public ConnorsRSI(DataSeries ds, int periodRSI, int periodStreak, int periodPR, string description)
: base(ds, description)
{
base.FirstValidValue = Math.Max(Math.Max(periodRSI, periodStreak), periodPR);
if (FirstValidValue <= 1)
{
return;
}
ConsecDaysDown cdd = ConsecDaysDown.Series(ds, 0);
ConsecDaysUp cdu = ConsecDaysUp.Series(ds, 0);
DataSeries streak = new DataSeries(ds, "streak");
for (int bar = 0; bar < ds.Count; bar++)
{
streak[bar] = cdd[bar] > 0 ? -cdd[bar] : cdu[bar] > 0 ? cdu[bar] : 0;
}
RSI rsi3 = RSI.Series(ds, periodRSI);
RSI rsiStreak = RSI.Series(streak, periodStreak);
ROC ret = ROC.Series(ds, 1);
DataSeries pr = PercentRank.Series(ret, periodPR) * 100.0;
DataSeries connorsRSI = (rsi3 + rsiStreak + pr) / 3;
for (int bar = base.FirstValidValue; bar < ds.Count; bar++)
{
base[bar] = connorsRSI[bar];
}
}
public MSR(Bars bars, int per1, int per2, string description)
: base(bars, description)
{
base.FirstValidValue = 252;
//MSR= (10-day median of (H, L, C) – 20-day MAX (H, L, C))/(20-day MAX (H, L, C))
//then take the 252-day percentrank of MSR or percentile ranking
DataSeries msrTemp = new DataSeries(bars, "msrTemp(" + per1 + "," + per2 + ")");
for (int bar = Math.Max(per1, per2); bar < bars.Count; bar++)
{
double max20 = Highest.Series(bars.High, per2)[bar];
List <double> prices = new List <double>(per1 * 3);
for (int i = bar; i > bar - per1; i--)
{
prices.Add(bars.High[i]); prices.Add(bars.Low[i]); prices.Add(bars.Close[i]);
}
prices.Sort();
msrTemp[bar] = (GetMedian(prices) - max20) / max20;
}
for (int bar = FirstValidValue; bar < bars.Count; bar++)
{
base[bar] = PercentRank.Series(msrTemp, 252)[bar];
//base[bar] = PrcRank.Series(msrTemp, 252)[bar];
}
}
public DV2_Bounded(DataSeries ds, int period, string description)
: base(ds, description)
{
base.FirstValidValue = period;
for (int bar = FirstValidValue; bar < ds.Count; bar++)
{
base[bar] = PercentRank.Series(ds, period)[bar];
}
}
/*
* The creation of the oscillator involves taking the arithmetic average of the percentage returns (or 1-day ROCs) over the last 5 days.
* A geometric average is created by simply adding 1 to the percentage returns and taking the product of this series over the last 5 days
* (the cumulative return).
* The geometric average is derived by subtracting 1 from the cumulative return.
* Then we subtract the arithmetic return from the geometric return to find the divergence.
* This divergence is smoothed twice using a 3-day average to create smooth signals (note that the raw divergence produces signals in the same direction).
* Finally this smoothed divergence is “bounded,” by taking the PERCENTRANK of the series going back 1-year.
*/
public GAMDO(Bars bars, string description)
: base(bars, description)
{
base.FirstValidValue = 252;
int period = 5;
DataSeries ArAvg = Community.Indicators.FastSMA.Series(bars.Close / (bars.Close >> 1), period) - 1d;
DataSeries GeomAvg = GMA.Series(bars.Close / (bars.Close >> 1), period) - 1d;
DataSeries Diver = GeomAvg - ArAvg;
DataSeries SmoothedDiver = Community.Indicators.FastSMA.Series(Diver, 3);
for (int bar = FirstValidValue; bar < bars.Count; bar++)
{
base[bar] = PercentRank.Series(SmoothedDiver, 252)[bar];
}
}
public UDIDSRI(DataSeries ds, int period, int power, int percentRankPeriod, int iteration, string description)
: base(ds, description)
{
base.FirstValidValue = Math.Max(period, percentRankPeriod);
DataSeries diff = ds - (ds >> 1);
DataSeries mov = new DataSeries(ds, "mov(" + ds.Description + "," + period + "," + percentRankPeriod + ")");
DataSeries movement = new DataSeries(ds, "movement(" + ds.Description + "," + period + "," + percentRankPeriod + ")");
DataSeries r = DataSeries.Abs(ds / (ds >> 1) - 1);
int u_iteration = iteration == 1 ? 1 : 2;
for (int bar = FirstValidValue; bar < ds.Count; bar++)
{
if (diff[bar] > 0)
{
mov[bar] = 1;
}
if (diff[bar] < 0)
{
mov[bar] = -1;
}
movement[bar] = mov[bar] * Math.Pow((1 + r[bar]), power);
}
DataSeries UDIDSRI_1st = Sum.Series(mov, period);
UDIDSRI_1st = PercentRank.Series(UDIDSRI_1st, percentRankPeriod);
DataSeries UDIDSRI_2nd = Sum.Series(movement, period);
UDIDSRI_2nd = PercentRank.Series(UDIDSRI_2nd, percentRankPeriod);
for (int bar = FirstValidValue; bar < ds.Count; bar++)
{
base[bar] = u_iteration == 1 ? UDIDSRI_1st[bar] : UDIDSRI_2nd[bar];
}
}
public DVI(DataSeries B, string description)
: base(B, description)
{
base.FirstValidValue = 252;
DataSeries C = B / Community.Indicators.FastSMA.Series(B, 3) - 1;
DataSeries D = (Community.Indicators.FastSMA.Series(C, 5) + (Community.Indicators.FastSMA.Series(C, 100) / 10)) / 2;
DataSeries E = Community.Indicators.FastSMA.Series(D, 5);
DataSeries F = GreaterThan.Series(B, (B >> 1), 1, -1);
DataSeries G = (Sum.Series(F, 10) + (Sum.Series(F, 100) / 10)) / 2;
DataSeries H = Community.Indicators.FastSMA.Series(G, 2);
// DVI [Magnitude]
DataSeries I = PercentRank.Series(E, 252);
// DVI [Stretch]
DataSeries J = PercentRank.Series(H, 252);
DataSeries DVI = (0.8 * I) + (0.2 * J);
for (int bar = base.FirstValidValue; bar < B.Count; bar++)
{
base[bar] = DVI[bar];
}
}
public DSR(Bars bars, int period, string description)
: base(bars, description)
{
base.FirstValidValue = 252;
List <double> lst = new List <double>();
DataSeries rawDSR = new DataSeries(bars, "raw dsr(" + period + ")");
for (int bar = period; bar < bars.Count; bar++)
{
lst.Clear();
for (int i = bar; i > bar - period; i--)
{
lst.Add(bars.High[i]); lst.Add(bars.Low[i]); lst.Add(bars.Close[i]);
}
//DSR differential= average( 65th percentile (H,L,C, 20-days), 80th percentile (H,L,C, 20-days)) –
//average( 35th percentile (H,L,C, 20-days), 20th percentile (H,L,C, 20-days))
double dsrDiff = (percentile(lst, 65) + percentile(lst, 80) / 2) - (percentile(lst, 35) + percentile(lst, 20) / 2);
//DSR raw= DSR differential/(max(H,L,C, 20-days)-min(H,L,C, 20-days))
double dsrRaw = dsrDiff / (Highest.Series(bars.High, period)[bar] - Lowest.Series(bars.Low, period)[bar]);
rawDSR[bar] = dsrRaw;
}
//DSR= 252-day percentrank of ( 10-day sma of DSR raw )
var rangePartitioner = Partitioner.Create(FirstValidValue, bars.Count);
Parallel.ForEach(rangePartitioner, (range, loopState) =>
{
for (int bar = range.Item1; bar < range.Item2; bar++)
{
base[bar] = PercentRank.Series(Community.Indicators.FastSMA.Series(rawDSR, 10), 252)[bar];
}
});
}