Markowitz

Markowitz perform optimization to the weights of a known portfolio through Modern Portfolio Theory (MPT). Added to the traditional MPT are Machine Learning models to predict stock returns and sentiment analysis adjust return expectations based on media outlook. Markowitz relies on ReturnPred, Sentiment, and Cache modules on the backend. None of the listed modules are user-facing.

__init__ Arguments

• portfolio -> Portfolio: A Portfolio object containing stock symbols.
• market -> IndexSymbol: Market index symbol.
• horizon -> Days: Investment horizon in days.
• lookback -> Days: Historical data lookback period in days.
• rf_rate_pa -> Optional[float]: Risk-free rate of return per annum. (10 Year US Treasury yield if None)
• api_key_path -> Optional[str | os.PathLike]: Path to the .env file containing a NewsAPI key.
• api_key_var -> Optional[str]: Name of the environment variable containing the NewsAPI key.

Attributes

• portfolio -> Portfolio: A Portfolio object containing stock symbols.
• names -> dict[StockSymbol, str]: Short names of the companies issuing the stocks.
• market -> yfinance.Ticker: Market index.
• market_name -> str: Name of the market index. (often times the symbol)
• rf -> float: Risk-free rate of return for the investment period. (not per annum)
• raw_close -> pandas.DataFrame: Historical closing prices of the stocks.
• periodic_return -> pandas.DataFrame: Periodic returns of the stocks.
• expected_returns -> pandas.DataFrame: Expected returns of the stocks.
• volatility -> pandas.Series: Volatility of the stocks.
• market_returns -> pandas.Series: Market returns.
• market_volatility -> float: Market volatility.
• rm -> float: Market expected return. (historical average)
• cov_matrix -> pandas.DataFrame: Covariance matrix of the stocks.
• beta -> list[float]: Beta values of the stocks.

Methods

optimize_return

Optimize the portfolio for a target return.

Parameters:

• target_return: float: Target return for the portfolio.
• additional_constraints: Optioonal[list]: Additional constraints formatted to be passed to scipy.optimize.minimize.
• bounds: tuple[float, float]: Tuple containing the lower and upper bounds for the weights.
• include_beta: bool: Include beta values in the optimization.
• record: bool: Record the optimization results in the Portfolio object.

Returns:

• tuple[dict, scipy.optimize.OptimizeResult]: A tuple containing the optimized weights and the optimization result.

optimize_volatility

Optimize the portfolio for a target volatility.

Parameters:

• target_volatility: float: Target volatility for the portfolio.
• additional_constraints: Optioonal[list]: Additional constraints formatted to be passed to scipy.optimize.minimize.
• bounds: tuple[float, float]: Tuple containing the lower and upper bounds for the weights.
• include_beta: bool: Include beta values in the optimization.
• record: bool: Record the optimization results in the Portfolio object.

Returns:

• tuple[dict, scipy.optimize.OptimizeResult]: A tuple containing the optimized weights and the optimization result.

efficient_frontier

Plot the efficient frontier.

Parameters: - target_input: Literal['return', 'volatility']: The target input for the efficient frontier. - n: int: Number of points to plot on the efficient frontier. - save: bool: Save the plot as a .png file.

Returns: - None