Added benchmark component and risk assignment and color to bubble visualization (#29)

- Added benchmark component and risk assignment and color to bubble visualization (b3daf186ec45d202d0982bc46938812f72735ab6)


Co-authored-by: Warren Borders <wborders@users.noreply.huggingface.co>

plots.py

@@ -8,9 +8,9 @@ import plotly.express as px
 
 
 def beta(stock_df, choices):
-    symbols, weights, investment, rf, A_coef  = choices.values()
+    symbols, weights, benchmark, investing_style, rf, A_coef  = choices.values()
     tickers = symbols
-    tickers.append('sp500')
+    tickers.append(benchmark)
     #print(tickers)
     quantity = weights
     selected_stocks = stock_df[tickers]
@@ -32,15 +32,15 @@ def beta(stock_df, choices):
     alpha_list = []
     stocks_daily_return = df_stocks
     for i in stocks_daily_return.columns:
-        if i != 'Date' and i != 'sp500':
+        if i != 'Date' and i != benchmark:
             # stocks_daily_return.plot(kind = 'scatter', x = 'A', y = i)
-            b, a = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return[i], 1)
+            b, a = np.polyfit(stocks_daily_return[benchmark], stocks_daily_return[i], 1)
             # plt.plot(stocks_daily_return['sp500'], b * stocks_daily_return['sp500'] + a, '-', color = 'r')
             beta_list.append(round(b, 2))
             alpha_list.append(round(a, 2))
             # plt.show()
     # Formats the results
-    symbols.remove('sp500')
+    symbols.remove(benchmark)
     beta = {'Assets': symbols, 'Beta': beta_list}
     alpha = {'Assets': symbols, 'Alpha': alpha_list}
     # Creates a header for streamlit
@@ -54,10 +54,10 @@ def beta(stock_df, choices):
 
 
 def ER(stock_df, choices):
-    symbols, weights, investment, rf, A_coef  = choices.values()
+    symbols, weights, investing_style, benchmark, rf, A_coef  = choices.values()
     symbols_ =symbols.copy()
     tickers = symbols
-    tickers.append('sp500')
+    tickers.append(benchmark)
     #print(tickers)
     quantity = weights
     selected_stocks = stock_df[tickers]
@@ -78,9 +78,9 @@ def ER(stock_df, choices):
     # print(df_stocks)
 
     for i in stocks_daily_return.columns:
-        if i != 'Date' and i != 'sp500':
+        if i != 'Date' and i != benchmark:
             # stocks_daily_return.plot(kind = 'scatter', x = 'A', y = i)
-            b, a = np.polyfit(stocks_daily_return['sp500'], stocks_daily_return[i], 1)
+            b, a = np.polyfit(stocks_daily_return[benchmark], stocks_daily_return[i], 1)
             # plt.plot(stocks_daily_return['sp500'], b * stocks_daily_return['sp500'] + a, '-', color = 'r')
             beta[i] = round(b, 2)
             alpha[i] = round(a, 2)
@@ -92,14 +92,14 @@ def ER(stock_df, choices):
     # rf = 0 assuming risk-free rate of 0
     rf = 0
     # rm - annualize retun
-    rm = stocks_daily_return['sp500'].mean() * 252
+    rm = stocks_daily_return[benchmark].mean() * 252
     for i in keys:
         ER_.append( round(rf + (beta[i] * (rm - rf)), 2))
 
     #for i in keys:
     #    print('Expected Return based on CAPM for {} is {}%'.format(i, ER_[i]))
     #print(ER)
-    symbols.remove('sp500')
+    symbols.remove(benchmark)
     #st.subheader('Expected Annual Return Based on CAPM Model')
 
     Expected_return = {'Assets': symbols_, 'Expected Annual Return': ER_}
@@ -187,24 +187,32 @@ def display_heat_map(stock_df,choices):
 #    st.subheader('Portfolio Historical Cumulative Returns Based On Inputs!')
 #    st.line_chart(cumulative_profit)
 def buble_interactive(stock_df,choices):
-    symbols, weights, investment, rf, A_coef  = choices.values()
+    symbols, weights, investing_style,benchmark, rf, A_coef  = choices.values()
     beta,cash_value_weights  = ER(stock_df,choices)
     my_list = []
+    my_colors = []
     for i in beta.values():
         my_list.append(i)
+        if i < 0.3:
+            my_colors.append("Conservative")
+        if i >= 0.3 and i <= 1.1:
+            my_colors.append("Moderate Risk")
+        if i > 1.1:
+            my_colors.append("Risky")
     
     df_final =pd.DataFrame()
     df_final['ticker'] = symbols
     df_final['quantities'] = weights
     df_final['cash_value']  =cash_value_weights
     df_final['Beta'] = my_list
+    df_final['Risk'] = my_colors
    
     fig = px.scatter(
     df_final,
     x="quantities",
     y="Beta",
     size="cash_value",
-    #color="continent",
+    color="Risk",
     hover_name="ticker",
     log_x=True,
     size_max=60,