Base

systematica.portfolio.frontier.base

BaseFrontier

BaseFrontier(
    alpha: float = 0.05,
    a_sim: int = 100,
    beta: float = None,
    b_sim: int = None,
    kappa: float = 0.3,
    model: str = 'Classic',
    rm: str = 'MV',
    kelly: str = None,
    rf: float = 0.0,
    hist: bool = True,
    solver: str = 'CLARABEL',
    constraints: List[Dict[str, str]] = None,
    asset_classes: dict = _Nothing.NOTHING,
    config: Dict[str, Any] = _Nothing.NOTHING,
)

Base class for portfolio objects with all properties needed to calculate optimal portfolios.

Risk Measures:

• MV: Standard Deviation.
• KT: Square Root of Kurtosis.
• MAD: Mean Absolute Deviation.
• GMD: Gini Mean Difference.
• MSV: Semi Standard Deviation.
• SKT: Square Root of Semi Kurtosis.
• FLPM: First Lower Partial Moment (Omega Ratio).
• SLPM: Second Lower Partial Moment (Sortino Ratio).
• CVaR: Conditional Value at Risk.
• TG: Tail Gini.
• EVaR: Entropic Value at Risk.
• RLVaR: Relativistic Value at Risk.
• WR: Worst Realization (Minimax).
• RG: Range of returns.
• CVRG: CVaR range of returns.
• TGRG: Tail Gini range of returns.
• EVRG: EVaR range of returns.
• RVRG: RLVaR range of returns. I recommend only use this function with MOSEK solver.
• MDD: Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).
• ADD: Average Drawdown of uncompounded cumulative returns.
• CDaR: Conditional Drawdown at Risk of uncompounded cumulative returns.
• EDaR: Entropic Drawdown at Risk of uncompounded cumulative returns.
• RLDaR: Relativistic Drawdown at Risk of uncompounded cumulative returns.
• UCI: Ulcer Index of uncompounded cumulative returns.

Method generated by attrs for class BaseFrontier.

Ancestors

• abc.ABC
• riskfolio.src.Portfolio.Portfolio

Descendants

• systematica.portfolio.frontier.models.FrontierAssetStats
• systematica.portfolio.frontier.models.FrontierBLFactorsStats
• systematica.portfolio.frontier.models.FrontierBLStats
• systematica.portfolio.frontier.models.FrontierFactorsStats
• systematica.portfolio.frontier.models.FrontierWCStats

Class variables

• alpha: float: Significance level of VaR, CVaR, EVaR, RLVaR, DaR, CDaR, EDaR, RLDaR and Tail Gini of losses. The default is 0.05.

• a_sim: int: Number of CVaRs used to approximate Tail Gini of losses. The default is 100.

• beta: float: Significance level of CVaR and Tail Gini of gains. If None it duplicates alpha value. The default is None.

• b_sim: int: Number of CVaRs used to approximate Tail Gini of gains. If None it duplicates a_sim value. The default is None.

• model: str: Methodology used to estimate input parameters. The default is Classic.

• rm: str: The risk measure used to optimize the portfolio. The default is MV. Possible values are displayed above.

• kelly: str: Method used to calculate mean return. Possible values are: None for arithmetic mean return, “approx” for approximate mean logarithmic return using first and second moment and “exact” for mean logarithmic return. The default is None.

• rf: float: Risk free rate. The default is 0.0.

• hist: bool: Indicate what kind of returns are used to calculate risk measures that depends on scenarios (All except MV risk measure). The default is True. If model = BL, True means historical covariance and returns and False Black Litterman covariance and historical returns. If model = FM, True means historical covariance and returns and False Risk Factor model for covariance and returns. If model = BL_FM, True means historical covariance and returns, False Black Litterman with Risk Factor model for covariance and Risk Factor model for returns, and 2 Risk Factor model for covariance and returns.

• solver: str: Solver available for CVXPY that supports power cone programming. Used to calculate RLVaR and RLDaR. The default value is CLARABEL.

• constraints: List[Dict[str, str]]: Risk contribution constraints. Defaults to None.

• asset_classes: dict: Asset classes.

• config: Dict[str, Any]: Riskfolio-lib configuration. Overwrite other required parameters if referenced.

Instance variables

• kappa: float: Deformation parameter of RLVaR and RLDaR for losses, must be between 0 and 1. The default is 0.30.

• freq: Infer the frequency of the returns index.

• ann_factor: Calculate the annualization factor based on the frequency.

• x_axis: str: Generate the label for the x-axis based on the risk measure.

• y_axis: str: Generate the label for the y-axis based on the Kelly criterion.

• z_axis: str: Generate the label for the z-axis.

Methods

init

init(
    self,
    market_returns: pandas.core.frame.DataFrame,
)

Initialize the portfolio object with market returns.

Parameters:

Name	Type	Default	Description
market_returns	pd.DataFrame	--	DataFrame containing market returns data.

Raises:

Type	Description
ValueError	If the cleaned market returns still contain NaN values.

fit

fit(
    self,
    market_returns: pandas.core.frame.DataFrame,
) ‑> systematica.portfolio.frontier.base.BaseFrontier

Abstract method to fit the portfolio model.

Parameters:

Name	Type	Default	Description
market_returns	pd.DataFrame	--	Market returns data.

Returns:

Type	Description
BaseFrontier	Fitted portfolio object.

add_constraints

add_constraints(
    self,
) ‑> systematica.portfolio.frontier.base.BaseFrontier

Add inequality constraints to the portfolio.

Returns:

Type	Description
BaseFrontier	Portfolio object with added constraints.

predict

predict(
    self,
    points: int = 20,
) ‑> pandas.core.frame.DataFrame

Predict the efficient frontier.

Parameters:

Name	Type	Default	Description
points	int	20	Number of points to calculate on the frontier, by default 20.

Returns:

Type	Description
pd.DataFrame	DataFrame containing the efficient frontier.

get_portfolio

get_portfolio(
    self,
    weights: pandas.core.series.Series,
) ‑> pandas.core.frame.DataFrame

Calculate the portfolio performance based on the given weights.

Parameters:

Name	Type	Default	Description
weights	pd.Series	--	Portfolio weights.

Returns:

Type	Description
pd.DataFrame	Target portfolio.

get_efficient_frontier

get_efficient_frontier(
    self,
    points: int = 20,
) ‑> pandas.core.frame.DataFrame

Calculate the efficient frontier.

Parameters:

Name	Type	Default	Description
points	int	20	Number of points to calculate on the frontier, by default 20.

Returns:

Type	Description
pd.DataFrame	Efficient frontier.

plot_frontier

plot_frontier(
    self,
    weights,
    display_frontier: bool = True,
    points: int = 20,
    portfolio_name: str = 'Target Portfolio',
    marker_color: str = 'red',
    marker_size: int = 20,
    make_figure_kwargs: Dict[str, Any] | None = None,
    fig: plotly.basedatatypes.BaseFigure = None,
    showscale: bool = True,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the efficient frontier.

Parameters:

Name	Type	Default	Description
weights	pd.Series	--	Portfolio weights.
display_frontier	bool	True	Display frontier. Defaults to True.
portfolio_name	str	Target...	Portfolio name. Defaults to “Target Portfolio”.
marker_color	str	red	Color of the portfolio star shaped marker. Defaults to “red”.
marker_size	int	20	Size of the portfolio star shaped marker. Default to 20.
make_figure_kwargs	tp.KwargsLike	None	Additional figure arguments, by default None.
fig	tp.BaseFigure	None	Existing figure to update, by default None.
showscale	bool	True	Whether to show the scale, by default True.
layout_kwargs	tp.Kwargs	--	Additional layout arguments for the figure.

Returns:

Type	Description
vbt.FigureWidget	A Plotly FigureWidget containing the frontier plot.