slope, intercept, r_value, p_value, std_err = stats.linregress(returns, m_returns) return slope def get_portfolio_treynor_measure(self): port_treynor_measure = float((ex_port_return - risk_free_rate))/beta return port_treynor_measure def get_portfolio_alpha_measure(self): port_alpha_measure = ex_port_return - (risk_free_rate + beta*(market_portfolio_expected_return - risk_free_rate)) return port_alpha_measure ## Refer to class Portfolio # create a portfolio consisting of several stocks portfolio = Portfolio(["GOOGL", "AAPL", "MSFT"], [0.3, 0.4, 0.3]) # let us add a new stock to portfolio with new weights: sum of weights should equal to 1 portfolio.add_stocks("^DJI", [0.2, 0.2, 0.4, 0.2]) # get a list of stocks within the portfolio stocks = portfolio.get_stocks() # get a list of weights of the stocks within the portfolio weights = portfolio.get_weights() ## Refer to class MarketDataProvider to get market data for stocks selected for the portfolio # define an instance of class MarketDataProvider() provider = MarketDataProvider() # retrieve stock prices for the period from yahoo stock_prices = provider.import_market_data(stocks, "1/1/2015", "7/1/2015") ## Refer to class StockPerformance stock_performance = StockPerformance()