Types

OnlinePortfolioSelection.EGA Type

EGA{T<:AbstractFloat}<:EGMFramework

EGA variant of the EGM algorithm.

Fields

• gamma1::T: momentum parameter

• gamma2::T: momentum parameter

Example

julia> model = EGA(0.99, 0.)
EGA{Float64}(0.99, 0.0)

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OnlinePortfolioSelection.EGE Type

EGE{T<:AbstractFloat}<:EGMFramework

EGE variant of the EGM algorithm.

Fields

• gamma1::T: momentum parameter

Example

julia> model = EGE(0.99)
EGE{Float64}(0.99)

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OnlinePortfolioSelection.EGR Type

EGR{T<:AbstractFloat}<:EGMFramework

EGR variant of the EGM algorithm.

Fields

• gamma2::T: momentum parameter

Example

julia> model = EGR(0.)
EGR{Float64}(0.0)

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OnlinePortfolioSelection.EMA Type

EMA{T<:AbstractFloat}<:TrendRep

Exponential Moving Average trend representation. Formula:

$${\hat{\mathbf{x}}}_{E,t+1}\left(\vartheta \right)=\frac{\sum _{k=0}^{t-1}{\left(1-\vartheta \right)}^k\vartheta {\mathbf{p}}_{t-k}+{\left(1-\vartheta \right)}^t{\mathbf{p}}_0}{{\mathbf{p}}_t}$$

Fields

• v::T: Smoothing factor.

Examples

julia> using OnlinePortfolioSelection

julia> ema = EMA(0.5)
EMA{Float64}(0.5)

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OnlinePortfolioSelection.KMDLOG Type

KMDLOG<:ClusLogVariant

KMDLOG is a concrete type used to represent the KMDLOG Model. Also, see KMNLOG.

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OnlinePortfolioSelection.KMNLOG Type

KMNLOG<:ClusLogVariant

KMNLOG is a concrete type used to represent the KMNLOG Model. Also, see KMDLOG.

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OnlinePortfolioSelection.OPSAlgorithm Type

OPSAlgorithm{T<:AbstractFloat}

An object that contains the result of running the algorithm.

Fields

• n_asset::Int: Number of assets in the portfolio.

• b::Matrix{T}: Weights of the created portfolios.

• alg::String: Name of the algorithm.

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OnlinePortfolioSelection.OPSMetrics Type

OPSMetrics{T<:AbstractFloat}

A struct to store the metrics of the OPS algorithm. This object is returned by the opsmetrics function.

Fields

• Sn::Vector{T}: The cumulative wealth of investment during the investment period.

• MER::T: The investments's Mean excess return (MER).

• IR::T: The Information Ratio (IR) of portfolio for the investment period.

• APY::T: The Annual Percentage Yield (APY) of investment.

• Ann_Std::T: The Annualized Standard Deviation (σₚ) of investment.

• Ann_Sharpe::T: The Annualized Sharpe Ratio (SR) of investment.

• MDD::T: The Maximum Drawdown (MDD) of investment.

• Calmar::T: The Calmar Ratio of investment.

• AT::T: The Average Turnover (AT) of the investment.

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OnlinePortfolioSelection.PAMR Type

PAMR<: PAMRModel

Create a PAMR object. Also, see PAMR1, and PAMR2.

Example

model = PAMR()

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OnlinePortfolioSelection.PAMR1 Type

PAMR1{T<:AbstractFloat}<: PAMRModel

Create a PAMR1 object. Also, see PAMR, and PAMR2.

Keyword Arguments

• C::AbstractFloat=1.: Aggressiveness parameter.

Example

model = PAMR1(C=0.02)

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OnlinePortfolioSelection.PAMR2 Type

PAMR2{T<:AbstractFloat}<: PAMRModel

Create a PAMR2 object. Also, see PAMR, and PAMR1.

Keyword Arguments

• C::AbstractFloat=1.: Aggressiveness parameter.

Example

model = PAMR2(C=0.02)

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OnlinePortfolioSelection.PP Type

PP<:TrendRep

Pick Price trend representation. Formula:

$${\hat{\mathbf{x}}}_{M,t+1}\left(w\right)=\frac{\underset{0⩽k⩽w-1}{max}{\mathbf{p}}_{t-k}^{(i)}}{{\mathbf{p}}_t},i=1,2,\dots ,d$$

Examples

julia> using OnlinePortfolioSelection

julia> pp = PP()
PP()

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OnlinePortfolioSelection.SMAP Type

SMAP<:TrendRep

Simple Moving Average trend representation using the close prices. Formula:

$${\hat{\mathbf{x}}}_{S,t+1}\left(w\right)=\frac{\sum _{k=0}^{w-1}{\mathbf{p}}_{t-k}}{w{\mathbf{p}}_t}$$

Examples

julia> using OnlinePortfolioSelection

julia> sma = SMAP()
SMA()

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OnlinePortfolioSelection.SMAR Type

SMAR<:TrendRep

Simple Moving Average trend representation using the relative prices. Formula:

$$\mathbf{1}+\frac{\mathbf{1}}{{\mathbf{x}}_t}+\cdots +\frac{\mathbf{1}}{{\otimes }_{k=0}^{w-2}{\mathbf{x}}_{t-k}}$$

Examples

julia> using OnlinePortfolioSelection

julia> sma = SMAR()
SMAR()

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