Update ef.py

ef.py

@@ -6,13 +6,26 @@ import streamlit as st
 import plotly.graph_objects as go
 import plotly.express as px
 
+
+### START AND RUN STREAMLIT
+#https://docs.streamlit.io/library/get-started/installation
+
 def ef_viz(stock_df,choices):
-    st.write("EF Visualization KOI EDITS")
+    #st.write("EF Visualization KOI EDITS")
+    # st.header('CAPM Model and the Efficient Frontier')
+    # """
+    # CAPM model measure systematic risks, however it 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. <br>
+    # 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. <br>
+    # As you input different porfolio assets, take note of how diversification can improve a portfolio's risk versus reward profile.
+    # """
     symbols, weights, investment = choices.values()
     tickers = symbols
     
     #tickers.append('sp500')
-    st.write(tickers)
+    #st.write(tickers)
     #st.write(stock_df)
     
     # Yearly returns for individual companies
@@ -30,12 +43,12 @@ def ef_viz(stock_df,choices):
     assets = pd.concat([ind_er, ann_sd], axis=1) # Creating a table for visualising returns and volatility of assets
     assets.columns = ['Returns', 'Volatility']
     assets
-    st.write(assets)
+    #st.write(assets)
     ln_pct_change = stock_df[tickers].pct_change().apply(lambda x: np.log(1+x))[1:]
     #Cov Matrix
     cov_matrix =ln_pct_change.cov()
 
-
+    ## CREATE PORFOLIOS WEIGHTS 
     p_ret = [] # Define an empty array for portfolio returns
     p_vol = [] # Define an empty array for portfolio volatility
     p_weights = [] # Define an empty array for asset weights
@@ -62,11 +75,31 @@ def ef_viz(stock_df,choices):
         data[symbol] = [w[counter] for w in p_weights]
         
     port_ef_df  = pd.DataFrame(data)
-    st.write(port_ef_df[tickers].T)
+    #st.write(port_ef_df[tickers].T)
+    ## NEEDS INPUT INSTEAD OF HARD CODE
+    a = 5 #the coefficient of risk aversion is A. If an invest is less risk averse A is small. We assume 25 < A < 35. 
     rf = 0.041
     min_vol_port = port_ef_df.iloc[port_ef_df['Volatility'].idxmin()]
     optimal_risky_port = port_ef_df.iloc[((port_ef_df['Returns']-rf)/port_ef_df['Volatility']).idxmax()]
     
+
+    ## CREATE CAPM LINE #https://www.youtube.com/watch?v=JWx2wcrSGkk
+    cal_x = []
+    cal_y = []
+    utl = []
+    
+
+
+    for er in np.linspace(rf, max(data['Returns']),20):
+        sd = (er - rf)/ ((optimal_risky_port[0] - rf)/ optimal_risky_port[1])
+        u = er - 0.5*a*(sd**2)
+        cal_x.append(sd)
+        cal_y.append(er)
+        utl.append(u)
+    
+    data2 = {'Utility':utl, 'cal_x':cal_x, 'cal_y':cal_y}
+
+    utl_df  = pd.DataFrame(data2)
     
     ## Create Figure
 
@@ -84,6 +117,13 @@ def ef_viz(stock_df,choices):
         
                         ])
                         )
+
+    fig1 = px.line(utl_df, x="cal_x", y="cal_y")
+    fig1.update_traces(line=dict(color = 'rgba(11,156,49,1)'))
 #https://stackoverflow.com/questions/59057881/python-plotly-how-to-customize-hover-template-on-with-what-information-to-show
-    st.plotly_chart(fig, use_container_width=True)  
+#https://stackoverflow.com/questions/65124833/plotly-how-to-combine-scatter-and-line-plots-using-plotly-express
+
+    fig3 = go.Figure(data=fig.data + fig1.data)
+
+    st.plotly_chart(fig3, use_container_width=True)  
         #st.write(stock_df)