/// <summary>
/// Calculate Covariance.
/// <see href="https://en.wikipedia.org/wiki/Covariance"/>
/// </summary>
/// <param name="series">this time series</param>
/// <param name="other">other time series</param>
/// <param name="n">number of bars</param>
/// <param name="subSample">distance between bars</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>covariance time series</returns>
public static ITimeSeries <double> Covariance(this ITimeSeries <double> series, ITimeSeries <double> other, int n, int subSample = 1,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), other.GetHashCode(), n);
var x = series;
var y = other;
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
var avgX = Enumerable.Range(0, n)
.Average(t => x[t * subSample]);
var avgY = Enumerable.Range(0, n)
.Average(t => y[t * subSample]);
var covXY = Enumerable.Range(0, n)
.Sum(t => (x[t * subSample] - avgX) * (y[t * subSample] - avgY))
/ (n - 1.0);
return covXY;
},
0.0,
cacheId));
}
/// <summary>
/// Calculate Pearson Correlation Coefficient.
/// <see href="https://en.wikipedia.org/wiki/Pearson_correlation_coefficient"/>
/// </summary>
/// <param name="series">this time series</param>
/// <param name="other">other time series</param>
/// <param name="n">number of bars</param>
/// <param name="subSample">distance between bars</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>correlation coefficient time series</returns>
public static ITimeSeries <double> Correlation(this ITimeSeries <double> series, ITimeSeries <double> other, int n, int subSample = 1,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), other.GetHashCode(), n);
var x = series;
var y = other;
double sum = n / 2.0 * (n + 1.0);
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
var avgX = Enumerable.Range(0, n)
.Average(t => x[t * subSample]);
var avgY = Enumerable.Range(0, n)
.Average(t => y[t * subSample]);
var covXY = Enumerable.Range(0, n)
.Sum(t => (x[t * subSample] - avgX) * (y[t * subSample] - avgY)) / n;
var varX = Enumerable.Range(0, n)
.Sum(t => Math.Pow(x[t * subSample] - avgX, 2.0)) / n;
var varY = Enumerable.Range(0, n)
.Sum(t => Math.Pow(y[t * subSample] - avgY, 2.0)) / n;
var corr = covXY
/ Math.Max(1e-99, Math.Sqrt(varX) * Math.Sqrt(varY));
return corr;
},
0.0,
cacheId));
}
/// <summary>
/// Calculate Kaufman's Adaptive Moving Average, as described here:
/// <see href="https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average"/>
/// </summary>
/// <param name="series">input time series</param>
/// <param name="erPeriod">period for efficiency ratio</param>
/// <param name="fastEma">period for fast EMA</param>
/// <param name="slowEma">period for slow EMA</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>KAMA as time series</returns>
public static ITimeSeries <double> KAMA(this ITimeSeries <double> series, int erPeriod = 10, int fastEma = 2, int slowEma = 30,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), erPeriod, fastEma, slowEma);
// TODO: we should be able to remove the try/ catch blocks here
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
try
{
double scFast = 2.0 / (1.0 + fastEma);
double scSlow = 2.0 / (1.0 + slowEma);
double change = Math.Abs(series[0] - series[erPeriod]);
double volatility = Enumerable.Range(0, erPeriod)
.Sum(t => Math.Abs(series[t] - series[t + 1]));
double efficiencyRatio = change / Math.Max(1e-10, volatility);
double smoothingConstant = Math.Pow(efficiencyRatio * (scFast - scSlow) + scSlow, 2);
double r = smoothingConstant * (series[0] - v) + v;
return double.IsNaN(r) ? 0.0 : r;
}
catch (Exception)
{
// we get here when we access bars too far in the past
return series[0];
}
},
series[0],
cacheId));
}
/// <summary>
/// Calculate Stochastic Oscillator, as described here:
/// <see href="https://en.wikipedia.org/wiki/Stochastic_oscillator"/>
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">oscillator period</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>Stochastic Oscillator as time series</returns>
public static _StochasticOscillator StochasticOscillator(this ITimeSeries <double> series, int n = 14,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
var container = Cache <_StochasticOscillator> .GetData(
cacheId,
() => new _StochasticOscillator());
double hh = series
.Highest(n, cacheId)[0];
double ll = series
.Lowest(n, cacheId)[0];
double price = series[0];
container.PercentK = IndicatorsBasic.BufferedLambda(
v => 100.0 * (price - ll) / Math.Max(1e-10, hh - ll),
50.0,
cacheId);
container.PercentD = container.PercentK
.SMA(3, cacheId);
return(container);
}
/// <summary>
/// Normalize time series over number of bars; 1.0 being the average.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">normalizing period</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>normalized time series</returns>
public static ITimeSeries <double> Normalize(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(series.Divide(series.EMA(n, cacheId), cacheId));
}
/// <summary>
/// Calculate logarithmic regression of time series.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">number of bars for regression</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>regression parameters as time series</returns>
public static _Regression LogRegression(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(series
.Log(cacheId)
.LinRegression(n, cacheId));
}
/// <summary>
/// Cast time series to double
/// </summary>
/// <param name="series">input series of long</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>output series of double</returns>
public static ITimeSeries <double> ToDouble(this ITimeSeries <long> series,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode());
return(IndicatorsBasic.Lambda(
(t) => (double)series[t],
cacheId));
}
/// <summary>
/// Calculate addition of time series and constant value.
/// </summary>
/// <param name="series">time series</param>
/// <param name="constValue">constant value</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>time series + constant value</returns>
public static ITimeSeries <double> Add(this ITimeSeries <double> series, double constValue,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), constValue.GetHashCode());
return(IndicatorsBasic.Lambda(
(t) => series[t] + constValue,
cacheId));
}
/// <summary>
/// Calculate historical volatility, based on log-returns.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">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>volatility as time series</returns>
public static ITimeSeries <double> Volatility(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);
return(series
.LogReturn(cacheId)
.StandardDeviation(n, cacheId));
}
/// <summary>
/// Calculate subtraction of two time series.
/// </summary>
/// <param name="series1">time series #1</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>
/// <param name="series2">time series #2</param>
/// <returns>time series #1 - time series #2</returns>
public static ITimeSeries <double> Subtract(this ITimeSeries <double> series1, ITimeSeries <double> series2,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series1.GetHashCode(), series2.GetHashCode());
return(IndicatorsBasic.Lambda(
(t) => series1[t] - series2[t],
cacheId));
}
/// <summary>
/// Delay time series by number of bars.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="delay">delay 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>delayed time series</returns>
public static ITimeSeries <double> Delay(this ITimeSeries <double> series, int delay,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), delay);
return(Lambda(
(t) => series[t + delay],
cacheId));
}
/// <summary>
/// Calculate logarithmic return from the previous to the current value
/// of the time series.
/// </summary>
/// <param name="series">input time series</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>logarithm of relative return</returns>
public static ITimeSeries <double> LogReturn(this ITimeSeries <double> series,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode());
return(Lambda(
(t) => Math.Log(series[t] / series[t + 1]),
cacheId));
}
/// <summary>
/// Calculate range over the specified number of past bars.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">number of bars to search</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>range between highest and lowest value of past n bars</returns>
public static ITimeSeries <double> Range(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(series
.Highest(n, cacheId)
.Subtract(series
.Lowest(n, cacheId), cacheId));
}
/// <summary>
/// Calculate simple momentum: m = p[0] / p[n] - 1
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">number of bars for regression</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>simple momentum, as time series</returns>
public static ITimeSeries <double> SimpleMomentum(this ITimeSeries <double> series, int n = 21,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n.GetHashCode());
return(IndicatorsBasic.BufferedLambda(
prev => series[0] / series[n] - 1.0,
0.0,
cacheId));
}
/// <summary>
/// Calculate Simple Moving Average as described here:
/// <see href="https://en.wikipedia.org/wiki/Moving_average#Simple_moving_average"/>
/// </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>SMA time series</returns>
public static ITimeSeries <double> SMA(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(IndicatorsBasic.BufferedLambda(
v => Enumerable.Range(0, n)
.Sum(t => series[t]) / n,
series[0],
cacheId));
}
/// <summary>
/// Calculate lowest value of the specified number of past bars.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">number of bars to search</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>lowest value of past n bars</returns>
public static ITimeSeries <double> Lowest(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
int N = Math.Max(1, n);
return(BufferedLambda(
(v) => Enumerable.Range(0, N).Min(t => series[t]),
series[0],
cacheId));
}
/// <summary>
/// Calculate Capital Asset Pricing Model parameters.
/// <see href="http://en.wikipedia.org/wiki/Capital_asset_pricing_model"/>
/// This indicator uses an exponentially-weighted, incremental method of
/// calculation, based on Tony Finch, which is very fast and efficient.
/// /// </summary>
/// <param name="series">input time series</param>
/// <param name="benchmark">benchmark time series</param>
/// <param name="n">length of observation window</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>container w/ CAPM parameters</returns>
public static _CAPM CAPM(this ITimeSeries <double> series, ITimeSeries <double> benchmark, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), benchmark.GetHashCode(), n);
var functor = Cache <CAPMFunctor> .GetData(
cacheId,
() => new CAPMFunctor(series, benchmark, n));
functor.Calc();
return(functor);
}
/// <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 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 ITimeSeries <double> FastStandardDeviation(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 functor = Cache <FunctorStandardDeviation> .GetData(
cacheId,
() => new FunctorStandardDeviation(series, n));
functor.Calc();
return(functor);
}
/// <summary>
/// Calculate linear regression of time series.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">number of bars for regression</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>regression parameters as time series</returns>
public static _Regression LinRegression(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
var functor = Cache <LinRegressionFunctor> .GetData(
cacheId,
() => new LinRegressionFunctor(series, n));
functor.Calc();
return(functor);
}
/// <summary>
/// Return current drawdown in percent, as value between 0 and 1.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">length of observation window</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>drawdown as time series</returns>
public static ITimeSeries <double> Drawdown(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
// TODO: rewrite this, using buffered lambda, see MaxDrawdown
return(IndicatorsBasic.Const(1.0, cacheId)
.Subtract(
series
.Divide(
series
.Highest(n, cacheId),
cacheId),
cacheId));
}
/// <summary>
/// Calculate return over maximum drawdown.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">length of observation window</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>RoMaD</returns>
public static ITimeSeries <double> ReturnOnMaxDrawdown(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(IndicatorsBasic.BufferedLambda(
(p) =>
{
double ret = series[0] / series[n] - 1.0;
double dd = series.MaxDrawdown(n)[0];
return ret / Math.Max(1e-3, dd);
},
0.0,
cacheId));
}
/// <summary>
/// Calculate volatility estimate from recent trading range.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">length of calculation window</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>volatility as time series</returns>
public static ITimeSeries <double> VolatilityFromRange(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
double hi = series.Highest(n)[0];
double lo = series.Lowest(n)[0];
return 0.80 * Math.Sqrt(1.0 / n) * Math.Log(hi / lo);
},
0.0,
cacheId));
}
/// <summary>
/// Calculate Williams %R, as described here:
/// <see href="https://en.wikipedia.org/wiki/Williams_%25R"/>
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">period</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>Williams %R as time series</returns>
public static ITimeSeries <double> WilliamsPercentR(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);
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
double hh = series.Highest(n)[0];
double ll = series.Lowest(n)[0];
double price = series[0];
return -100.0 * (hh - price) / Math.Max(1e-10, hh - ll);
},
-50.0,
cacheId));
}
/// <summary>
/// Calculate Exponential Moving Average, as described here:
/// <see href="https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average"/>
/// </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>EMA time series</returns>
public static ITimeSeries <double> EMA(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
double alpha = 2.0 / (Math.Max(1, n) + 1);
return(IndicatorsBasic.BufferedLambda(
(v) =>
{
double r = alpha * (series[0] - v) + v;
return double.IsNaN(r) ? 0.0 : r;
},
series[0],
cacheId));
}
/// <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));
}
/// <summary>
/// Calculate MACD, as described here:
/// <see href="https://en.wikipedia.org/wiki/MACD"/>
/// </summary>
/// <param name="series">input time series</param>
/// <param name="fast">fast EMA period</param>
/// <param name="slow">slow EMA period</param>
/// <param name="signal">signal line period</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></returns>
public static _MACD MACD(this ITimeSeries <double> series, int fast = 12, int slow = 26, int signal = 9,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), fast, slow, signal);
var container = Cache <_MACD> .GetData(
cacheId,
() => new _MACD());
container.Fast = series.EMA(fast, cacheId);
container.Slow = series.EMA(slow, cacheId);
container.MACD = container.Fast.Subtract(container.Slow, cacheId);
container.Signal = container.MACD.EMA(signal, cacheId);
container.Divergence = container.MACD.Subtract(container.Signal, cacheId);
return(container);
}
/// <summary>
/// Calculate Sharpe Ratio, as described here:
/// <see href="https://en.wikipedia.org/wiki/Sharpe_ratio"/>
/// </summary>
/// <param name="series"></param>
/// <param name="riskFreeRate"></param>
/// <param name="n"></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></returns>
public static ITimeSeries <double> SharpeRatio(this ITimeSeries <double> series, ITimeSeries <double> riskFreeRate, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), riskFreeRate.GetHashCode(), n);
var excessReturn = series
.Return(cacheId)
.Subtract(riskFreeRate
.Return(cacheId),
cacheId);
return(excessReturn
.EMA(n, cacheId)
.Divide(excessReturn
.FastStandardDeviation(n, cacheId)
.Max(IndicatorsBasic.Const(1e-10, cacheId), cacheId),
cacheId));
}
/// <summary>
/// Calculate Hull Moving Average, as described here:
/// <see href="https://alanhull.com/hull-moving-average"/>
/// </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>HMA time series</returns>
public static ITimeSeries <double> HMA(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
// http://www.incrediblecharts.com/indicators/weighted_moving_average.php
// Integer(SquareRoot(Period)) WMA [2 x Integer(Period/2) WMA(Price) - Period WMA(Price)]
int n2 = (int)Math.Round(n / 2.0);
int n3 = (int)Math.Round(Math.Sqrt(n));
return(series
.WMA(n2, cacheId)
.Multiply(2.0, cacheId)
.Subtract(
series
.WMA(n, cacheId),
cacheId)
.WMA(n3, cacheId));
}
/// <summary>
/// Return maximum drawdown.
/// </summary>
/// <param name="series">input time series</param>
/// <param name="n">length of observation window</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>maximum drawdown as time series</returns>
public static ITimeSeries <double> MaxDrawdown(this ITimeSeries <double> series, int n,
CacheId parentId = null, [CallerMemberName] string memberName = "", [CallerLineNumber] int lineNumber = 0)
{
var cacheId = new CacheId(parentId, memberName, lineNumber,
series.GetHashCode(), n);
return(IndicatorsBasic.BufferedLambda(
(p) =>
{
double highestHigh = 0.0;
double maxDrawdown = 0.0;
for (int t = n - 1; t >= 0; t--)
{
highestHigh = Math.Max(highestHigh, series[t]);
maxDrawdown = Math.Max(maxDrawdown, 1.0 - series[t] / highestHigh);
}
return maxDrawdown;
},
0.0,
cacheId));
}