<a id="skfolio-portfolio-baseportfolio"></a>

# skfolio.portfolio.BasePortfolio

<a id="skfolio.portfolio.BasePortfolio"></a>

### *class* skfolio.portfolio.BasePortfolio(returns, observations, name=None, tag=None, annualization_factor=None, fitness_measures=None, risk_free_rate=0.0, compounded=False, sample_weight=None, min_acceptable_return=None, value_at_risk_beta=0.95, entropic_risk_measure_theta=1.0, 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, \*\*kwargs)

Base Portfolio class for all portfolios in skfolio.

* **Parameters:**
  **returns** *array-like of shape (n_observations,)*
  : Vector of portfolio returns.

  **observations** *array-like of shape (n_observations,)*
  : Vector of portfolio observations.

  **name** *str, optional*
  : Name of the portfolio.
    The default (`None`) is to use the object id.

  **tag** *str, optional*
  : Tag given to the portfolio.
    Tags are used to manipulate groups of Portfolios from a `Population`.

  **fitness_measures** *list[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]

  **annualization_factor** *float, 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_rate** *float, default=0.0*
  : Risk-free rate. The default value is `0.0`.

  **compounded** *bool, default=False*
  : If this is set to True, cumulative returns are compounded.
    The default is `False`.

  **sample_weight** *ndarray of shape (n_observations,), optional*
  : Sample weights for each observation. The weights must sum to one.
    : If None, equal weights are assumed.

  **min_acceptable_return** *float, 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
    <br/>
    The default (`None`) is to use the mean.

  **value_at_risk_beta** *float, 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_theta** *float, default=1.0*
  : The risk aversion level of the Portfolio Entropic Risk Measure.
    The default value is `1.0`.

  **entropic_risk_measure_beta** *float, default=0.95*
  : The confidence level of the Portfolio Entropic Risk Measure.
    The default value is `0.95`.

  **cvar_beta** *float, 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_beta** *float, default=0.95*
  : The confidence level of the Portfolio EVaR (Entropic Value at Risk).
    The default value is `0.95`.

  **drawdown_at_risk_beta** *float, 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_beta** *float, 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_beta** *float, default=0.95*
  : The confidence level of the Portfolio EDaR (Entropic Drawdown at Risk).
    The default value is `0.95`.
* **Attributes:**
  [`n_observations`](#skfolio.portfolio.BasePortfolio.n_observations) *float*
  : Number of observations.

  **mean** *float*
  : Mean of the portfolio returns.

  **annualized_mean** *float*
  : Mean annualized by $mean \times annualization\_factor$

  **mean_absolute_deviation** *float*
  : Mean Absolute Deviation. The deviation is the difference between the
    return and a minimum acceptable return (`min_acceptable_return`).

  **first_lower_partial_moment** *float*
  : First Lower Partial Moment. The First Lower Partial Moment is the mean of the
    returns below a minimum acceptable return (`min_acceptable_return`).

  **variance** *float*
  : Variance (Second Moment)

  **annualized_variance** *float*
  : Variance annualized by $variance \times annualization\_factor$

  **semi_variance** *float*
  : 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_variance** *float*
  : Semi-variance annualized by
    $semi\_variance \times annualization\_factor$

  **standard_deviation** *float*
  : Standard Deviation (Square Root of the Second Moment).

  **annualized_standard_deviation** *float*
  : Standard Deviation annualized by
    $standard\_deviation \times \sqrt{annualization\_factor}$

  **semi_deviation** *float*
  : 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_deviation** *float*
  : Semi-deviation annualized by
    $semi\_deviation \times \sqrt{annualization\_factor}$

  **skew** *float*
  : 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.

  **kurtosis** *float*
  : 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_moment** *float*
  : Fourth Central Moment.

  **fourth_lower_partial_moment** *float*
  : 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_realization** *float*
  : Worst Realization which is the worst return.

  **value_at_risk** *float*
  : Historical VaR (Value at Risk).
    The VaR is the maximum loss at a given confidence level (`value_at_risk_beta`).

  **cvar** *float*
  : Historical CVaR (Conditional Value at Risk). The CVaR (or Tail VaR) represents
    the mean shortfall at a specified confidence level (`cvar_beta`).

  **entropic_risk_measure** *float*
  : 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`).

  **evar** *float*
  : 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_risk** *float*
  : Historical Drawdown at Risk. It is the maximum drawdown at a given
    confidence level (`drawdown_at_risk_beta`).

  **cdar** *float*
  : Historical CDaR (Conditional Drawdown at Risk) at a given confidence level
    (`cdar_beta`).

  **max_drawdown** *float*
  : Maximum Drawdown.

  **average_drawdown** *float*
  : Average Drawdown.

  **edar** *float*
  : 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_index** *float*
  : Ulcer Index

  **gini_mean_difference** *float*
  : 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_ratio** *float*
  : Mean Absolute Deviation ratio.
    It is the excess mean (mean - risk_free_rate) divided by the MaD.

  **first_lower_partial_moment_ratio** *float*
  : First Lower Partial Moment ratio.
    It is the excess mean (mean - risk_free_rate) divided by the First Lower
    Partial Moment.

  **sharpe_ratio** *float*
  : Sharpe ratio.
    It is the excess mean (mean - risk_free_rate) divided by the standard-deviation.

  **annualized_sharpe_ratio** *float*
  : Sharpe ratio annualized by
    $sharpe\_ratio \times \sqrt{annualization\_factor}$.

  **sortino_ratio** *float*
  : Sortino ratio.
    It is the excess mean (mean - risk_free_rate) divided by the semi
    standard-deviation.

  **annualized_sortino_ratio** *float*
  : Sortino ratio annualized by
    $sortino\_ratio \times \sqrt{annualization\_factor}$.

  **value_at_risk_ratio** *float*
  : VaR ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Value at Risk
    (VaR).

  **cvar_ratio** *float*
  : CVaR ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Conditional Value
    at Risk (CVaR).

  **entropic_risk_measure_ratio** *float*
  : Entropic risk measure ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Entropic risk
    measure.

  **evar_ratio** *float*
  : EVaR ratio.
    It is the excess mean (mean - risk_free_rate) divided by the EVaR (Entropic
    Value at Risk).

  **worst_realization_ratio** *float*
  : Worst Realization ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Worst Realization
    (worst return).

  **drawdown_at_risk_ratio** *float*
  : Drawdown at Risk ratio.
    It is the excess mean (mean - risk_free_rate) divided by the drawdown at
    risk.

  **cdar_ratio** *float*
  : CDaR ratio.
    It is the excess mean (mean - risk_free_rate) divided by the CDaR (conditional
    drawdown at risk).

  **calmar_ratio** *float*
  : Calmar ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Maximum Drawdown.

  **average_drawdown_ratio** *float*
  : Average Drawdown ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Average Drawdown.

  **edar_ratio** *float*
  : EDaR ratio.
    It is the excess mean (mean - risk_free_rate) divided by the EDaR (Entropic
    Drawdown at Risk).

  **ulcer_index_ratio** *float*
  : Ulcer Index ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Ulcer Index.

  **gini_mean_difference_ratio** *float*
  : Gini Mean Difference ratio.
    It is the excess mean (mean - risk_free_rate) divided by the Gini Mean
    Difference.

### Methods

| [`clear`](#skfolio.portfolio.BasePortfolio.clear)()                                        | Clear all measures, fitness, cumulative returns and drawdowns in slots.   |
|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| [`contribution`](#skfolio.portfolio.BasePortfolio.contribution)(measure[, spacing, to_df])        | Compute the contribution of each asset to a given measure.                |
| [`copy`](#skfolio.portfolio.BasePortfolio.copy)()                                         | Copy the Portfolio attributes without its measures values.                |
| [`dominates`](#skfolio.portfolio.BasePortfolio.dominates)(other[, idx])                        | Portfolio domination.                                                     |
| [`get_measure`](#skfolio.portfolio.BasePortfolio.get_measure)(measure)                           | Returns the value of a given measure.                                     |
| [`plot_composition`](#skfolio.portfolio.BasePortfolio.plot_composition)()                             | Plot the Portfolio composition.                                           |
| [`plot_contribution`](#skfolio.portfolio.BasePortfolio.plot_contribution)(measure[, spacing])          | Plot the contribution of each asset to a given measure.                   |
| [`plot_cumulative_returns`](#skfolio.portfolio.BasePortfolio.plot_cumulative_returns)([log_scale, idx])      | Plot the Portfolio cumulative returns.                                    |
| [`plot_drawdowns`](#skfolio.portfolio.BasePortfolio.plot_drawdowns)([idx])                          | Plot the Portfolio drawdowns.                                             |
| [`plot_returns`](#skfolio.portfolio.BasePortfolio.plot_returns)([idx])                            | Plot the Portfolio returns.                                               |
| [`plot_returns_distribution`](#skfolio.portfolio.BasePortfolio.plot_returns_distribution)([percentile_cutoff]) | Plot the Portfolio returns distribution using Gaussian KDE.               |
| [`plot_rolling_measure`](#skfolio.portfolio.BasePortfolio.plot_rolling_measure)([measure, window])        | Plot the measure over a rolling window.                                   |
| [`rolling_measure`](#skfolio.portfolio.BasePortfolio.rolling_measure)([measure, window])             | Compute the measure over a rolling window.                                |
| [`summary`](#skfolio.portfolio.BasePortfolio.summary)([formatted])                           | Portfolio summary of all its measures.                                    |

<a id="skfolio.portfolio.BasePortfolio.annualization_factor"></a>

#### *property* annualization_factor

Portfolio annualization factor.

<a id="skfolio.portfolio.BasePortfolio.annualized_factor"></a>

#### *property* annualized_factor

Deprecated alias for `annualization_factor`.

<a id="skfolio.portfolio.BasePortfolio.clear"></a>

#### clear()

Clear all measures, fitness, cumulative returns and drawdowns in slots.

<a id="skfolio.portfolio.BasePortfolio.composition"></a>

#### *abstract property* composition

DataFrame of the Portfolio composition.

<a id="skfolio.portfolio.BasePortfolio.contribution"></a>

#### *abstractmethod* contribution(measure, spacing=None, to_df=True)

Compute the contribution of each asset to a given measure.

<a id="skfolio.portfolio.BasePortfolio.copy"></a>

#### copy()

Copy the Portfolio attributes without its measures values.

<a id="skfolio.portfolio.BasePortfolio.cumulative_returns"></a>

#### cumulative_returns

Portfolio cumulative returns array.
Non-compounded (arithmetic) cumulative returns start at 0.
Compounded (geometric) cumulative returns are expressed as a wealth index,
starting at 1.0 (i.e., the value of $1 invested).

<a id="skfolio.portfolio.BasePortfolio.cumulative_returns_df"></a>

#### *property* cumulative_returns_df

Portfolio cumulative returns Series.
Non-compounded (arithmetic) cumulative returns start at 0.
Compounded (geometric) cumulative returns are expressed as a wealth index,
starting at 1.0 (i.e., the value of $1 invested).

<a id="skfolio.portfolio.BasePortfolio.dominates"></a>

#### 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:**
  **other** *BasePortfolio*
  : The other portfolio.

  **idx** *slice | array, optional*
  : Indexes or slice indicating on which objectives the domination is performed.
    The default (`None`) is to use all objectives.
* **Returns:**
  **value** *bool*
  : Returns True if the Portfolio dominates the other one.

<a id="skfolio.portfolio.BasePortfolio.drawdowns"></a>

#### drawdowns

Portfolio drawdowns array.

<a id="skfolio.portfolio.BasePortfolio.drawdowns_df"></a>

#### *property* drawdowns_df

Portfolio drawdowns Series.

<a id="skfolio.portfolio.BasePortfolio.fitness"></a>

#### fitness

Portfolio fitness.

<a id="skfolio.portfolio.BasePortfolio.fitness_measures"></a>

#### *property* fitness_measures

Portfolio fitness measures.

<a id="skfolio.portfolio.BasePortfolio.get_measure"></a>

#### get_measure(measure)

Returns the value of a given measure.

* **Parameters:**
  **measure** *PerfMeasure | RiskMeasure | ExtraRiskMeasure | RatioMeasure*
  : The input measure.
* **Returns:**
  **value** *float*
  : The measure value.

<a id="skfolio.portfolio.BasePortfolio.measures_df"></a>

#### *property* measures_df

DataFrame of all measures.

<a id="skfolio.portfolio.BasePortfolio.n_observations"></a>

#### *property* n_observations

Number of observations.

<a id="skfolio.portfolio.BasePortfolio.plot_composition"></a>

#### plot_composition()

Plot the Portfolio composition.

* **Returns:**
  **plot** *Figure*
  : Returns the plot Figure object.

<a id="skfolio.portfolio.BasePortfolio.plot_contribution"></a>

#### plot_contribution(measure, spacing=None)

Plot the contribution of each asset to a given measure.

* **Parameters:**
  **measure** *Measure*
  : The measure used for the contribution computation.

  **spacing** *float, optional*
  : Spacing “h” of the finite difference:
    $contribution(wi)= \frac{measure(wi-h) - measure(wi+h)}{2h}$
* **Returns:**
  **plot** *Figure*
  : The plotly Figure of assets contribution to the measure.

<a id="skfolio.portfolio.BasePortfolio.plot_cumulative_returns"></a>

#### plot_cumulative_returns(log_scale=False, idx=None)

Plot the Portfolio cumulative returns.
Non-compounded (arithmetic) cumulative returns start at 0.
Compounded (geometric) cumulative returns are expressed as a wealth index,
starting at 1.0 (i.e., the value of $1 invested).

* **Parameters:**
  **log_scale** *bool, default=False*
  : If this is set to True, the cumulative returns are displayed with a
    logarithm scale on the y-axis. The cumulative returns must be compounded
    otherwise an exception is raised.

  **idx** *slice | array, optional*
  : Indexes or slice of the observations to plot.
    The default (`None`) is to plot all observations.
* **Returns:**
  **plot** *Figure*
  : Returns the plot Figure object.

<a id="skfolio.portfolio.BasePortfolio.plot_drawdowns"></a>

#### plot_drawdowns(idx=None)

Plot the Portfolio drawdowns.

* **Parameters:**
  **idx** *slice | array, optional*
  : Indexes or slice of the observations to plot.
    The default (`None`) is to plot all observations.
* **Returns:**
  **plot** *Figure*
  : Returns the plot Figure object.

<a id="skfolio.portfolio.BasePortfolio.plot_returns"></a>

#### plot_returns(idx=None)

Plot the Portfolio returns.

* **Parameters:**
  **idx** *slice | array, optional*
  : Indexes or slice of the observations to plot.
    The default (`None`) is to plot all observations.
* **Returns:**
  **plot** *Figure*
  : Returns the plot Figure object

<a id="skfolio.portfolio.BasePortfolio.plot_returns_distribution"></a>

#### plot_returns_distribution(percentile_cutoff=None)

Plot the Portfolio returns distribution using Gaussian KDE.

* **Parameters:**
  **percentile_cutoff** *float, default=None*
  : Percentile cutoff for tail truncation (percentile), in percent.
    If a float p is provided, the distribution support is truncated at the p-th
    and (100 - p)-th percentiles.
    If None, no truncation is applied (uses full min/max of returns).
* **Returns:**
  **plot** *Figure*
  : Returns the plot Figure object

<a id="skfolio.portfolio.BasePortfolio.plot_rolling_measure"></a>

#### plot_rolling_measure(measure=Sharpe Ratio, window=30)

Plot the measure over a rolling window.

* **Parameters:**
  **measure** *Measure, default = RatioMeasure.SHARPE_RATIO*
  : The measure.

  **window** *int, default=30*
  : The window size.
* **Returns:**
  **plot** *Figure*
  : Returns the plot Figure object

<a id="skfolio.portfolio.BasePortfolio.returns_df"></a>

#### *property* returns_df

Portfolio returns DataFrame.

<a id="skfolio.portfolio.BasePortfolio.rolling_measure"></a>

#### rolling_measure(measure=Sharpe Ratio, window=30)

Compute the measure over a rolling window.

* **Parameters:**
  **measure** *Measure, default=RatioMeasure.SHARPE_RATIO*
  : The measure. The default measure is the Sharpe Ratio.

  **window** *int, default=30*
  : The window size. The default value is `30` observations.
* **Returns:**
  **series** *pandas Series*
  : The rolling measure Series.

<a id="skfolio.portfolio.BasePortfolio.sample_weight"></a>

#### *property* sample_weight

Observations sample weights.

<a id="skfolio.portfolio.BasePortfolio.summary"></a>

#### summary(formatted=True)

Portfolio summary of all its measures.

* **Parameters:**
  **formatted** *bool, default=True*
  : If this is set to True, the measures are formatted into rounded string
    with units.
* **Returns:**
  **summary** *pandas Series*
  : The Portfolio summary.