add stats to HRP

pages/2_Portfolio_Builder.py

@@ -250,3 +250,45 @@ with st.form(key="selecting columns"):
         )
         st.pyplot(fig3)
 
+        # ----------------------------  (Portfolio Statistics) -------------------------------- #
+
+        spy_prices = obb.equity.price.historical(symbol = "spy", provider="yfinance", start_date=start_date, end_date=end_date).to_df()
+
+        # Calculate daily returns
+        # Ensure you're using the adjusted close prices for accurate return calculation
+        benchmark_returns = spy_prices['close'].pct_change().dropna()
+
+        port.rf = 0.0406  # Risk-free rate
+        portfolio_return = np.dot(w, mu)
+
+        # market return
+        spy_daily_return = benchmark_returns
+        spy_expected_return = spy_daily_return.mean()
+
+        # portfolio's beta
+        covariance = returns.apply(lambda x: x.cov(spy_daily_return))
+        spy_variance = spy_daily_return.var()
+        beta_values = covariance / spy_variance
+        portfolio_beta = np.dot(w['weights'], beta_values)
+        st.write('Portfolio Beta: ', np.round(portfolio_beta,3))
+
+        # jensens alpha
+        expected_return = port.rf + portfolio_beta * (spy_daily_return - port.rf)
+        st.write('Jensen\'s Alpha: ', np.round(expected_return.iloc[-1],3))
+
+        # treynor ratio
+        treynor_ratio = (expected_return - port.rf) / portfolio_beta
+        st.write('Treynor Ratio: ', np.round(treynor_ratio.iloc[-1],3))
+
+        # Portfolio volatility
+        portfolio_stddev = np.sqrt(np.dot(pd.Series(w['weights']).T, np.dot(covariance, w['weights'])))
+
+        # Sharpe Ratio, adjusted for the risk-free rate
+        sharpe_ratio = rp.RiskFunctions.Sharpe(
+            mu=mu,
+            cov=cov,
+            returns=returns,
+            rf=port.rf,
+            w=w,
+        )
+        st.write('Sharpe Ratio: ', np.round(sharpe_ratio, 3))