/// <summary>
/// downside risk (deviation, variance) of the return distribution
/// Downside deviation, semideviation, and semivariance are measures of downside
/// risk.
/// </summary>
// = "full"
// = false
//func DownsideDeviation(Ra *utils.SlidingWindow, MAR *utils.SlidingWindow, method string, potential bool) float64 {
func DownsideDeviation(Ra *utils.SlidingWindow, MAR *utils.SlidingWindow) (float64, error) {
if Ra == nil {
return math.NaN(), errors.New("In DownsideDeviation, Ra == nil")
}
if Ra.Count() <= 0 {
return math.NaN(), errors.New("In DownsideDeviation, Ra.Count() <= 0")
}
r, err := utils.NewSlidingWindow(Ra.Count())
if err != nil {
return math.NaN(), err
}
newMAR, err := utils.NewSlidingWindow(Ra.Count())
if err != nil {
return math.NaN(), err
}
len := 0.0
result := 0.0
for i := 0; i < Ra.Count(); i++ {
if Ra.Data()[i] < MAR.Data()[i] {
r.Add(Ra.Data()[i])
newMAR.Add(MAR.Data()[i])
}
}
potential := false
method := "subset"
if method == "full" {
len = float64(Ra.Count())
} else if method == "subset" {
len = float64(r.Count())
} else {
return math.NaN(), errors.New("In DownsideDeviation, method default !!!")
}
if newMAR.Count() <= 0 || r.Count() <= 0 || len <= 0 {
return math.NaN(), errors.New("In DownsideDeviation, newMAR.Count() <= 0 || r.Count() <= 0 || len <= 0")
}
if potential {
sub_Sliding, err := utils.Sub(newMAR, r)
if err != nil {
return math.NaN(), err
}
result = sub_Sliding.Sum() / len
} else {
sub_Sliding, err := utils.Sub(newMAR, r)
if err != nil {
return math.NaN(), err
}
pow_Sliding, err := utils.Power(sub_Sliding, 2.0)
if err != nil {
return math.NaN(), err
}
result = math.Sqrt(pow_Sliding.Sum() / len)
}
return result, nil
}
/// <summary>
/// epsilon与R中不同,但似乎没有影响
/// Specific risk is the standard deviation of the error term in the
/// regression equation.
/// </summary>
func SpecificRisk(Ra *utils.SlidingWindow, Rb *utils.SlidingWindow, scale float64, Rf float64) (float64, error) {
//Period = Frequency(Ra)
alpha, err := Alpha2(Ra, Rb, Rf)
if err != nil {
return math.NaN(), err
}
beta, err := Beta2(Ra, Rb, Rf)
if err != nil {
return math.NaN(), err
}
add_Ra_Sliding, err := utils.Add(-Rf, Ra)
if err != nil {
return math.NaN(), err
}
add_Rb_Sliding, err := utils.Add(-Rf, Rb)
if err != nil {
return math.NaN(), err
}
multi_beta_Slidinig, err := utils.Multi(beta, add_Rb_Sliding)
if err != nil {
return math.NaN(), err
}
sub_Ra_Beta, err := utils.Sub(add_Ra_Sliding, multi_beta_Slidinig)
if err != nil {
return math.NaN(), err
}
epsilon, err := utils.Add(-alpha, sub_Ra_Beta)
if err != nil {
return math.NaN(), err
}
var_eps, err := Variance(epsilon)
if err != nil {
return math.NaN(), err
}
var result = math.Sqrt(var_eps*float64(epsilon.Count()-1)/float64(epsilon.Count())) * math.Sqrt(float64(scale))
return result, nil
}
/// <summary>
/// Appraisal ratio is the Jensen's alpha adjusted for specific risk. The numerator
/// is divided by specific risk instead of total risk.
/// </summary>
func AppraisalRatio(Ra *utils.SlidingWindow, Rb *utils.SlidingWindow, scale float64, Rf float64, method string) (float64, error) {
var result = 0.0
switch method {
case "appraisal":
be_data, err := Beta2(Ra, Rb, Rf)
if err != nil {
return math.NaN(), err
}
multi_Sliding, err := utils.Multi(be_data, Rb)
if err != nil {
return math.NaN(), err
}
sub_Sliding, err := utils.Sub(Ra, multi_Sliding)
if err != nil {
return math.NaN(), err
}
al_data, err := Alpha2(Ra, Rb, Rf)
if err != nil {
return math.NaN(), err
}
epsilon, err := utils.Add(-al_data, sub_Sliding)
if err != nil {
return math.NaN(), err
}
add_Sliding, err := utils.Add(-epsilon.Average(), epsilon)
if err != nil {
return math.NaN(), err
}
pow_Sliding, err := utils.Power(add_Sliding, 2)
if err != nil {
return math.NaN(), err
}
specifikRisk := math.Sqrt(pow_Sliding.Sum()/float64(epsilon.Count())) * math.Sqrt(float64(scale))
jsa_data, err := JensenAlpha2(Ra, Rb, Rf, scale)
if err != nil {
return math.NaN(), err
}
result = jsa_data / specifikRisk
break
case "modified":
jsa2_data, err := JensenAlpha2(Ra, Rb, Rf, scale)
if err != nil {
return math.NaN(), err
}
be2_data, err := Beta2(Ra, Rb, Rf)
if err != nil {
return math.NaN(), err
}
result = jsa2_data / be2_data
break
case "alternative":
jsa2_data, err := JensenAlpha2(Ra, Rb, Rf, scale)
if err != nil {
return math.NaN(), err
}
sr_data, err := SystematicRisk(Ra, Rb, scale, Rf)
if err != nil {
return math.NaN(), err
}
result = jsa2_data / sr_data
break
default:
return math.NaN(), errors.New("In AppraisalRatio, method is default !!!")
}
return result, nil
}