updated growth strat

pages/1_Fundamentals.py

@@ -181,10 +181,9 @@ with st.form(key="selecting columns"):
           metric_data, annual_series_data, quarterly_series_data  = get_company_metrics(ticker)
 
           # reformat all JSON returns to be flattened dictionaries 
-          ebitPerShare_dict = {'ebitPerShare': annual_series_data['ebitPerShare'][0]['v'] if 'ebitPerShare' in annual_series_data else 0}
+          roe_dict = {'roe': annual_series_data['roe'][0]['v'] if 'roe' in annual_series_data else 0}
           ev_dict = {'ev' :annual_series_data['ev'][0]['v'] if 'ev' in annual_series_data else 0}
-          salesPerShare_dict = {'salesPerShare': annual_series_data['salesPerShare'][0]['v'] if 'salesPerShare' in annual_series_data else 0}
-          operatingMargin_dict = {'operatingMargin': annual_series_data['operatingMargin'][0]['v'] if 'operatingMargin' in annual_series_data else 0}
+          salesPerShare_dict = {'salesPerShare': quarterly_series_data['salesPerShare'][0]['v'] if 'salesPerShare' in quarterly_series_data else 0}
           eps_dict = {'eps' :annual_series_data['eps'][0]['v'] if 'eps' in annual_series_data else 0}
           pe_dict = {'pe': annual_series_data['pe'][0]['v'] if 'pe' in annual_series_data else 0}
           ps_dict = {'ps': annual_series_data['ps'][0]['v'] if 0 in annual_series_data['ps'] else 0}
@@ -192,7 +191,7 @@ with st.form(key="selecting columns"):
 
           # merge all dictionary keys per ticker
           combined_info = basic_info.copy()  # Make a copy of the basic info
-          combined_info = combined_info | metric_data | ebitPerShare_dict | ev_dict | salesPerShare_dict | operatingMargin_dict | eps_dict | pe_dict | ps_dict | pb_dict
+          combined_info = combined_info | metric_data | ev_dict | salesPerShare_dict | eps_dict | pe_dict | ps_dict | pb_dict | roe_dict
           
           hash_map[ticker] = combined_info
           
@@ -202,10 +201,10 @@ with st.form(key="selecting columns"):
       
 
       # Now, create a DataFrame from the hash_map
-      df_1 = pd.DataFrame.from_dict(hash_map, orient='index')[['finnhubIndustry','pe','ps','pb','eps','epsGrowth5Y','ebitdPerShareAnnual', 'ebitdPerShareTTM', 'ebitdaCagr5Y', 'ebitdaInterimCagr5Y']]
+      df_1 = pd.DataFrame.from_dict(hash_map, orient='index')[['finnhubIndustry','roe','marketCapitalization','ebitdPerShareAnnual','pe','ps','pb','salesPerShare','eps','epsGrowth5Y','ev','operatingMarginAnnual', 'ebitdPerShareTTM', 'ebitdaCagr5Y', 'ebitdaInterimCagr5Y']]
       df_2 = pd.DataFrame.from_dict(gains_data, orient='index', columns=['Recent Dividend','Price'])
       df_final = df_1.join(df_2)
 
       df_final['PE/G'] = df_final['pe'] / df_final['epsGrowth5Y']
-      df_final.rename({'finnhubIndustry':'Industry','pe':'P/E', 'ps':'P/S', 'pb':'P/B', 'eps': 'EPS'}, inplace=True, axis=1)
+      df_final.rename({'finnhubIndustry':'Industry','marketCapitalization':'MarketCap','roe':'ROE', 'ev':'Enterp. Val', 'pe':'P/E', 'ps':'P/S', 'pb':'P/B', 'eps': 'EPS'}, inplace=True, axis=1)
       st.write(df_final)