/// <summary>
/// Calculate standard deviation, based on exponentially weighted filters. This is an
/// incremental calculation, based on Tony Finch, which is very fast and efficient.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">filtering 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>variance as time series</returns>
public static _SemiDeviation SemiDeviation(this ITimeSeries <double> series, int n = 10,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
var container = Cache <_SemiDeviation> .GetData(
cacheId,
() => new _SemiDeviation());
container.Average = series.SMA(n);
container.Downside = IndicatorsBasic.BufferedLambda(
v =>
{
var downSeries = Enumerable.Range(0, n)
.Where(t => series[t] < container.Average[0]);
if (downSeries.Count() == 0)
{
return(0.0);
}
else
{
return(Math.Sqrt(downSeries
.Average(t => Math.Pow(series[t] - container.Average[0], 2.0))));
}
}, 0.0,
cacheId);
container.Upside = IndicatorsBasic.BufferedLambda(
v =>
{
var upSeries = Enumerable.Range(0, n)
.Where(t => series[t] > container.Average[0]);
if (upSeries.Count() == 0)
{
return(0.0);
}
else
{
return(Math.Sqrt(upSeries
.Average(t => Math.Pow(series[t] - container.Average[0], 2.0))));
}
}, 0.0,
cacheId);
return(container);
}
/// <summary>
/// Calculate Bollinger Bands, as described here:
/// <see href="https://traderhq.com/ultimate-guide-to-bollinger-bands/"/>.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">length of calculation</param>
/// <param name="stdev">width of bands</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>Bollinger Band time series</returns>
public static _BollingerBands BollingerBands(this ITimeSeries <double> series, int n = 20, double stdev = 2.0,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n, stdev.GetHashCode());
var container = Cache <_BollingerBands> .GetData(
cacheId,
() => new _BollingerBands());
var stdevSeries = series.StandardDeviation(n, cacheId).Multiply(stdev, cacheId);
container.Middle = series.SMA(n, cacheId);
container.Upper = container.Middle.Add(stdevSeries, cacheId);
container.Lower = container.Middle.Subtract(stdevSeries, cacheId);
container.PercentB = IndicatorsBasic.BufferedLambda(
prev => (series[0] - container.Lower[0]) / Math.Max(1e-10, container.Upper[0] - container.Lower[0]),
0.0,
cacheId);
return(container);
}
/// <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));
}
