Golang utils.SlidingWindow类代码示例

//= "full"
// = false
func DownsideDeviation2(Ra *utils.SlidingWindow, MAR float64) (float64, error) {
	if Ra == nil || Ra.Count() <= 0 {
		return math.NaN(), errors.New("In DownsideDeviation2, Ra == nil || Ra.Count() <= 0")
	}
	newMAR, _ := utils.CreateList(MAR, Ra.Count())
	return DownsideDeviation(Ra, newMAR)
}

/// <summary>
/// subset of returns that are
/// more than the target (or Minimum Acceptable Returns (MAR)) returns and
/// divide the length of this subset by the total number of returns.
/// （超过MAR的频率）
/// </summary>
func UpsideFrequency(Ra *utils.SlidingWindow, MAR float64) (float64, error) {
	aboveMAR, err := utils.AboveValue(Ra, MAR)
	if err != nil {
		return math.NaN(), err
	}
	return float64(aboveMAR.Count()) / float64(Ra.Count()), nil
}

/// <summary>
/// Upside Potential Ratio,compared to Sortino, was a further improvement, extending the
/// measurement of only upside on the numerator, and only downside of the
/// denominator of the ratio equation.
/// （分子只考虑超过MAR部分，分母只考虑DownsideDeviation的下跌风险）
/// </summary>
func UpsidePotentialRatio(Ra *utils.SlidingWindow, MAR float64) (float64, error) {
	//var r = Ra.Where<float64>(singleData => singleData > MAR).ToList<float64>();
	r, err := utils.AboveValue(Ra, MAR)
	if err != nil {
		return math.NaN(), err
	}
	var length int
	method := "subset"
	switch method {
	case "full":
		length = Ra.Count()
		break
	case "subset":
		length = r.Count()
		break
	default:
		return math.NaN(), errors.New("In UpsidePotentialRatio, method is default !!!")
	}
	add_Sliding, err := utils.Add(-MAR, r)
	if err != nil {
		return math.NaN(), err
	}
	dd2Data, err := DownsideDeviation2(Ra, MAR)
	if err != nil {
		return math.NaN(), err
	}
	var result = (add_Sliding.Sum() / float64(length)) / dd2Data
	return result, nil
}

/// <summary>
/// calculate a traditional or modified Sharpe Ratio of Return over StdDev or
/// VaR or ES
///
/// The Sharpe ratio is simply the return per unit of risk (represented by
/// variability).  In the classic case, the unit of risk is the standard
/// deviation of the returns.
/// </summary>
func SharpeRatio(Ra *utils.SlidingWindow, Rf_val float64, scale float64) (float64, error) {
	Rf, err := utils.CreateList(Rf_val, Ra.Count())
	if err != nil {
		return math.NaN(), err
	}
	xR, err := Excess(Ra, Rf)
	if err != nil {
		return math.NaN(), err
	}
	numerator := 0.0
	denominator := 0.0
	annualize := 1
	if annualize == 1 {
		denominator, err = StdDev_Annualized(Ra, scale)
		if err != nil {
			return math.NaN(), err
		}
		numerator, err = Annualized(xR, scale, true)
		if err != nil {
			return math.NaN(), err
		}
	} else {
		denominator, err = StdDev(Ra)
		if err != nil {
			return math.NaN(), err
		}
		numerator = xR.Average()
	}

	return numerator / denominator, nil
}

/// <summary>
/// Calculate the drawdown levels in a timeseries
/// </summary>
//= true
func Drawdowns(Rb *utils.SlidingWindow) ([]float64, error) {
	Ra := Rb.Data()
	if Ra == nil || len(Ra) <= 0 {
		return nil, errors.New("In Drawdowns, Ra == nil")
	}

	geometric := 1

	curReturn := 1.0
	curMaxReturn := 1.0 + Ra[0]
	result := []float64{}
	if geometric == 1 {
		for _, r := range Ra {
			curReturn = curReturn * (1.0 + r)
			if curReturn > curMaxReturn {
				curMaxReturn = curReturn
			}
			result = append(result, curReturn/curMaxReturn-1.0)
		}
	} else {
		for _, r := range Ra {
			curReturn = curReturn + r
			if curReturn > curMaxReturn {
				curMaxReturn = curReturn
			}
			result = append(result, curReturn/curMaxReturn-1.0)
		}
	}

	return result, nil
}

func Beta2(Ra, Rb *utils.SlidingWindow, Rf float64) (float64, error) {
	RfList, err := utils.CreateList(Rf, Ra.Count())
	if err != nil {
		return math.NaN(), err
	}
	return Beta(Ra, Rb, RfList)
}

/// <summary>
/// 收益率序列的几何均值，非年化
/// </summary>
func MeanGeometric(Ra *utils.SlidingWindow) (float64, error) {
	if Ra.Count() <= 0 {
		return math.NaN(), errors.New("In MeanGeometric, Ra.Count() <= 0")
	}
	add_Sliding, _ := utils.Add(1, Ra)
	log_Sliding, _ := utils.Log(add_Sliding)
	return math.Exp(log_Sliding.Average()) - 1.0, nil
}

/// <param name="returns"></param>
/// <returns></returns>
func Centered(returns *utils.SlidingWindow) (*utils.SlidingWindow, error) {
	if returns == nil {
		return nil, errors.New("Centered Sliding window is nil")
	}
	if returns.Count() == 0 {
		return nil, errors.New("Centered Count is Zero !!!")
	}
	return utils.Add(-returns.Average(), returns)
}

/// <summary>
/// To calculate Burke ratio we take the difference between the portfolio
/// return and the risk free rate and we divide it by the square root of the
/// sum of the square of the drawdowns. To calculate the modified Burke ratio
/// we just multiply the Burke ratio by the square root of the number of datas.
/// （一种调整收益率的计算方式，调整是通过drawdown的平方和进行的）
/// </summary>
func BurkeRatio(Ra *utils.SlidingWindow, Rf float64, scale float64) (float64, error) {
	var len = Ra.Count()
	var in_drawdown = false
	var peak = 1
	var temp = 0.0
	drawdown, err := utils.NewSlidingWindow(len)
	if err != nil {
		return math.NaN(), err
	}
	for i := 1; i < len; i++ {
		if Ra.Data()[i] < 0 {
			if !in_drawdown {
				peak = i - 1
				in_drawdown = true
			}
		} else {
			if in_drawdown {
				temp = 1.0
				for j := peak + 1; j < i; j++ {
					temp = temp * (1.0 + Ra.Data()[j])
				}
				drawdown.Add(temp - 1.0) //Source
				in_drawdown = false
			}
		}
	}

	if in_drawdown {
		temp = 1.0
		for j := peak + 1; j < len; j++ {
			temp = temp * (1.0 + Ra.Data()[j])
		}
		drawdown.Add(temp - 1.0) //Source
		//drawdown.Add((temp - 1.0) * 100.0)
		in_drawdown = false
	}
	//var Rp = Annualized(Ra, scale, true) - 1.0--->Source
	Rp, err := Annualized(Ra, scale, true)
	if err != nil {
		return math.NaN(), err
	}
	var result float64

	if drawdown.Count() != 0 {
		pow_Sliding, err := utils.Power(drawdown, 2)
		if err != nil {
			return math.NaN(), err
		}
		Rf = Rf * scale
		result = (Rp - Rf) / math.Sqrt(pow_Sliding.Sum())
	} else {
		result = 0
	}

	modified := true
	if modified {
		result = result * math.Sqrt(float64(len))
	}
	return result, nil
}

/// <summary>
/// Fama beta is a beta used to calculate the loss of diversification. It is made
/// so that the systematic risk is equivalent to the total portfolio risk.
/// </summary>
func FamaBeta(Ra *utils.SlidingWindow, Rb *utils.SlidingWindow, Ra_sclae float64, Rb_scale float64) (float64, error) {
	var n1 = Ra.Count()
	var n2 = Rb.Count()
	var_Ra, err := Variance(Ra)
	if err != nil {
		return math.NaN(), err
	}
	var_Rb, err := Variance(Rb)
	if err != nil {
		return math.NaN(), err
	}
	var result = math.Sqrt(var_Ra*float64(n1-1)/float64(n1)) * math.Sqrt(float64(Ra_sclae)) / (math.Sqrt(var_Rb*float64(n2-1)/float64(n2)) * math.Sqrt(float64(Rb_scale)))
	return result, nil
}

/// <summary>
/// Prospect ratio is a ratio used to penalise loss since most people feel loss
/// greater than gain
/// （经验类型调整收益率，给损失赋予更大的权重）
/// </summary>
func ProspectRatio(Ra *utils.SlidingWindow, MAR float64) (float64, error) {
	var n = Ra.Count()
	SigD, err := DownsideDeviation2(Ra, MAR)
	if err != nil {
		return math.NaN(), err
	}

	positivevalues, negativevalues, err := utils.PosNegValues(Ra)
	if err != nil {
		return math.NaN(), err
	}

	var result = ((positivevalues.Sum()+2.25*negativevalues.Sum())/float64(n) - MAR) / SigD
	return result, nil
}

/// <summary>
/// downside frequency of the return distribution
/// To calculate Downside Frequency, we take the subset of returns that are
/// less than the target (or Minimum Acceptable Returns (MAR)) returns and
/// divide the length of this subset by the total number of returns.
/// </summary>
func DownsideFrequency(Ra *utils.SlidingWindow, MAR *utils.SlidingWindow) (float64, error) {
	if Ra == nil {
		return math.NaN(), errors.New("In DownsideFrequency, Ra == nil")
	}
	if Ra.Count() <= 0 {
		return math.NaN(), errors.New("In DownsideFrequency, Ra.Count() <= 0")
	}
	len := 0.0
	for i := 0; i < Ra.Count(); i++ {
		if Ra.Data()[i] < MAR.Data()[i] {
			len++
		}
	}

	return len / float64(Ra.Count()), nil
}

func Excess(returns, Rf *utils.SlidingWindow) (*utils.SlidingWindow, error) {
	result, err := utils.NewSlidingWindow(returns.Count())
	if err != nil {
		return nil, err
	}
	for i := 0; i < returns.Count(); i++ {
		result.Add(returns.Data()[i] - Rf.Data()[i])
	}
	return result, nil
}

func PainRatio(Ra *utils.SlidingWindow, Rf float64, scale float64) (float64, error) {
	PI, err := PainIndex(Ra)
	if err != nil {
		return math.NaN(), err
	}
	n := Ra.Count()
	add_Sliding, err := utils.Add(1.0, Ra)
	if err != nil {
		return math.NaN(), err
	}
	prod_Sliding, err := utils.Prod(add_Sliding)
	if err != nil {
		return math.NaN(), err
	}
	Rp := math.Pow(prod_Sliding, float64(scale)/float64(n)) - 1.0
	Rf = Rf * scale
	return (Rp - Rf) / PI, nil
}

/// <param name="returns"></param>
/// <param name="geometric"></param>
/// <returns></returns>
func Cumulative(returns *utils.SlidingWindow, geometric bool) (float64, error) {
	if returns == nil {
		return math.NaN(), errors.New("Cumulative Sliding window is Nil !!!")
	}
	if returns.Count() == 0 {
		return math.NaN(), errors.New("Cumulative Count == 0 !!")
	}
	if !geometric {
		return (returns.Sum()), nil
	} else {
		add_data, err := utils.Add(1.0, returns)
		if err != nil {
			return math.NaN(), err
		}
		prod_data, err := utils.Prod(add_data)
		if err != nil {
			return math.NaN(), err
		}
		return (prod_data - 1.0), nil
	}
}