update plot.py (#10)

- update plot.py (b53e87a56aa9dd6875c948bb70e2f13047ec2060)


Co-authored-by: handepehlivan <handepeh@users.noreply.huggingface.co>

plots.py

@@ -53,7 +53,7 @@ def beta(stock_df, choices):
 
 def ER(stock_df, choices):
     symbols, weights, investment = choices.values()
-    symbols_ =symbols
+    symbols_ =symbols.copy()
     tickers = symbols
     tickers.append('sp500')
     #print(tickers)
@@ -98,11 +98,11 @@ def ER(stock_df, choices):
     #    print('Expected Return based on CAPM for {} is {}%'.format(i, ER_[i]))
     #print(ER)
     symbols.remove('sp500')
-    st.subheader('Expected Annual Return Based on CAPM Model')
+    #st.subheader('Expected Annual Return Based on CAPM Model')
 
-    Expected_return = {'Assets': symbols, 'Expected Annual Return': ER_}
+    Expected_return = {'Assets': symbols_, 'Expected Annual Return': ER_}
     # Creates a header for streamlit
-    st.dataframe(Expected_return)
+    #st.dataframe(Expected_return)
 
     
     # calculate expected return for the portfolio
@@ -123,13 +123,20 @@ def ER(stock_df, choices):
     portfolio_weights = (portfolio_weights / total_portfolio_value)*100
     ER_portfolio= []
     ER_portfolio = sum(list(ER_) * portfolio_weights)/100
-    print('aaa',ER_portfolio)
+    #print(ER_portfolio)
 
-    st.subheader('Expected Portfolio Return Based on CAPM Model')
+    #st.subheader('Expected Portfolio Return Based on CAPM Model')
     # Creates a header for streamlit
-    st.write('Expected Portfolio Return is:', ER_portfolio)
-    return beta,cash_value_stocks
-
+    #st.write('Expected Portfolio Return is:', ER_portfolio)
+    Bar_output = Expected_return.copy()
+    Bar_output['Assets'].append('Portfolio')
+    Bar_output['Expected Annual Return'].append(ER_portfolio)
+    fig = px.bar(Bar_output, x='Assets', y="Expected Annual Return",color='Assets') 
+    fig.update_layout(title_text = 'Annual Expected Return of the Assets and Portfolio',
+                      title_x=0.458)
+    st.plotly_chart(fig, use_container_width=True)
+    
+    return beta, cash_value_stocks
 
 def basic_portfolio(stock_df):
     """Uses the stock dataframe to graph the normalized historical cumulative returns of each asset.
@@ -145,24 +152,23 @@ def basic_portfolio(stock_df):
     st.line_chart(cumulative_return)
 
 
-def display_heat_map(stock_df):
-    """Uses the stock dataframe to calculate the correlations between the different assets and display them as a heatmap.
-    """
+def display_heat_map(stock_df,choices):
+    symbols, weights, investment = choices.values()
+    selected_stocks = stock_df[symbols]
     # Calcuilates the correlation of the assets in the portfolio
-    price_correlation = stock_df.corr()
+    price_correlation = selected_stocks.corr()
 
     # Creates the title for streamlit
     st.subheader('Heatmap Showing Correlation Of Assets')
     # Generates a figure for the heatmap
     fig, ax = plt.subplots()
-    sns.heatmap(price_correlation, ax=ax)
+    fig = px.imshow(price_correlation,text_auto=True, aspect="auto")
     # Displays the heatmap on streamlit
     st.write(fig)
     # Creates a header for the correlation data
-    st.subheader('Correlation Data')
+    #st.subheader('Correlation Data')
     # Displays the correlation data on streamlit
-    st.dataframe(price_correlation)
-
+    #st.dataframe(price_correlation)
 
 #def display_portfolio_return(stock_df, choices):
     """Uses the stock dataframe and the chosen weights from choices to calculate and graph the historical cumulative portfolio return.