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()
