Giving back to the community (some new/updated indicators)

avishn

#1

7/9/2010 7:17 PM

Just some new and updated indicators for Comminity.Indicators

avishn

#2

7/9/2010 7:18 PM

Percent Rank (corrects Community.Indicators' version, matches MS Excel output, implements PartialValue)

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Percent Rank (corrects Community.Indicators' version, matches MS Excel output, implements PartialValue)
[CODE]using System;
using System.Collections.Generic;
using System.Text;
using System.Drawing;

using WealthLab;
using WealthLab.Indicators;

using MyWealthLabLibrary.Components;

namespace MyWealthLabLibrary.Indicators {
	
	public class PrcRank : DataSeries {
		
		DataSeries ds;
		int period;

public PrcRank(DataSeries ds, int period, string description) : base(ds, description) {
			
			this.ds = ds;
			this.period = period;
			
			FirstValidValue = period + ds.FirstValidValue - 1;
			
			for (int bar = FirstValidValue; bar < ds.Count; bar++) {
				int countLessThan = 0;
				for (int i = 0; i < period - 1; i++) {
					if (ds[bar] > ds[bar - period + 1 + i]) {
						countLessThan++;
					}
				}
				base[bar] = (double)countLessThan / (period - 1);
			}

}
		
		public static PrcRank Series(DataSeries ds, int period) {
			string description = string.Concat(new object[] { "PrcRank(", ds.Description, ",", period, ")" });
			if (ds.Cache.ContainsKey(description)) {
				return (PrcRank)ds.Cache[description];
			}
			PrcRank _pr = new PrcRank(ds, period, description);
			ds.Cache[description] = _pr;
			return _pr;
		}
		
		public override void CalculatePartialValue() {
			ds.CalculatePartialValue(ds);
			if (Double.IsNaN(ds.PartialValue)) {
				PartialValue = Double.NaN;
			} else {
				int countLessThan = 0;
				for (int i = 1; i < period; i++) {
					if (ds.PartialValue > ds[ds.Count - period + i]) {
						countLessThan++;
					}
				}
				PartialValue = (double)countLessThan / (period - 1);				
			}
		}
		
	}
	
	public class PrcRankHelper : IndicatorHelper {
		
		private static object[] _paramDefaults;
		private static string[] _paramNames;

static PrcRankHelper() {
			_paramDefaults = new object[] { CoreDataSeries.Close, new RangeBoundInt32(2, 2, 300) };
			_paramNames = new string[] { "DataSeries", "Period" };
		}

public override Color DefaultColor { get { return Color.Black; } }
		public override int DefaultWidth { get { return 1; } }
		public override string Description { get { return "Returns percent rank of the current element of the dataseries within all elements over the specified period"; } }
		public override Type IndicatorType { get { return typeof(PrcRank); } }
		public override IList<object> ParameterDefaultValues { get { return _paramDefaults; } }
		public override IList<string> ParameterDescriptions { get { return _paramNames; } }
		public override string TargetPane { get { return "PrcRank"; } }
		
	}

}
[/CODE]

avishn

#3

7/9/2010 7:19 PM

David Varadi's DV2 (implements PartialValue, important if you trade at market close)

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David Varadi's DV2 (implements PartialValue, important if you trade at market close)
[CODE]using System;
using System.Collections.Generic;
using System.Text;
using System.Drawing;

using WealthLab;
using WealthLab.Indicators;

namespace MyWealthLabLibrary.Indicators {

public class DV2 : DataSeries {
		
		Bars bars;

public DV2(Bars bars, string description) : base(bars, description) {
			this.bars = bars;
			base.FirstValidValue = 1;
			DataSeries dv2 = bars.Close / (bars.High + bars.Low) * 2d - 1d;
			dv2 = (dv2 + (dv2 >> 1)) / 2d;
			for (int bar = FirstValidValue; bar < bars.Count; bar++) {
				base[bar] = dv2[bar];
			}			
		}

public static DV2 Series(Bars bars) {
			string description = string.Concat(new object[] { "DV2(", bars.Symbol, ")" });
			if (bars.Cache.ContainsKey(description)) {
				return (DV2)bars.Cache[description];
			}
			DV2 _DV2 = new DV2(bars, description);
			bars.Cache[description] = _DV2;
			return _DV2;
		}
		
		public override void CalculatePartialValue() {
			if (bars.Count < (FirstValidValue + 1) || Double.IsNaN(bars.Close.PartialValue)) {
				PartialValue = Double.NaN;
			} else {
				int lb = bars.Count - 1;
				PartialValue = ((bars.Close.PartialValue / (bars.High.PartialValue + bars.Low.PartialValue) * 2d - 1d)
				                + (bars.Close[lb] / (bars.High[lb] + bars.Low[lb]) * 2d - 1d)) / 2d;
			}
		}
		
	}

public class DV2Helper : IndicatorHelper {
		
		private static object[] _paramDefaults;
		private static string[] _paramNames;

static DV2Helper() {
			_paramDefaults = new object[] { BarDataType.Bars };
			_paramNames = new string[] { "Bars" };
		}

public override Color DefaultColor { get { return Color.Blue; } }
		public override string Description { get { return "DV2 (PartialValue version)"; } }
		public override Type IndicatorType { get { return typeof(DV2); } }
		public override IList<object> ParameterDefaultValues { get { return _paramDefaults; } }
		public override IList<string> ParameterDescriptions { get { return _paramNames; } }
		public override string TargetPane { get { return "DV2"; } }
		public override string URL { get { return "http://marketsci.wordpress.com/2009/07/15/varadi%E2%80%99s-rsi2-alternative-the-dv2/"; } }
		
	}

}[/CODE]

avishn

#4

7/9/2010 7:22 PM

DV2, bounded version, combines the two indicators above

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DV2, bounded version, combines the two indicators above
[CODE]using System;
using System.Collections.Generic;
using System.Text;
using System.Drawing;

using WealthLab;
using WealthLab.Indicators;

namespace MyWealthLabLibrary.Indicators {

public class DV2Bounded : DataSeries {
		
		Bars bars;
		int period;
		DataSeries dv2;

public DV2Bounded(Bars bars, int period, string description) : base(bars, description) {
			
			this.bars = bars;
			this.period = period;
			base.FirstValidValue = period + 2;

dv2 = PrcRank.Series(DV2.Series(bars), period);

for (int bar = FirstValidValue; bar < bars.Count; bar++) {
				base[bar] = dv2[bar];
			}
			
		}

public static DV2Bounded Series(Bars bars, int period) {
			string description = string.Concat(new object[] { "DV2Bounded(", bars.Symbol, ",", period, ")" });
			if (bars.Cache.ContainsKey(description)) {
				return (DV2Bounded)bars.Cache[description];
			}
			DV2Bounded _DV2Bounded = new DV2Bounded(bars, period, description);
			bars.Cache[description] = _DV2Bounded;
			return _DV2Bounded;
		}
		
		public override void CalculatePartialValue() {
			dv2.CalculatePartialValue();
			PartialValue = dv2.PartialValue;
		}
		
	}

public class DV2BoundedHelper : IndicatorHelper {
		
		private static object[] _paramDefaults;
		private static string[] _paramNames;

static DV2BoundedHelper() {
			_paramDefaults = new object[] { BarDataType.Bars, new RangeBoundInt32(252, 1, 300)  };
			_paramNames = new string[] { "Bars", "Period" };
		}

public override Color DefaultColor { get { return Color.Blue; } }
		public override string Description { get { return "DV2Bounded (PartialValue, corrected PercentRank version)"; } }
		public override Type IndicatorType { get { return typeof(DV2Bounded); } }
		public override IList<object> ParameterDefaultValues { get { return _paramDefaults; } }
		public override IList<string> ParameterDescriptions { get { return _paramNames; } }
		public override string TargetPane { get { return "DV2Bounded"; } }
		public override string URL { get { return "http://marketsci.wordpress.com/2009/07/15/varadi%E2%80%99s-rsi2-alternative-the-dv2/"; } }
		
	}

}[/CODE]

avishn

#5

7/9/2010 7:26 PM

David Varadi's DVO ("the mother oscillator"), the idea is published in this article -- http://daveab.com/mr_swing/index.htm

CODE:
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Note: because of complexity of parameters, this implementation has two static methods to create an instance of indicator -- DVO.Series takes string parameters and DVO.Series2 takes array parameters.

DV2 mentioned above is a specific configuration of more generic DVO with parameters "0, 0.5, 0.5, 0" and "0.5, 0.5".

Implements PartialValue.

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David Varadi's DVO ("the mother oscillator"), the idea is published in this article -- [link]http://daveab.com/mr_swing/index.htm[/link]

[CODE]using System;
using System.Linq;
using System.Collections.Specialized;
using System.Collections.Generic;
using System.Text;
using System.Drawing;

using WealthLab;
using WealthLab.Indicators;

namespace MyWealthLabLibrary.Indicators {

public class DVO : DataSeries {
		
		Bars bars;
		
		double[] w;	// individual bar prices' weights
		double[] s;	// prior bars' weights

public DVO(Bars bars, double[] w, double[] s, string description) : base(bars, description) {
			
			this.bars = bars;
			this.w = w;
			this.s = s;
			
			this.FirstValidValue = s.Length - 1;
			
			for (int bar = FirstValidValue; bar < bars.Count; bar++) {
				double v = 0d;
				for (int i = 0; i < s.Length; i++) {
					int b = bar - s.Length + 1 + i;
					v += (bars.Close[b] / (w[0] * bars.Open[b] + w[1] * bars.High[b] + w[2] * bars.Low[b] + w[3] * bars.Close[b]) - 1d)* s[i];
				}
				base[bar] = v;
			}
			
		}

public static DVO Series(Bars bars, string w, string s) {
			return Series2(bars, w.Split(',').Select(v => Double.Parse(v)).ToArray(), s.Split(',').Select(v => Double.Parse(v)).ToArray());
		}
		
		public static DVO Series2(Bars bars, double[] w, double[] s) {
			string description = string.Concat(new object[] { "DVO(", bars.Symbol, ",[",
			                                   	String.Join(",", w.Select(v => v.ToString("F")).ToArray()), "],[",
			                                   	String.Join(",", s.Select(v => v.ToString("F")).ToArray()), "])" });
			if (bars.Cache.ContainsKey(description)) {
				return (DVO)bars.Cache[description];
			}
			DVO _s = new DVO(bars, w, s, description);
			bars.Cache[description] = _s;
			return _s;
		}
		
		public override void CalculatePartialValue() {
			if (Double.IsNaN(bars.Close.PartialValue)) {
				PartialValue = Double.NaN;
			} else {
				double v = 0d;
				for (int i = 0; i < s.Length - 1; i++) {
					int b = bars.Count - s.Length + 1 + i;
					v += bars.Close[b] / (w[0] * bars.Open[b] + w[1] * bars.High[b] + w[2] * bars.Low[b] + w[3] * bars.Close[b]) * s[i];
				}
				v += bars.Close.PartialValue / (w[0] * bars.Open.PartialValue + w[1] * bars.High.PartialValue + w[2] * bars.Low.PartialValue + w[3] * bars.Close.PartialValue) * s[s.Length - 1];
				PartialValue = v - 1d;
			}
		}
		
	}

public class DVOHelper : IndicatorHelper {
		
		private static object[] _paramDefaults;
		private static string[] _paramNames;

static DVOHelper() {
			_paramDefaults = new object[] { BarDataType.Bars, "0, 0.5, 0.5, 0", "0.5, 0.5" };
			_paramNames = new string[] { "Bars", "OHLC Weights", "Bar Weights" };
		}

public override Color DefaultColor { get { return Color.Blue; } }
		public override string Description { get { return "DVO (PartialValue version)"; } }
		public override Type IndicatorType { get { return typeof(DVO); } }
		public override IList<object> ParameterDefaultValues { get { return _paramDefaults; } }
		public override IList<string> ParameterDescriptions { get { return _paramNames; } }
		public override string TargetPane { get { return "DVO"; } }
		public override string URL { get { return "http://daveab.com/mr_swing/index.htm"; } }
		
	}

}[/CODE]

Note: because of complexity of parameters, this implementation has two static methods to create an instance of indicator -- DVO.Series takes string parameters and DVO.Series2 takes array parameters.

DV2 mentioned above is a specific configuration of more generic DVO with parameters "0, 0.5, 0.5, 0" and "0.5, 0.5".

Implements PartialValue.

Eugene

#6

7/10/2010 3:04 AM

Great, thank you for the contribution. I'll include it in Community.Indicators in the next update.

Eugene

#7

7/20/2010 8:23 AM

...except for the DVO (which requires .NET 3.5 due to its use of LINQ). This one will be added in a future release.

avishn

#8

10/8/2010 8:47 AM

Recent post on CSS Analytics http://cssanalytics.wordpress.com/2010/09/08/using-non-parametric-statistics-to-improve-portfolio-optimization/ reminded me that I was planning to share a couple of non-parametric tools...

Spearman Rank Correlation

CODE:
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Requires ALGLIB DLL.
You can also obtain z-score and t-score series to check robustness of correlation.

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Color:

Recent post on CSS Analytics [LINK]http://cssanalytics.wordpress.com/2010/09/08/using-non-parametric-statistics-to-improve-portfolio-optimization/[/LINK] reminded me that I was planning to share a couple of non-parametric tools...

Spearman Rank Correlation

[CODE]using System;
using System.Collections.Generic;
using System.Text;
using System.Drawing;

using WealthLab;
using WealthLab.Indicators;

namespace MyWealthLabLibrary.Indicators {  
    
    public class SpearmanRankCorrelation : DataSeries {
        
        DataSeries ds1, ds2, zscore, tscore;
        int period;

public SpearmanRankCorrelation(DataSeries ds1, DataSeries ds2, int period, string description) : base(ds1, description) {

this.ds1 = ds1;
            this.ds2 = ds2;
            this.period = period;
            
            this.FirstValidValue = period + Math.Max(ds1.FirstValidValue, ds2.FirstValidValue) - 1;

double[] a1 = new double[period];
            double[] a2 = new double[period];
                    
            for (int bar = FirstValidValue; bar < ds1.Count; bar++) {
                for (int i = 0; i < period; i++) {
                    a1[i] = ds1[bar - period + i + 1];
                    a2[i] = ds2[bar - period + i + 1];
                }
                base[bar] = alglib.correlation.spearmanrankcorrelation(a1, a2, period);
            }
            
        }

public static SpearmanRankCorrelation Series(DataSeries ds1, DataSeries ds2, int period) {
            string description = string.Concat(new object[] { "SpearmanRankCorrelation(", ds1.Description, ",", ds2.Description, ",", period, ")" });
            if (ds1.Cache.ContainsKey(description)) {
                return (SpearmanRankCorrelation)ds1.Cache[description];
            }
            SpearmanRankCorrelation _s = new SpearmanRankCorrelation(ds1, ds2, period, description);
            ds1.Cache[description] = _s;
            return _s;
        }
        
        public DataSeries ZScore() {
            if (zscore == null) {
                zscore = new DataSeries(this, "ZScore(" + this.Description + ")");
                for (int bar = this.FirstValidValue; bar < this.Count; bar++) {
                    zscore[bar] = Math.Sqrt((this.period - 3d) / 1.06) * Math.Atan(this[bar]);
                }
            }
            return zscore;
        }
                
        public DataSeries tScore() {
            if (tscore == null) {
                tscore = new DataSeries(this, "tScore(" + this.Description + ")");
                for (int bar = this.FirstValidValue; bar < this.Count; bar++) {
                    tscore[bar] = this[bar] * Math.Sqrt((this.period - 2d) / (1d - this[bar] * this[bar]));
                }
            }
            return tscore;
        }
                
    }
    
    public class SpearmanRankCorrelationHelper : IndicatorHelper {
        
        private static object[] _paramDefaults;
        private static string[] _paramNames;

static SpearmanRankCorrelationHelper() {
            _paramDefaults = new object[] { CoreDataSeries.Close, CoreDataSeries.Close, new RangeBoundInt32(2, 2, 300) };
            _paramNames = new string[] { "DataSeries 1", "DataSeries 2", "Period" };
        }

public override Color DefaultColor { get { return Color.Black; } }
        public override int DefaultWidth { get { return 1; } }
        public override string Description { get { return "Spearman Rank Correlation"; } }
        public override Type IndicatorType { get { return typeof(SpearmanRankCorrelation); } }
        public override IList<object> ParameterDefaultValues { get { return _paramDefaults; } }
        public override IList<string> ParameterDescriptions { get { return _paramNames; } }
        public override string TargetPane { get { return "SpearmanRankCorrelation"; } }
        public override string URL { get { return "http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient"; } }
        
    }

}
[/CODE]

Requires ALGLIB DLL.
You can also obtain z-score and t-score series to check robustness of correlation.

avishn

#9

10/8/2010 8:47 AM

Kendall Tau Rank Correlation

CODE:
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CPU-intensive. Also allows you to obtain z-score.

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Kendall Tau Rank Correlation

[CODE]using System;
using System.Collections.Generic;
using System.Text;
using System.Drawing;

using WealthLab;
using WealthLab.Indicators;

namespace MyWealthLabLibrary.Indicators {
	
	public class KendallTauRankCorrelation : DataSeries {
		
		DataSeries ds1, ds2, zscore;
		int period;

public KendallTauRankCorrelation(DataSeries ds1, DataSeries ds2, int period, string description) : base(ds1, description) {

this.ds1 = ds1;
			this.ds2 = ds2;
			this.period = period;
			
			this.FirstValidValue = period;
			
			double[] r1 = new double[period];
			double[] r2 = new double[period];

for (int bar = FirstValidValue; bar < ds1.Count; bar++) {
				
				for (int i = 0; i < period; i ++) {
					r1[i] = Rank(ref ds1, bar - period + 1, bar, ds1[bar - period + i + 1]);
					r2[i] = Rank(ref ds2, bar - period + 1, bar, ds2[bar - period + i + 1]);
				}
				
				int conc_disc = 0;				
				for (int i = 0; i < period; i++) {
					for (int j = 0; j < (i - 1); j++) {
						conc_disc += Math.Sign(r1[i] - r1[j]) * Math.Sign(r2[i] - r2[j]);
					}
				}
				
				base[bar] = conc_disc / (0.5 * period * (period - 1));
				
			}
			
		}
		
		double Rank(ref DataSeries ds, int start, int stop, double v) {
			int countLessThan = 0;
			for (int i = start; i <= stop; i++) if (v > ds[i]) countLessThan++;
			return (double)countLessThan / (stop - start);
		}

public static KendallTauRankCorrelation Series(DataSeries ds1, DataSeries ds2, int period) {
			string description = string.Concat(new object[] { "KendallTauRankCorrelation(", ds1.Description, ",", ds2.Description, ",", period, ")" });
			if (ds1.Cache.ContainsKey(description)) {
				return (KendallTauRankCorrelation)ds1.Cache[description];
			}
			KendallTauRankCorrelation _s = new KendallTauRankCorrelation(ds1, ds2, period, description);
			ds1.Cache[description] = _s;
			return _s;
		}
		
		public DataSeries ZScore() {
			if (zscore == null) {
				zscore = new DataSeries(this, "ZScore(" + this.Description + ")");
				for (int bar = this.FirstValidValue; bar < this.Count; bar++) {
					zscore[bar] = this[bar] / Math.Sqrt(2d * (2d * this.period + 5d) / (9d * this.period * (this.period - 1)));
				}
			}
			return zscore;
		}
		
	}
	
	public class KendallTauRankCorrelationHelper : IndicatorHelper {
		
		private static object[] _paramDefaults;
		private static string[] _paramNames;

static KendallTauRankCorrelationHelper() {
			_paramDefaults = new object[] { CoreDataSeries.Close, CoreDataSeries.Close, new RangeBoundInt32(2, 2, 300) };
			_paramNames = new string[] { "DataSeries 1", "DataSeries 2", "Period" };
		}

public override Color DefaultColor { get { return Color.Black; } }
		public override int DefaultWidth { get { return 1; } }
		public override string Description { get { return "Kendall Tau Rank Correlation"; } }
		public override Type IndicatorType { get { return typeof(KendallTauRankCorrelation); } }
		public override IList<object> ParameterDefaultValues { get { return _paramDefaults; } }
		public override IList<string> ParameterDescriptions { get { return _paramNames; } }
		public override string TargetPane { get { return "KendallTauRankCorrelation"; } }
		public override string URL { get { return "http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient"; } }
		
	}

}
[/CODE]

CPU-intensive. Also allows you to obtain z-score.

Eugene

#10

10/13/2010 11:16 AM

Thank you!

Eugene

#11

10/20/2010 8:51 AM

"Kendall Tau Rank Correlation" will make it into the next release of Community Indicators but inclusion of "Spearman Rank Correlation" is unlikely. It requires ALGLIB DLL which is not OK to use in commercial applications due to GPL. Sure, Community Indicators is free an open source but Wealth-Lab isn't, and one of our rules of thumb learnt in the school of hard knocks is "never deal with GPL" (exception: LGPL).