Update pages/portfolio_tracker.py

pages/portfolio_tracker.py

@@ -92,57 +92,56 @@ with st.form(key="selecting columns"):
     symbols = st.multiselect(label='Select Tickers:', options=list_of_tickers, placeholder='MSFT, AAPL, ...')
     submit_button = st.form_submit_button(label='Evaluate')
 
-    if submit_button:
-        st.write(symbols)
-
-# if submit_button and symbols:
-#     beta_dfs = []
-#     gains_data = {}
-#     hash_map = {}
-    
-#     for ticker in symbols:
-#       # make all the API calls and capture return json
-#       basic_info = get_industry(ticker) 
-#       metric_data, annual_series_data, quarterly_series_data  = get_company_metrics(ticker)
-      
-#       # reformat all JSON returns to be flattened dictionaries 
-#       roe_dict = {'roe': annual_series_data['roe'][0]['v']}
-#       totalDebtToTotalCapital_y_dict = {'totalDebtToTotalCapital_y' : annual_series_data['totalDebtToTotalCapital'][0]['v']}
-#       totalDebtToEquity_y_dict = {'totalDebtToEquity_y' :   annual_series_data['totalDebtToEquity'][0]['v']}
-#       eps_dict = {'eps' :   annual_series_data['eps'][0]['v']}
-#       totalDebtToTotalCapital_q_dict = {'totalDebtToTotalCapital_q' :   quarterly_series_data['totalDebtToTotalCapital'][0]['v']}
-#       totalDebtToEquity_q_dict = {'totalDebtToEquity_q' :   quarterly_series_data['totalDebtToEquity'][0]['v']}
-    
-#       # merge all dictionary keys per ticker
-#       combined_info = basic_info.copy()  # Make a copy of the basic info
-#       combined_info = combined_info | metric_data | roe_dict | totalDebtToTotalCapital_y_dict | totalDebtToEquity_q_dict |totalDebtToEquity_y_dict | eps_dict | totalDebtToTotalCapital_q_dict
-    
-#       hash_map[ticker] = combined_info
-    
-#       # beta calculations
-#       df_b = calc_beta(ticker)
-#       beta_dfs.append(df_b)
-      
-#       # equity gains
-#       _, div = get_equity_gains(ticker=ticker, period=1810)
-#       gains_data[ticker] = div
-    
-#     # Now, create a DataFrame from the hash_map
-#     df_1 = pd.DataFrame.from_dict(hash_map, orient='index')[['finnhubIndustry','eps','roe','dividendGrowthRate5Y','epsGrowth5Y','payoutRatioAnnual','payoutRatioTTM','roeTTM','totalDebtToEquity_y','totalDebtToEquity_q', 'totalDebtToTotalCapital_y','totalDebtToTotalCapital_q']]
-#     # Create beta df
-#     beta_df = pd.concat(beta_dfs)
-#     df_2 = pd.DataFrame(list(gains_data.items()), columns=['symbol', 'recent_dividend']).set_index('symbol')
-    
-#     df_apis = df_1.join(beta_df)
-#     df_final = df_apis.join(df_2)
-    
-#     # calculate additional columns 
-#     df_final['5Y_SP500_growth'], _ = get_equity_gains(ticker= '^GSPC', period=1810)
-    
-#     df_final['90_day_tbill'] = 4.06
-    
-#     df_final['CAPM'] = df_final['90_day_tbill']/100 + df_final['Beta']*(df_final['5Y_SP500_growth'] - df_final['90_day_tbill']/100)
-    
-#     df_final['DDM'] = (df_final['recent_dividend'] * (1+df_final['dividendGrowthRate5Y']/100)) / (df_final['CAPM'] - df_final['dividendGrowthRate5Y']/100)
+    if submit_button and symbols:
+        beta_dfs = []
+        gains_data = {}
+        hash_map = {}
+        
+        for ticker in symbols:
+            st.spinner(text=f"Loading {ticker} Data...", *, cache=False)
+          # make all the API calls and capture return json
+          basic_info = get_industry(ticker) 
+          metric_data, annual_series_data, quarterly_series_data  = get_company_metrics(ticker)
+          
+          # reformat all JSON returns to be flattened dictionaries 
+          roe_dict = {'roe': annual_series_data['roe'][0]['v']}
+          totalDebtToTotalCapital_y_dict = {'totalDebtToTotalCapital_y' : annual_series_data['totalDebtToTotalCapital'][0]['v']}
+          totalDebtToEquity_y_dict = {'totalDebtToEquity_y' :   annual_series_data['totalDebtToEquity'][0]['v']}
+          eps_dict = {'eps' :   annual_series_data['eps'][0]['v']}
+          totalDebtToTotalCapital_q_dict = {'totalDebtToTotalCapital_q' :   quarterly_series_data['totalDebtToTotalCapital'][0]['v']}
+          totalDebtToEquity_q_dict = {'totalDebtToEquity_q' :   quarterly_series_data['totalDebtToEquity'][0]['v']}
+        
+          # merge all dictionary keys per ticker
+          combined_info = basic_info.copy()  # Make a copy of the basic info
+          combined_info = combined_info | metric_data | roe_dict | totalDebtToTotalCapital_y_dict | totalDebtToEquity_q_dict |totalDebtToEquity_y_dict | eps_dict | totalDebtToTotalCapital_q_dict
+        
+          hash_map[ticker] = combined_info
+        
+          # beta calculations
+          df_b = calc_beta(ticker)
+          beta_dfs.append(df_b)
+          
+          # equity gains
+          _, div = get_equity_gains(ticker=ticker, period=1810)
+          gains_data[ticker] = div
+        
+        # Now, create a DataFrame from the hash_map
+        df_1 = pd.DataFrame.from_dict(hash_map, orient='index')[['finnhubIndustry','eps','roe','dividendGrowthRate5Y','epsGrowth5Y','payoutRatioAnnual','payoutRatioTTM','roeTTM','totalDebtToEquity_y','totalDebtToEquity_q', 'totalDebtToTotalCapital_y','totalDebtToTotalCapital_q']]
+        # Create beta df
+        beta_df = pd.concat(beta_dfs)
+        df_2 = pd.DataFrame(list(gains_data.items()), columns=['symbol', 'recent_dividend']).set_index('symbol')
+        
+        df_apis = df_1.join(beta_df)
+        df_final = df_apis.join(df_2)
+        
+        # calculate additional columns 
+        df_final['5Y_SP500_growth'], _ = get_equity_gains(ticker= '^GSPC', period=1810)
+        
+        df_final['90_day_tbill'] = 4.06
+        
+        df_final['CAPM'] = df_final['90_day_tbill']/100 + df_final['Beta']*(df_final['5Y_SP500_growth'] - df_final['90_day_tbill']/100)
+        
+        df_final['DDM'] = (df_final['recent_dividend'] * (1+df_final['dividendGrowthRate5Y']/100)) / (df_final['CAPM'] - df_final['dividendGrowthRate5Y']/100)
+        
+        st.write(df_final)
     
-#     st.write(df_final)