/// <summary>
/// Calculate Ehlers' Zero Lag Exponential Moving Average, as described here:
/// <see href="https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average"/>
/// </summary>
/// <param name="series">input time series</param>
/// <param name="period">averaging length</param>
/// <param name="parentId">caller cache id, optional</param>
/// <param name="memberName">caller's member name, optional</param>
/// <param name="lineNumber">caller line number, optional</param>
/// <returns>ZLEMA as time series</returns>
public static ITimeSeries <double> ZLEMA(this ITimeSeries <double> series, int period,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), period);
int lag = (int)Math.Round((period - 1.0) / 2.0);
return(series
.Add(
series
.Subtract(
series
.Delay(lag, cacheId),
cacheId),
cacheId)
.EMA(period, cacheId));
}
override public void Run()
{
//---------- initialization
// set simulation time frame
StartTime = DateTime.Parse("01/01/2015");
EndTime = DateTime.Parse("12/31/2016");
// add instruments
AddDataSource(_instrumentNick);
//---------- simulation
foreach (DateTime simTime in SimTimes)
{
// find our instrument. if we have only one instrument,
// we could also just use Instruments.Values.First()
Instrument instrument = FindInstrument(_instrumentNick);
// calculate simple indicators
// the output of an indicator is a time series
ITimeSeries <double> ema26 = instrument.Close.EMA(26);
ITimeSeries <double> ema12 = instrument.Close.EMA(12);
// therefore, indicators can be calculated on top of indicators
ITimeSeries <double> macd = ema12.Subtract(ema26);
ITimeSeries <double> signal = macd.EMA(9);
// plot our data
_plotter.SelectChart("indicators vs time", "date");
_plotter.SetX(simTime.Date);
_plotter.Plot(instrument.Symbol, instrument.Close[0] + _offsetPrice);
_plotter.Plot("ema26", ema26[0] + _offsetPrice);
_plotter.Plot("ema12", ema12[0] + _offsetPrice);
_plotter.Plot("macd", macd[0]);
_plotter.Plot("signal", signal[0]);
}
}
/// <summary>
/// Calculate Commodity Channel Index of input time series, as described here:
/// <see href="https://en.wikipedia.org/wiki/Commodity_channel_index"/>
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">averaging length</param>
/// <param name="parentId">caller cache id, optional</param>
/// <param name="memberName">caller's member name, optional</param>
/// <param name="lineNumber">caller line number, optional</param>
/// <returns>CCI time series</returns>
public static ITimeSeries <double> CCI(this ITimeSeries <double> series, int n = 20,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
ITimeSeries <double> delta = series
.Subtract(
series
.SMA(n, cacheId),
cacheId);
ITimeSeries <double> meanDeviation = delta
.AbsValue(cacheId)
.SMA(n, cacheId);
return delta[0] / Math.Max(1e-10, 0.015 * meanDeviation[0]);
},
0.5,
cacheId));
}