Help on module portfolioopt.portfolioopt in portfolioopt:

NAME
    portfolioopt.portfolioopt - PortfolioOpt: Financial Portfolio Optimization

FILE
    /usr/local/lib/python2.7/dist-packages/portfolioopt-0.3.3-py2.7.egg/portfolioopt/portfolioopt.py

DESCRIPTION
    This module provides a set of functions for financial portfolio
    optimization, such as construction of Markowitz portfolios, minimum
    variance portfolios and tangency portfolios (i.e. maximum Sharpe ratio
    portfolios) in Python. The construction of long-only, long/short and
    market neutral portfolios is supported.

FUNCTIONS
    markowitz_portfolio(cov_mat, exp_rets, target_ret, allow_short=False, market_neutral=False)
        Computes a Markowitz portfolio.
        
        Parameters
        ----------
        cov_mat: pandas.DataFrame
            Covariance matrix of asset returns.
        exp_rets: pandas.Series
            Expected asset returns (often historical returns).
        target_ret: float
            Target return of portfolio.
        allow_short: bool, optional
            If 'False' construct a long-only portfolio.
            If 'True' allow shorting, i.e. negative weights.
        market_neutral: bool, optional
            If 'False' sum of weights equals one.
            If 'True' sum of weights equal zero, i.e. create a
                market neutral portfolio (implies allow_short=True).
                
        Returns
        -------
        weights: pandas.Series
            Optimal asset weights.
    
    max_ret_portfolio(exp_rets)
        Computes a long-only maximum return portfolio, i.e. selects
        the assets with maximal return. If there is more than one
        asset with maximal return, equally weight all of them.
        
        Parameters
        ----------
        exp_rets: pandas.Series
            Expected asset returns (often historical returns).
        
        Returns
        -------
        weights: pandas.Series
            Optimal asset weights.
    
    min_var_portfolio(cov_mat, allow_short=False)
        Computes the minimum variance portfolio.
        
        Note: As the variance is not invariant with respect
        to leverage, it is not possible to construct non-trivial
        market neutral minimum variance portfolios. This is because
        the variance approaches zero with decreasing leverage,
        i.e. the market neutral portfolio with minimum variance
        is not invested at all.
        
        Parameters
        ----------
        cov_mat: pandas.DataFrame
            Covariance matrix of asset returns.
        allow_short: bool, optional
            If 'False' construct a long-only portfolio.
            If 'True' allow shorting, i.e. negative weights.
        
        Returns
        -------
        weights: pandas.Series
            Optimal asset weights.
    
    tangency_portfolio(cov_mat, exp_rets, allow_short=False)
        Computes a tangency portfolio, i.e. a maximum Sharpe ratio portfolio.
        
        Note: As the Sharpe ratio is not invariant with respect
        to leverage, it is not possible to construct non-trivial
        market neutral tangency portfolios. This is because for
        a positive initial Sharpe ratio the sharpe grows unbound
        with increasing leverage.
        
        Parameters
        ----------
        cov_mat: pandas.DataFrame
            Covariance matrix of asset returns.
        exp_rets: pandas.Series
            Expected asset returns (often historical returns).
        allow_short: bool, optional
            If 'False' construct a long-only portfolio.
            If 'True' allow shorting, i.e. negative weights.
        
        Returns
        -------
        weights: pandas.Series
            Optimal asset weights.
    
    truncate_weights(weights, min_weight=0.01, rescale=True)
        Truncates small weight vectors, i.e. sets weights below a treshold to zero.
        This can be helpful to remove portfolio weights, which are negligibly small.
        
        Parameters
        ----------
        weights: pandas.Series
            Optimal asset weights.
        min_weight: float, optional
            All weights, for which the absolute value is smaller
            than this parameter will be set to zero.
        rescale: boolean, optional
            If 'True', rescale weights so that weights.sum() == 1.
            If 'False', do not rescale.
        
        Returns
        -------
        adj_weights: pandas.Series
            Adjusted weights.

DATA
    __all__ = ['markowitz_portfolio', 'min_var_portfolio', 'tangency_portf...