def loadReturns(portfolio_returns: pd.Series,
index_returns: pd.Series) -> tuple:
assert "Return" in portfolio_returns.columns,\
"Dataframe should have a column named: Return"
assert "Close" in index_returns.columns,\
"Dataframe should have a column named: Close"
# Calculate key metrics
return_10y, return_10y_i = "-", "-"
return_5y, return_5y_i = "-", "-"
return_1y, return_1y_i = "-", "-"
if portfolio_returns.shape[0] > 0: # if stocks in portfolio
# Get start values
start_investment = portfolio_returns.Return[0]
index_start = index_returns['Close'][0]
ratio = start_investment / index_start
# Modify index returns to match initial investment as portfolio value 10 years ago
index_returns.Close = index_returns['Close'] * ratio
# Get the number of days
days = portfolio_returns.shape[0]
days_i = index_returns.shape[0]
# Calculate key numbers
now = portfolio_returns.Return[days - 1]
ago_5y = portfolio_returns.Return[days - 5 * 365 - 1]
ago_1y = portfolio_returns.Return[days - 366]
index_5y = index_returns['Close'][days_i - 5 * 365 - 1]
index_1y = index_returns['Close'][days_i - 365]
index_now = index_returns['Close'][days_i - 1]
return_10y = round((now - start_investment) * 100 / start_investment,
2)
return_5y = round((now - ago_5y) * 100 / ago_5y, 2)
return_1y = round((now - ago_1y) * 100 / ago_1y, 2)
return_10y_i = round(
(index_now - start_investment) * 100 / start_investment, 2)
return_5y_i = round((index_now - index_5y) * 100 / index_5y, 2)
return_1y_i = round((index_now - index_1y) * 100 / index_1y, 2)
return return_1y, return_5y, return_10y, return_1y_i, return_5y_i, return_10y_i
