Portfolio Tools

The portfoliotools module provides functions for portfolio optimization, simulation, and analysis.

Portfolio Optimization

openseries.portfoliotools.efficient_frontier(eframe, num_ports=5000, seed=71, bounds=None, frontier_points=200, minimize_method='SLSQP', *, tweak=True)[source]

Identify an efficient frontier.

Parameters:

  • eframe (OpenFrame) – Portfolio data.

  • num_ports (int) – Number of possible portfolios to simulate. Defaults to 5000.

  • seed (int) – The seed for the random process. Defaults to 71.

  • bounds (tuple[tuple[float, float], ...] | None) – The range of minimum and maximum allowed allocations for each asset.

  • frontier_points (int) – Number of points along frontier to optimize. Defaults to 200.

  • minimize_method (LiteralMinimizeMethods) – The method passed into the scipy.minimize function. Defaults to SLSQP.

  • tweak (bool) – Cutting the frontier to exclude multiple points with almost the same risk. Defaults to True.

Returns:

The efficient frontier data, simulation data and optimal portfolio.

Return type:

tuple[DataFrame, DataFrame, NDArray[float64]]

Portfolio Simulation

openseries.portfoliotools.simulate_portfolios(simframe, num_ports, seed)[source]

Generate random weights for simulated portfolios.

Parameters:

  • simframe (OpenFrame) – Return data for portfolio constituents.

  • num_ports (int) – Number of possible portfolios to simulate.

  • seed (int) – The seed for the random process.

Returns:

The resulting data.

Return type:

DataFrame

Portfolio Constraints

openseries.portfoliotools.constrain_optimized_portfolios(data, serie, portfolioname='Current Portfolio', simulations=10000, curve_points=200, bounds=None, minimize_method='SLSQP')[source]

Constrain optimized portfolios to those that improve on the current one.

Parameters:

  • data (OpenFrame) – Portfolio data.

  • serie (OpenTimeSeries) – A timeseries representing the current portfolio.

  • portfolioname (str) – Name of the portfolio. Defaults to “Current Portfolio”.

  • simulations (int) – Number of possible portfolios to simulate. Defaults to 10000.

  • curve_points (int) – Number of optimal portfolios on the efficient frontier. Defaults to 200.

  • bounds (tuple[tuple[float, float], ...] | None) – The range of minimum and maximum allowed allocations for each asset.

  • minimize_method (LiteralMinimizeMethods) – The method passed into the scipy.minimize function. Defaults to SLSQP.

Returns:

The constrained optimal portfolio data.

Return type:

tuple[OpenFrame, OpenTimeSeries, OpenFrame, OpenTimeSeries]

Visualization

openseries.portfoliotools.prepare_plot_data(assets, current, optimized)[source]

Prepare data to be used as point_frame in the sharpeplot function.

Parameters:

  • assets (OpenFrame) – Portfolio data with individual assets and a weighted portfolio.

  • current (OpenTimeSeries) – The current or initial portfolio based on given weights.

  • optimized (NDArray[float64]) – Data optimized with the efficient_frontier method.

Returns:

The data prepared with mean returns, volatility and weights.

Return type:

DataFrame

openseries.portfoliotools.sharpeplot(sim_frame=None, line_frame=None, point_frame=None, point_frame_mode='markers', filename=None, directory=None, titletext=None, output_type='file', include_plotlyjs='cdn', *, title=True, add_logo=True, auto_open=True)[source]

Create scatter plot coloured by Sharpe Ratio.

Parameters:

  • sim_frame (DataFrame | None) – Data from the simulate_portfolios method.

  • line_frame (DataFrame | None) – Data from the efficient_frontier method.

  • point_frame (DataFrame | None) – Data to highlight current and efficient portfolios.

  • point_frame_mode (LiteralLinePlotMode) – Which type of scatter to use. Defaults to markers.

  • filename (str | None) – Name of the Plotly html file.

  • directory (DirectoryPath | None) – Directory where Plotly html file is saved.

  • titletext (str | None) – Text for the plot title.

  • output_type (LiteralPlotlyOutput) – Determines output type. Defaults to “file”.

  • include_plotlyjs (LiteralPlotlyJSlib) – Determines how the plotly.js library is included in the output. Defaults to “cdn”.

  • title (bool) – Whether to add standard plot title. Defaults to True.

  • add_logo (bool) – Whether to add Captor logo. Defaults to True.

  • auto_open (bool) – Determines whether to open a browser window with the plot. Defaults to True.

Returns:

The scatter plot with simulated and optimized results.

Return type:

tuple[Figure, str]