namespace QuanTAlib; using System; using System.Linq; /* EMA: Exponential Moving Average EMA needs very short history buffer and calculates the EMA value using just the previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1) Sources: https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA Issues: There is no consensus what the first EMA value should be - a zero, a first datapoint, or an average of the initial Period bars. All three starting methods converge within 20+ bars to the same moving average. Most implementations (including this one) use SMA() for the first Period bars as a seeding value for EMA. */ public class EMA_Series : TSeries { private double _k; private double _lastema, _oldema; private double _sum, _oldsum; private int _len; private readonly bool _useSMA; protected readonly int _period; protected readonly bool _NaN; protected readonly TSeries _data; //core constructors public EMA_Series(int period, bool useNaN, bool useSMA) { _period = period; _NaN = useNaN; _useSMA = useSMA; Name = $"EMA({period})"; _k = 2.0 / (_period + 1); _len = 0; _sum = _oldsum = _lastema = _oldema = 0; } public EMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) { _data = source; Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; _data.Pub += Sub; Add(_data); } public EMA_Series() : this(0, false, true) { } public EMA_Series(int period) : this(period, false, true) { } public EMA_Series(TBars source) : this(source.Close, 0, false) { } public EMA_Series(TBars source, int period) : this(source.Close, period, false) { } public EMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } public EMA_Series(TSeries source, int period) : this(source, period, false, true) { } public EMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } ////////////////// // core Add() algo public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) { if (update) { _lastema = _oldema; _sum = _oldsum; } else { _oldema = _lastema; _oldsum = _sum; _len++; } double _ema = 0; if (_period == 0) { _k = 2.0 / (_len + 1); } if (Count == 0) { _ema = _sum = TValue.v; } else if (_len <= _period && _useSMA && _period != 0) { _sum += TValue.v; if (_period != 0 && _len > _period) { _sum -= _data[Count - _period - (update ? 1 : 0)].v; } _ema = _sum / Math.Min(_len, _period); } else { _ema = _k * (TValue.v - _lastema) + _lastema; } _lastema = double.IsNaN(_ema) ? _lastema : _ema; var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ema); return base.Add(res, update); } //variation of Add() public override (DateTime t, double v) Add(TSeries data) { if (data == null) { return (DateTime.Today, Double.NaN); } foreach (var item in data) { Add(item, false); } return _data.Last; } public (DateTime t, double v) Add(bool update) { return this.Add(TValue: _data.Last, update: update); } public (DateTime t, double v) Add() { return Add(TValue: _data.Last, update: false); } private new void Sub(object source, TSeriesEventArgs e) { Add(TValue: _data.Last, update: e.update); } //reset calculation public override void Reset() { _sum = _oldsum = _lastema = _oldema = 0; _len = 0; } }