skfolio.portfolio
.MultiPeriodPortfolio#
- class skfolio.portfolio.MultiPeriodPortfolio(portfolios=None, name=None, tag=None, risk_free_rate=0, annualized_factor=252.0, fitness_measures=None, compounded=False, min_acceptable_return=None, value_at_risk_beta=0.95, entropic_risk_measure_theta=1, entropic_risk_measure_beta=0.95, cvar_beta=0.95, evar_beta=0.95, drawdown_at_risk_beta=0.95, cdar_beta=0.95, edar_beta=0.95, check_observations_order=False)[source]#
Multi-Period Portfolio class.
A Multi-Period Portfolio is composed of a list of
Portfolio
.- Parameters:
- portfolioslist[Portfolio], optional
A list of
Portfolio
. The default (None
) is to initialize with an empty list.- namestr, optional
Name of the multi-period portfolio. The default (
None
) is to use the object id.- tagstr, optional
Tag given to the multi-period portfolio. Tags are used to manipulate groups of portfolios from a
Population
.- fitness_measureslist[measures], optional
List of fitness measures. Fitness measures are used to compute the portfolio fitness which is used to compute domination. The default (
None
) is to use the list [PerfMeasure.MEAN, RiskMeasure.VARIANCE]- annualized_factorfloat, default=252.0
Factor used to annualize the below measures using the square-root rule:
Annualized Mean = Mean * factor
Annualized Variance = Variance * factor
Annualized Semi-Variance = Semi-Variance * factor
Annualized Standard-Deviation = Standard-Deviation * sqrt(factor)
Annualized Semi-Deviation = Semi-Deviation * sqrt(factor)
Annualized Sharpe Ratio = Sharpe Ratio * sqrt(factor)
Annualized Sortino Ratio = Sortino Ratio * sqrt(factor)
- risk_free_ratefloat, default=0.0
Risk-free rate. The default value is
0.0
.- compoundedbool, default=False
If this is set to True, cumulative returns are compounded. The default is
False
.- min_acceptable_returnfloat, optional
The minimum acceptable return used to distinguish “downside” and “upside” returns for the computation of lower partial moments:
First Lower Partial Moment
Semi-Variance
Semi-Deviation
The default (
None
) is to use the mean.- value_at_risk_betafloat, default=0.95
The confidence level of the portfolio VaR (Value At Risk) which represents the return on the worst (1-beta)% observations. The default value is
0.95
.- entropic_risk_measure_thetafloat, default=1.0
The risk aversion level of the portfolio Entropic Risk Measure. The default value is
1.0
.- entropic_risk_measure_betafloat, default=0.95
The confidence level of the portfolio Entropic Risk Measure. The default value is
0.95
.- cvar_betafloat, default=0.95
The confidence level of the portfolio CVaR (Conditional Value at Risk) which represents the expected VaR on the worst (1-beta)% observations. The default value is
0.95
.- evar_betafloat, default=0.95
The confidence level of the portfolio EVaR (Entropic Value at Risk). The default value is
0.95
.- drawdown_at_risk_betafloat, default=0.95
The confidence level of the portfolio Drawdown at Risk (DaR) which represents the drawdown on the worst (1-beta)% observations. The default value is
0.95
.- cdar_betafloat, default=0.95
The confidence level of the portfolio CDaR (Conditional Drawdown at Risk) which represents the expected drawdown on the worst (1-beta)% observations. The default value is
0.95
.- edar_betafloat, default=0.95
The confidence level of the portfolio EDaR (Entropic Drawdown at Risk). The default value is
0.95
.- check_observations_orderbool, default=False
If this is set to True, and if the list of portfolios is not chronologically sorted, an error is raised. The chronological order is determined by comparing the first and last observations of each portfolio. The default is
False
.
- Attributes:
n_observations
floatNumber of observations
- meanfloat
Mean of the portfolio returns.
- annualized_meanfloat
Mean annualized by \(mean \times annualization\_factor\)
- mean_absolute_deviationfloat
Mean Absolute Deviation. The deviation is the difference between the return and a minimum acceptable return (
min_acceptable_return
).- first_lower_partial_momentfloat
First Lower Partial Moment. The First Lower Partial Moment is the mean of the returns below a minimum acceptable return (
min_acceptable_return
).- variancefloat
Variance (Second Moment)
- annualized_variancefloat
Variance annualized by \(variance \times annualization\_factor\)
- semi_variancefloat
Semi-variance (Second Lower Partial Moment). The semi-variance is the variance of the returns below a minimum acceptable return (
min_acceptable_return
).- annualized_semi_variancefloat
Semi-variance annualized by \(semi\_variance \times annualization\_factor\)
- standard_deviationfloat
Standard Deviation (Square Root of the Second Moment).
- annualized_standard_deviationfloat
Standard Deviation annualized by \(standard\_deviation \times \sqrt{annualization\_factor}\)
- semi_deviationfloat
Semi-deviation (Square Root of the Second Lower Partial Moment). The Semi Standard Deviation is the Standard Deviation of the returns below a minimum acceptable return (
min_acceptable_return
).- annualized_semi_deviationfloat
Semi-deviation annualized by \(semi\_deviation \times \sqrt{annualization\_factor}\)
- skewfloat
Skew. The Skew is a measure of the lopsidedness of the distribution. A symmetric distribution have a Skew of zero. Higher Skew corresponds to longer right tail.
- kurtosisfloat
Kurtosis. It is a measure of the heaviness of the tail of the distribution. Higher Kurtosis corresponds to greater extremity of deviations (fat tails).
- fourth_central_momentfloat
Fourth Central Moment.
- fourth_lower_partial_momentfloat
Fourth Lower Partial Moment. It is a measure of the heaviness of the downside tail of the returns below a minimum acceptable return (
min_acceptable_return
). Higher Fourth Lower Partial Moment corresponds to greater extremity of downside deviations (downside fat tail).- worst_realizationfloat
Worst Realization which is the worst return.
- value_at_riskfloat
Historical VaR (Value at Risk). The VaR is the maximum loss at a given confidence level (
value_at_risk_beta
).- cvarfloat
Historical CVaR (Conditional Value at Risk). The CVaR (or Tail VaR) represents the mean shortfall at a specified confidence level (
cvar_beta
).- entropic_risk_measurefloat
Historical Entropic Risk Measure. It is a risk measure which depends on the risk aversion defined by the investor (
entropic_risk_measure_theta
) through the exponential utility function at a given confidence level (entropic_risk_measure_beta
).- evarfloat
Historical EVaR (Entropic Value at Risk). It is a coherent risk measure which is an upper bound for the VaR and the CVaR, obtained from the Chernoff inequality at a given confidence level (
evar_beta
). The EVaR can be represented by using the concept of relative entropy.- drawdown_at_riskfloat
Historical Drawdown at Risk. It is the maximum drawdown at a given confidence level (
drawdown_at_risk_beta
).- cdarfloat
Historical CDaR (Conditional Drawdown at Risk) at a given confidence level (
cdar_beta
).- max_drawdownfloat
Maximum Drawdown.
- average_drawdownfloat
Average Drawdown.
- edarfloat
EDaR (Entropic Drawdown at Risk). It is a coherent risk measure which is an upper bound for the Drawdown at Risk and the CDaR, obtained from the Chernoff inequality at a given confidence level (
edar_beta
). The EDaR can be represented by using the concept of relative entropy.- ulcer_indexfloat
Ulcer Index
- gini_mean_differencefloat
Gini Mean Difference (GMD). It is the expected absolute difference between two realizations. The GMD is a superior measure of variability for non-normal distribution than the variance. It can be used to form necessary conditions for second-degree stochastic dominance, while the variance cannot.
- mean_absolute_deviation_ratiofloat
Mean Absolute Deviation ratio. It is the excess mean (mean - risk_free_rate) divided by the MaD.
- first_lower_partial_moment_ratiofloat
First Lower Partial Moment ratio. It is the excess mean (mean - risk_free_rate) divided by the First Lower Partial Moment.
- sharpe_ratiofloat
Sharpe ratio. It is the excess mean (mean - risk_free_rate) divided by the standard-deviation.
- annualized_sharpe_ratiofloat
Sharpe ratio annualized by \(sharpe\_ratio \times \sqrt{annualization\_factor}\).
- sortino_ratiofloat
Sortino ratio. It is the excess mean (mean - risk_free_rate) divided by the semi standard-deviation.
- annualized_sortino_ratiofloat
Sortino ratio annualized by \(sortino\_ratio \times \sqrt{annualization\_factor}\).
- value_at_risk_ratiofloat
VaR ratio. It is the excess mean (mean - risk_free_rate) divided by the Value at Risk (VaR).
- cvar_ratiofloat
CVaR ratio. It is the excess mean (mean - risk_free_rate) divided by the Conditional Value at Risk (CVaR).
- entropic_risk_measure_ratiofloat
Entropic risk measure ratio. It is the excess mean (mean - risk_free_rate) divided by the Entropic risk measure.
- evar_ratiofloat
EVaR ratio. It is the excess mean (mean - risk_free_rate) divided by the EVaR (Entropic Value at Risk).
- worst_realization_ratiofloat
Worst Realization ratio. It is the excess mean (mean - risk_free_rate) divided by the Worst Realization (worst return).
- drawdown_at_risk_ratiofloat
Drawdown at Risk ratio. It is the excess mean (mean - risk_free_rate) divided by the drawdown at risk.
- cdar_ratiofloat
CDaR ratio. It is the excess mean (mean - risk_free_rate) divided by the CDaR (conditional drawdown at risk).
- calmar_ratiofloat
Calmar ratio. It is the excess mean (mean - risk_free_rate) divided by the Maximum Drawdown.
- average_drawdown_ratiofloat
Average Drawdown ratio. It is the excess mean (mean - risk_free_rate) divided by the Average Drawdown.
- edar_ratiofloat
EDaR ratio. It is the excess mean (mean - risk_free_rate) divided by the EDaR (Entropic Drawdown at Risk).
- ulcer_index_ratiofloat
Ulcer Index ratio. It is the excess mean (mean - risk_free_rate) divided by the Ulcer Index.
- gini_mean_difference_ratiofloat
Gini Mean Difference ratio. It is the excess mean (mean - risk_free_rate) divided by the Gini Mean Difference.
Methods
append
(portfolio)Append a Portfolio to the Portfolio list.
clear
()Clear all measures, fitness, cumulative returns and drawdowns in slots
contribution
(measure[, spacing, to_df])Compute the contribution of each asset to a given measure for each portfolio.
copy
()Copy the Portfolio attributes without its measures values.
dominates
(other[, idx])Portfolio domination.
get_measure
(measure)Returns the value of a given measure.
Plot the Portfolio composition.
plot_contribution
(measure[, spacing])Plot the contribution of each asset to a given measure.
plot_cumulative_returns
([log_scale, idx])Plot the Portfolio cumulative returns.
plot_returns
([idx])Plot the Portfolio returns
plot_rolling_measure
([measure, window])Plot the measure over a rolling window.
rolling_measure
([measure, window])Compute the measure over a rolling window.
summary
([formatted])Portfolio summary of all its measures.
- property annualized_factor#
Portfolio annualized factor.
- append(portfolio)[source]#
Append a Portfolio to the Portfolio list.
- Parameters:
- portfolioPortfolio
The Portfolio to append.
- property assets#
List of assets names in each Portfolio.
- clear()#
Clear all measures, fitness, cumulative returns and drawdowns in slots
- property composition#
DataFrame of the Portfolio composition.
- contribution(measure, spacing=None, to_df=True)[source]#
Compute the contribution of each asset to a given measure for each portfolio.
- Parameters:
- measureMeasure
The measure used for the contribution computation.
- spacingfloat, optional
Spacing “h” of the finite difference: \(contribution(wi)= \frac{measure(wi-h) - measure(wi+h)}{2h}\)
- to_dfbool, default=False
If this is set to True, a DataFrame with asset names in index and portfolio names in columns is returned, otherwise a list of numpy array is returned. When a DataFrame is returned, the assets with zero weights are removed.
- Returns:
- valueslist of numpy array of shape (n_assets,) for each portfolio or a DataFrame
The measure contribution of each asset for each portfolio.
- copy()#
Copy the Portfolio attributes without its measures values.
- cumulative_returns#
Portfolio cumulative returns array.
- property cumulative_returns_df#
Portfolio cumulative returns Series.
- dominates(other, idx=None)#
Portfolio domination.
Returns true if each objective of the current portfolio fitness is not strictly worse than the corresponding objective of the other portfolio fitness and at least one objective is strictly better.
- Parameters:
- otherBasePortfolio
The other portfolio.
- idxslice | array, optional
Indexes or slice indicating on which objectives the domination is performed. The default (
None
) is to use all objectives.
- Returns:
- valuebool
Returns True if the Portfolio dominates the other one.
- drawdowns#
Portfolio drawdowns array.
- fitness#
The Portfolio fitness.
- property fitness_measures#
Portfolio fitness measures.
- get_measure(measure)#
Returns the value of a given measure.
- Parameters:
- measurePerfMeasure | RiskMeasure | ExtraRiskMeasure | RatioMeasure
The input measure.
- Returns:
- valuefloat
The measure value.
- property measures_df#
DataFrame of all measures.
- property n_observations#
Number of observations
- plot_composition()#
Plot the Portfolio composition.
- Returns:
- plotFigure
Returns the plot Figure object.
- plot_contribution(measure, spacing=None)#
Plot the contribution of each asset to a given measure.
- Parameters:
- measureMeasure
The measure used for the contribution computation.
- spacingfloat, optional
Spacing “h” of the finite difference: \(contribution(wi)= \frac{measure(wi-h) - measure(wi+h)}{2h}\)
- Returns:
- plotFigure
The plotly Figure of assets contribution to the measure.
- plot_cumulative_returns(log_scale=False, idx=None)#
Plot the Portfolio cumulative returns. Non-compounded cumulative returns start at 0. Compounded cumulative returns are rescaled to start at 1000.
- Parameters:
- log_scalebool, default=False
If this is set to True, the cumulative returns are displayed with a logarithm scale on the y-axis and rebased at 1000. The cumulative returns must be compounded otherwise an exception is raised.
- idxslice | array, optional
Indexes or slice of the observations to plot. The default (
None
) is to plot all observations.
- Returns:
- plotFigure
Returns the plot Figure object.
- plot_returns(idx=None)#
Plot the Portfolio returns
- Parameters:
- idxslice | array, optional
Indexes or slice of the observations to plot. The default (
None
) is to plot all observations.
- Returns:
- plotFigure
Returns the plot Figure object
- plot_rolling_measure(measure=Sharpe Ratio, window=30)#
Plot the measure over a rolling window.
- Parameters:
- measurect.Measure, default = RatioMeasure.SHARPE_RATIO
The measure.
- windowint, default=30
The window size.
- Returns:
- plotFigure
Returns the plot Figure object
- property portfolios#
List of portfolios composing the mutli-period portfolio.
- property returns_df#
Portfolio returns DataFrame.
- rolling_measure(measure=Sharpe Ratio, window=30)#
Compute the measure over a rolling window.
- Parameters:
- measurect.Measure, default=RatioMeasure.SHARPE_RATIO
The measure. The default measure is the Sharpe Ratio.
- windowint, default=30
The window size. The default value is
30
observations.
- Returns:
- seriespandas Series
The rolling measure Series.
- summary(formatted=True)[source]#
Portfolio summary of all its measures.
- Parameters:
- formattedbool, default=True
If this is set to True, the measures are formatted into rounded string with units.
- Returns:
- summaryseries
Portfolio summary of all its measures.
- property weights_per_observation#
DataFrame of the Portfolio weights per observation.