Added explanation for heatmap and basic portfolio return (#43)

- Added explanation for heatmap and basic portfolio return (bf04b3da159beb901af262ef20a622e8465e8a46)


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

app.py

@@ -31,14 +31,7 @@ from sharp_ratio import(
     sharp_ratio_func
 )
 
-def risk_str(num):
-    if num >=5 and num <15:
-        return 'Low Risk Aversion'
-    elif num >= 15 and num <25:
-        return 'Medium Risk Aversion'
-    elif num >= 25 and num <=35:
-        return 'High Rish Aversion'
-#### Koi
+
         
 def load_heading():
     """The function that displays the heading.
@@ -59,7 +52,7 @@ def get_choices():
     choices = {}
     #tab1, tab2, tab3, tab4, tab5 = st.tabs(["Tickers", "Quantity", "Benchmark","Risk Free Return","Risk Aversion"])
 
-    tickers = st.sidebar.text_input('Enter stock tickers.', 'GOOG,A,AVOG,AMD')
+    tickers = st.sidebar.text_input('Enter stock tickers.', 'GOOG,AA,AVOG,AMD')
 
     # Set the weights
     weights_str = st.sidebar.text_input('Enter the investment quantities', '50,30,25,25')
@@ -210,7 +203,59 @@ return is calculated. The model results can be found in Appendix A.
 
         """
         ##### ##### Koi
+        # Creates the title for streamlit
+        st.subheader('Portfolio Historical Normalized Cumulative Returns')
+        """
+Cumulative Returns:\n 
+The cumulative return of an asset is calculated by subtracting the original price paid from the current profit or loss. This answers the question, 
+what is the return on my initial investment?\n 
+The graph below shows the historical normalized cumulative returns for each of the chosen assets for the entire time period of the available data. 
+The default line chart shows tickers AA, AMD, AVGO, and GOOG and we can see that all have a positive cumulative return over the period of the available data. 
+Any of these assets purchased on the starting day and sold on the ending day for the period would have earned a return on their investment.\n
+This chart can also be used to analyze the correlation of the returns of the chosen assets over the displayed period. 
+Any segments of the line charts that show cumulative returns with similarly or oppositely angled segments can be considered to have some level of 
+correlation during those periods. 
+        """
         basic_portfolio(choices['combined_df'])
+        """
+Negative Correlations (1): \n
+Occur for assets whose cumulative returns move in opposite directions. When one goes up the other goes down and vice versa. 
+These negatively correlated assets would offer some level of diversification protection to each other. 
+Perfectly negatively correlated stocks are sort of the goal, but unlikely to be common. 
+In most cases finding some level of negatively correlated stocks, should offer some level of diversification protection to your portfolio. 
+The amount of protection depends upon the calculated metric. Our tool includes some CAPM analysis, which attempts to relate the risk and return 
+and the correlation of assets to determine the expected portfolio returns versus the combined, hopefully reduced, risk.\n
+
+Positive Correlations (2):\n 
+Occur for assets whose cumulative returns move in concert. When one goes up the other also goes up and vice versa. 
+These positively correlated assets would not offer much or any diversification protection to each other.\n 
+        """
+        im = Image.open('1vs2.png')
+        col1, col2, col3 = st.columns([1,6,1])
+
+        with col1:
+            st.write("")
+    
+        with col2:
+            st.image(im, caption='Trends of Assets Correlations',use_column_width='auto')
+    
+        with col3:
+            st.write("")
+            
+            # Creates the title for streamlit
+        st.subheader('Heatmap Showing Correlation Of Assets')
+        """
+Heatmap: \n
+The Heat map shows the overall correlation of each asset to the other assets. Notice that the middle diagonal row is filled in with all 1’s. 
+That is because they are all perfectly correlated with themselves. A value of 1 equates to perfect correlation, -1 equates to perfect negative correlation, 
+and 0 equates to no correlation with values in between being relative to their distance from the extremes. A correlation value of .5 would mean 
+the asset moves half as much in the same direction as the correlated asset.  A values of -0.5 would mean it moves half as much in the opposite direction 
+as the correlated asset. \n
+The Heat map shows the correlation coefficient or value for each asset over the entire period to each other asset. 
+It also depicts the color of the intersection as darker for less correlation and lighter for more correlation, which could be either positive or negative. 
+The legend on the right indicates the absolute level of correlation for each color, again positive or negative associated to each color.\n 
+        """
+        
         display_heat_map(choices['data'],choices['choices'])
         #display_portfolio_return(choices['combined_df'], choices['choices'])
         preprocess(choices['raw_data'], choices['choices'])