从交易视图，我们看到rsi可以用pinescript写成如下：
pine_rsi(x, y) =>
u = max(x - x[1], 0) // upward change
d = max(x[1] - x, 0) // downward change
rs = rma(u, y) / rma(d, y)
rsi = 100 - 100 / (1 + rs)
我用 C# 重写了这个：
public static double RelativeStrengthIndex(List<double> input, int samples)
{
List<double> gains = new List<double>();
List<double> losses = new List<double>();
for (int i = input.Count - samples; i < input.Count; i++)
{
double change = input[i] - input[i - 1];
gains.Add(change >= 0 ? change : 0);
losses.Add(change < 0 ? -1 * change : 0);
}
double rs = RollingMovingAverage(gains, samples) / RollingMovingAverage(losses, samples);
return 100 - 100 / (1 + rs);
}
然而，我的结果并不等同。它们的差异足以排除数据差异（交易视图和其他提供商的数据可能略有不同）。我已经尝试解决这个问题一段时间了，但已经完全放弃了。
有谁知道为什么我的代码会产生不同的结果？
我遇到了完全相同的问题，发现 RSI 是基于 滚动移动平均线 (RMA) 的，它是一个累积函数。对于 14 周期 RSI，您需要大约 100 根柱才能使其稳定。我必须将值放入 excel 电子表格中才能弄清楚并获得与 Pinescript 匹配的公式。
我最终基于此编写了自己的 C# RSI 函数并进行了测试，它得到与 pinescript 相同的结果。基类 AnalyzableBase 来自 Trady - 一个开源指标框架。
using System;
using System.Collections.Generic;
using System.Linq;
using System.Reflection;
using HodlBot.Common.Extensions;
using Trady.Analysis;
using Trady.Analysis.Infrastructure;
using Trady.Core.Infrastructure;
namespace HodlBot.Lite.Strategy
{
public class FastRsi<TOutput> : AnalyzableBase<IOhlcv, IOhlcv, decimal?, TOutput>
{
private readonly List<IOhlcv> _inputs;
public int Period { get; }
private List<decimal?> _rsi = new List<decimal?>();
private int _periodMinus1;
private List<DateTimeOffset> _dateTimes;
private decimal _lastGain = 0;
private decimal _lastLoss = 0;
public FastRsi(IEnumerable<IOhlcv> inputs, int period) : base(inputs, i => i)
{
_inputs = inputs.ToList();
_dateTimes = _inputs.Select(x => x.DateTime).ToList();
Period = period;
_rsi.Add(null);
_periodMinus1 = period - 1;
// RMA_Gain=((Gain*(Period-1)) + RMA_Gain[i-1])/Period
for (int i = 1; i < _inputs.Count; i++)
{
decimal change = _inputs[i].Close - _inputs[i-1].Close;
decimal gain = change > 0 ? change : 0;
decimal loss = change < 0 ? -change : 0;
decimal rmaGain = ((_lastGain * _periodMinus1) + gain) / period;
decimal rmaLoss = ((_lastLoss * _periodMinus1) + loss) / period;
decimal rs = rmaLoss == 0 ? 100 : rmaGain / rmaLoss;
decimal rsi = 100 - (100 / (1 + rs));
_rsi.Add(i < period ? null : (decimal?)rsi);
_lastGain = rmaGain;
_lastLoss = rmaLoss;
}
}
public FastRsi<TOutput> AddOhlcv(IOhlcv ohlc)
{
_inputs.Add(ohlc);
_dateTimes.Add(ohlc.DateTime);
IReadOnlyList<IOhlcv> mappedInputs = _inputs;
IReadOnlyList<DateTimeOffset> mappedDateTimes = _dateTimes;
// Trady base class needs these to be able to compute a single index.
// TODO: Set these
typeof(FastRsi)
.GetField("_mappedInputs", BindingFlags.Instance | BindingFlags.NonPublic)
.SetValue(this, mappedInputs);
typeof(AnalyzableBase<IOhlcv, IOhlcv, decimal?, TOutput>)
.GetField("_mappedDateTimes", BindingFlags.Instance | BindingFlags.NonPublic)
.SetValue(this, mappedDateTimes);
int i = _mappedInputs.Count - 1;
decimal change = i > 0 ? _mappedInputs[i].Close - _mappedInputs[i - 1].Close : 0;
decimal gain = change > 0 ? change : 0;
decimal loss = change < 0 ? -change : 0;
decimal rmaGain = ((_lastGain * _periodMinus1) + gain) / Period;
decimal rmaLoss = ((_lastLoss * _periodMinus1) + loss) / Period;
decimal rs = rmaLoss == 0 ? 100 : rmaGain / rmaLoss;
decimal rsi = 100 - (100 / (1 + rs));
_rsi.Add(i < Period ? null : (decimal?)rsi);
_lastGain = rmaGain;
_lastLoss = rmaLoss;
return this;
}
protected override decimal? ComputeByIndexImpl(IReadOnlyList<IOhlcv> mappedInputs, int index)
{
return _rsi[index];
}
}
public class FastRsi : FastRsi<AnalyzableTick<decimal?>>
{
public FastRsi(IEnumerable<IOhlcv> inputs, int period) : base(inputs, period)
{
}
}
}
这里还有更多内容我的实施和推理.
您没有处理初始 MA 周期，您需要考虑第一个 RSI 值。
我使用给定股票代码的历史数据为 RSI 和 StochRSI 编写的以下方法
public DataTable getRSIDataTableFromDaily(string symbol, string exchange, string seriestype = "CLOSE", string outputsize = "Compact", string time_interval = "1d",
string fromDate = null, string period = "14", bool stochRSI = false)
{
DataTable dailyTable = null;
//DataTable rsiDataTable = null;
int iPeriod;
double change, gain, loss, avgGain = 0.00, avgLoss = 0.00, rs, rsi;
double sumOfGain = 0.00, sumOfLoss = 0.00;
//DateTime dateCurrentRow = DateTime.Today;
List<string> seriesNameList;
try
{
//GetStockPriceData returns data table with historical OHLC values for period specified
dailyTable = GetStockPriceData(symbol, exchange, seriestype, outputsize, time_interval, fromDate, sqlite_cmd: null);
if ((dailyTable != null) && (dailyTable.Rows.Count > 0))
{
iPeriod = System.Convert.ToInt32(period);
DataColumn newCol;
if (stochRSI == false)
{
//If caller wants only RSI then we will only use the seriestype specified by caller to calculate the RSI for specified period
newCol = new DataColumn("RSI_" + seriestype, typeof(decimal));
newCol.DefaultValue = 0.00;
dailyTable.Columns.Add(newCol);
seriesNameList = new List<string> { seriestype };
}
else
{
//If caller wants StochRSI then we need to include all of OHLC values
newCol = new DataColumn("RSI_OPEN", typeof(decimal));
newCol.DefaultValue = 0.00;
dailyTable.Columns.Add(newCol);
newCol = new DataColumn("RSI_CLOSE", typeof(decimal));
newCol.DefaultValue = 0.00;
dailyTable.Columns.Add(newCol);
newCol = new DataColumn("RSI_HIGH", typeof(decimal));
newCol.DefaultValue = 0.00;
dailyTable.Columns.Add(newCol);
newCol = new DataColumn("RSI_LOW", typeof(decimal));
newCol.DefaultValue = 0.00;
dailyTable.Columns.Add(newCol);
seriesNameList = new List<string> { "CLOSE", "OPEN", "HIGH", "LOW" };
}
foreach (var item in seriesNameList)
{
change = gain = loss = avgGain = avgLoss = rs = rsi = 0.00;
sumOfGain = sumOfLoss = 0.00;
for (int rownum = 1; rownum < dailyTable.Rows.Count; rownum++)
{
//current - prev
//change = System.Convert.ToDouble(dailyTable.Rows[rownum][seriestype]) - System.Convert.ToDouble(dailyTable.Rows[rownum - 1][seriestype]);
change = System.Convert.ToDouble(dailyTable.Rows[rownum][item.ToString()]) - System.Convert.ToDouble(dailyTable.Rows[rownum - 1][item.ToString()]);
//dateCurrentRow = System.Convert.ToDateTime(dailyTable.Rows[rownum]["TIMESTAMP"]);
if (change < 0)
{
loss = Math.Abs(change);
gain = 0.00;
}
else
{
gain = change;
loss = 0.00;
}
//for the first iPeriod keep adding loss & gain
if (rownum < iPeriod)
{
sumOfGain += gain;
sumOfLoss += loss;
}
else
{
if (rownum == iPeriod)
{
//this means we are at period specified
sumOfGain += gain;
sumOfLoss += loss;
//we also find other fields and SAVE
avgGain = sumOfGain / iPeriod;
avgLoss = sumOfLoss / iPeriod;
rs = avgGain / avgLoss;
rsi = 100 - (100 / (1 + rs));
}
else
{
//this means we are now beyond the period, calculate RSI
avgGain = ((avgGain * (iPeriod - 1)) + gain) / iPeriod;
avgLoss = ((avgLoss * (iPeriod - 1)) + loss) / iPeriod;
rs = avgGain / avgLoss;
rsi = 100 - (100 / (1 + rs));
}
//dailyTable.Rows[rownum]["RSI"] = Math.Round(rsi, 2);
dailyTable.Rows[rownum]["RSI_" + item.ToString()] = Math.Round(rsi, 2);
}
}
}
}
}
catch (Exception ex)
{
Console.WriteLine("getRSIDataTableFromDaily exception: " + ex.Message);
if (dailyTable != null)
{
dailyTable.Clear();
dailyTable.Dispose();
}
dailyTable = null;
}
return dailyTable;
}博士Andrew Burnett-Thompson 你是个天才。有用。非常感谢，祝你生活愉快=）
我一直在寻找在您提出的选项中使用 RMA 的 RIS 解决方案，99.99% 给出了相同的结果。