; i++)
    results.Add(values[i]);

  return results;
}

免责声明，我没有测试代码，但它应该让您了解平滑是如何应用的。

Answer 2

如果没有缓冲区/全局变量来存储数据，就无法获得精确值。

这是一个平滑的指示器，这意味着它不只使用14个条块，而是所有的条块:这是一篇循序渐进的文章，如果价格和可用条块的数量相同，则工作和验证的源代码将生成完全相同的值(当然，您只需要从源中加载价格数据)：

测试和验证：

using System;
using System.Data;
using System.Globalization;

namespace YourNameSpace
  {
   class PriceEngine
      {
        public static DataTable data;
        public static double[] positiveChanges;
        public static double[] negativeChanges;
        public static double[] averageGain;
        public static double[] averageLoss;
        public static double[] rsi;
        
        public static double CalculateDifference(double current_price, double previous_price)
          {
              return current_price - previous_price;
          }

        public static double CalculatePositiveChange(double difference)
          {
              return difference > 0 ? difference : 0;
          }

        public static double CalculateNegativeChange(double difference)
          {
              return difference < 0 ? difference * -1 : 0;
          }

        public static void CalculateRSI(int rsi_period, int price_index = 5)
          {
              for(int i = 0; i < PriceEngine.data.Rows.Count; i++)
              {
                  double current_difference = 0.0;
                  if (i > 0)
                  {
                      double previous_close = Convert.ToDouble(PriceEngine.data.Rows[i-1].Field<string>(price_index));
                      double current_close = Convert.ToDouble(PriceEngine.data.Rows[i].Field<string>(price_index));
                      current_difference = CalculateDifference(current_close, previous_close);
                  }
                  PriceEngine.positiveChanges[i] = CalculatePositiveChange(current_difference);
                  PriceEngine.negativeChanges[i] = CalculateNegativeChange(current_difference);

                  if(i == Math.Max(1,rsi_period))
                  {
                      double gain_sum = 0.0;
                      double loss_sum = 0.0;
                      for(int x = Math.Max(1,rsi_period); x > 0; x--)
                      {
                          gain_sum += PriceEngine.positiveChanges[x];
                          loss_sum += PriceEngine.negativeChanges[x];
                      }

                      PriceEngine.averageGain[i] = gain_sum / Math.Max(1,rsi_period);
                      PriceEngine.averageLoss[i] = loss_sum / Math.Max(1,rsi_period);

                  }else if (i > Math.Max(1,rsi_period))
                  {
                      PriceEngine.averageGain[i] = ( PriceEngine.averageGain[i-1]*(rsi_period-1) + PriceEngine.positiveChanges[i]) / Math.Max(1, rsi_period);
                      PriceEngine.averageLoss[i] = ( PriceEngine.averageLoss[i-1]*(rsi_period-1) + PriceEngine.negativeChanges[i]) / Math.Max(1, rsi_period);
                      PriceEngine.rsi[i] = PriceEngine.averageLoss[i] == 0 ? 100 : PriceEngine.averageGain[i] == 0 ? 0 : Math.Round(100 - (100 / (1 + PriceEngine.averageGain[i] / PriceEngine.averageLoss[i])), 5);
                  }
              }
          }
          
        public static void Launch()
          {
            PriceEngine.data = new DataTable();            
            //load {date, time, open, high, low, close} values in PriceEngine.data (6th column (index #5) = close price) here
            
            positiveChanges = new double[PriceEngine.data.Rows.Count];
            negativeChanges = new double[PriceEngine.data.Rows.Count];
            averageGain = new double[PriceEngine.data.Rows.Count];
            averageLoss = new double[PriceEngine.data.Rows.Count];
            rsi = new double[PriceEngine.data.Rows.Count];
            
            CalculateRSI(14);
          }
          
      }
  }

有关详细的逐步说明，我写了一篇很长的文章，您可以在这里查看它：https://turmanauli.medium.com/a-step-by-step-guide-for-calculating-reliable-rsi-values-programmatically-a6a604a06b77

P.S.函数只适用于简单指标(简单移动平均)，甚至指数移动平均，平均真实范围绝对需要全局变量来存储先前的值。