romanmichaelpaolucci/portfolio_optimization.py

## portfolio_optimization.py
class PortfolioOptimization:

    """
        Class for optimizing a historic portfolio
    """

    def __init__(self, table):
        mu = expected_returns.mean_historical_return(table)
        S = risk_models.sample_cov(table)

        # Optimise for maximal Sharpe ratio
        ef = EfficientFrontier(mu, S)
        ef.max_sharpe()  # Raw weights
        self.cleaned_weights = ef.clean_weights()
        print(self.cleaned_weights)
        ef.portfolio_performance(verbose=True)

        latest_prices = discrete_allocation.get_latest_prices(table)
        self.allocation, self.leftover = discrete_allocation.portfolio(
            self.cleaned_weights, latest_prices, total_portfolio_value=10000  # This value can be adjusted to your actual portfolio value
        )

    def report_discrete_allocation(self):
        print(self.allocation)
        print("Funds remaining: ${:.2f}".format(self.leftover))

    def get_cleaned_weights(self):
        return self.cleaned_weights
	class PortfolioOptimization:

	"""
	Class for optimizing a historic portfolio
	"""

	def __init__(self, table):
	mu = expected_returns.mean_historical_return(table)
	S = risk_models.sample_cov(table)

	# Optimise for maximal Sharpe ratio
	ef = EfficientFrontier(mu, S)
	ef.max_sharpe() # Raw weights
	self.cleaned_weights = ef.clean_weights()
	print(self.cleaned_weights)
	ef.portfolio_performance(verbose=True)

	latest_prices = discrete_allocation.get_latest_prices(table)
	self.allocation, self.leftover = discrete_allocation.portfolio(
	self.cleaned_weights, latest_prices, total_portfolio_value=10000 # This value can be adjusted to your actual portfolio value
	)

	def report_discrete_allocation(self):
	print(self.allocation)
	print("Funds remaining: ${:.2f}".format(self.leftover))

	def get_cleaned_weights(self):
	return self.cleaned_weights