portfolio_toolkit.optimization package

Submodules

• portfolio_toolkit.optimization.compute_var module
  • compute_var()
• portfolio_toolkit.optimization.efficient_frontier module
  • compute_efficient_frontier()
  • calculate_portfolio_metrics()
  • find_maximum_sharpe_portfolio()
• portfolio_toolkit.optimization.optimization module
  • Optimization
    • Optimization.name
    • Optimization.currency
    • Optimization.assets
    • Optimization.data_provider
    • Optimization.period
    • Optimization.returns
    • Optimization.covariance_matrix
    • Optimization.means
    • Optimization.weights
    • Optimization.expected_returns
    • Optimization.from_dict()
    • Optimization.get_var()
    • Optimization.get_efficient_frontier()
    • Optimization.__init__()
• portfolio_toolkit.optimization.optimization_from_dict module
  • create_optimization_from_json()

Module contents

class portfolio_toolkit.optimization.Optimization(name: str, currency: str, assets: List[OptimizationAsset], data_provider: DataProvider, period: str = '1y', returns: DataFrame | None = None, covariance_matrix: DataFrame | None = None, means: Series | None = None, weights: Series | None = None, expected_returns: Series | None = None)

Bases: object

Class to represent and manage an asset optimization.

__init__(name: str, currency: str, assets: List[OptimizationAsset], data_provider: DataProvider, period: str = '1y', returns: DataFrame | None = None, covariance_matrix: DataFrame | None = None, means: Series | None = None, weights: Series | None = None, expected_returns: Series | None = None) → None

covariance_matrix: DataFrame | None = None

expected_returns: Series | None = None

classmethod from_dict(data: dict, data_provider: DataProvider) → Optimization

get_efficient_frontier(num_points: int)

get_var() → float

means: Series | None = None

period: str = '1y'

returns: DataFrame | None = None

weights: Series | None = None

name: str

currency: str

assets: List[OptimizationAsset]

data_provider: DataProvider

portfolio_toolkit.optimization.find_maximum_sharpe_portfolio(expected_returns: Series, covariance_matrix: DataFrame, risk_free_rate: float = 0.0) → Dict[str, ndarray | float]

Find the portfolio with maximum Sharpe ratio (tangency portfolio).

Parameters:

expected_returnspd.Series

Expected returns for each asset

covariance_matrixpd.DataFrame

Covariance matrix of asset returns

risk_free_ratefloat, default=0.0

Risk-free rate

Returns:

Dict[str, Union[np.ndarray, float]]

Dictionary containing: - ‘weights’: Optimal weights - ‘return’: Expected portfolio return - ‘volatility’: Portfolio volatility - ‘sharpe_ratio’: Sharpe ratio

portfolio_toolkit.optimization.calculate_portfolio_metrics(weights: List[float] | ndarray, expected_returns: Series, covariance_matrix: DataFrame, risk_free_rate: float = 0.0) → Dict[str, float]

Calculate key portfolio metrics for given weights.

Parameters:

weightsList[float] or np.ndarray

Portfolio weights

expected_returnspd.Series

Expected returns for each asset

covariance_matrixpd.DataFrame

Covariance matrix of asset returns

risk_free_ratefloat, default=0.0

Risk-free rate for Sharpe ratio calculation

Returns:

Dict[str, float]

Dictionary containing portfolio metrics: - ‘return’: Expected portfolio return - ‘volatility’: Portfolio volatility (standard deviation) - ‘sharpe_ratio’: Sharpe ratio