Update app.py (#12)

- Update app.py (e65d55058bb796229f7e4a6c93055af20d674b2f)


Co-authored-by: Vi Koi Nguyen <Yim-Koi@users.noreply.huggingface.co>

app.py

@@ -120,11 +120,22 @@ With this initial analysis, beta was calculated to determine the stock’s risk
 price changes to the benchmark. By using CAPM model, annual expected return and portfolio
 return is calculated. The model results can be found in Appendix A.
         """
+        
+        beta(choices['data'], choices['choices'])
+        ER(choices['data'], choices['choices'])
         ##### EDIT HERE ##### koi
+        st.header('CAPM Model and the Efficient Frontier')
+        """
+    CAPM model measures systematic risks, however many of it's functions has unrealistic assumptions and relies heavily on a linear interpretation 
+    of the risks vs. returns relationship. It is better to use CAPM model in conjunction with the Efficient Frontier to better
+    graphically depict volatility (a measure of investment risk) for the defined rate of return. \n
+    Below we map the linear Utility function from the CAPM economic model along with the Efficient Frontier
+    Each circle depicted above is a variation of the portfolio with the same input assest, only different weights. 
+    Portfolios with higher volatilities has a yellower shade of hue, while portfolios with a higher return has a bigger radius. \n
+    As you input different porfolio assets, take note of how diversification can improve a portfolio's risk versus reward profile.
+        """
         ef_viz(choices['data'],choices['choices'])
         ##### #####
-        beta(choices['data'], choices['choices'])
-        ER(choices['data'], choices['choices'])
         basic_portfolio(choices['combined_df'])
         display_heat_map(choices['data'],choices['choices'])
         #display_portfolio_return(choices['combined_df'], choices['choices'])