updating capm, ddm

pages/1_Fundamentals.py

@@ -51,24 +51,6 @@ def get_company_metrics(ticker):
   return metric_data, annual_series_data, quarterly_series_data
 
 
-def calc_beta(ticker):
-  tick = yf.Ticker(f'{ticker}')
-  sp_hist = yf.Ticker('^GSPC').history(period="5y")['Close']
-  ticker_hist = tick.history(period="5y")['Close']
-
-  sp500 = sp_hist.reset_index().rename(columns={'Close':'sp500_Close'})
-  sp500['sp500_variance'] = sp500['sp500_Close'].var()
-
-  merged_df = sp500.merge(ticker_hist, how='outer', on='Date')
-
-  beta = merged_df.cov(numeric_only=True).loc['sp500_Close']['Close'] / sp500['sp500_variance'].max()
-  max_date = merged_df['Date'].max()
-
-  d = {'Beta': beta, 'Symbol': ticker}
-  df_beta = pd.DataFrame(d, index = [0])
-  return df_beta.set_index('Symbol')
-
-
 def get_equity_gains(ticker, period):
   sp = yf.Ticker(f"{ticker}")
   sp_hist = sp.history(period=f'{period}d')[['Close','Dividends']]
@@ -135,7 +117,6 @@ with st.form(key="selecting columns"):
         for ticker in symbols:
             # make all the API calls and capture return json
             basic_info = get_industry(ticker) 
-            st.write(metric_data)
             metric_data, annual_series_data, quarterly_series_data  = get_company_metrics(ticker)
 
             # reformat all JSON returns to be flattened dictionaries 
@@ -158,18 +139,18 @@ 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','beta','pe','ps','pb','pfcf','eps','roe','roeTTM','dividendGrowthRate5Y','epsGrowth5Y']]
+        df_1 = pd.DataFrame.from_dict(hash_map, orient='index')[['finnhubIndustry','beta','pe','ps','pb','pfcf','eps','roe','roeTTM','dividendPerShareTTM','dividendGrowthRate5Y','epsGrowth5Y']]
         df_2 = pd.DataFrame.from_dict(gains_data, orient='index', columns=['Recent Dividend','Price'])
 
         df_final = df_1.join(df_2)
         
         # calculate additional columns 
-        df_final['5Y_SP500_growth'], _, _ = get_equity_gains(ticker= '^GSPC', period=365)
+        df_final['1Y_SP500_growth'], _, _ = get_equity_gains(ticker= '^GSPC', period=720)
         df_final['90_day_tbill'] = 4.06
         df_final['dividendGrowthRate5Y'] = df_final['dividendGrowthRate5Y']/100
-        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'])) / (df_final['CAPM'] - df_final['dividendGrowthRate5Y'])
-        df_final = df_final[['finnhubIndustry','Price','eps','roe','roeTTM','pe','ps','pb','pfcf','epsGrowth5Y','beta','Recent Dividend','90_day_tbill','5Y_SP500_growth','dividendGrowthRate5Y','CAPM','DDM']]
+        df_final['1Y_CAPM'] = df_final['90_day_tbill']/100 + df_final['beta'] * (df_final['1Y_SP500_growth'] - df_final['90_day_tbill']/100)
+        df_final['1Y_DDM'] = (df_final['Recent Dividend'] * (1+df_final['dividendGrowthRate5Y'])) / (df_final['1Y_CAPM'] - df_final['dividendGrowthRate5Y'])
+        df_final = df_final[['finnhubIndustry','Price','eps','roe','roeTTM','pe','ps','pb','pfcf','epsGrowth5Y','beta','Recent Dividend','90_day_tbill','1Y_SP500_growth','dividendPerShareTTM','dividendGrowthRate5Y','1Y_CAPM','1Y_DDM']]
         df_final.rename({'finnhubIndustry':'Industry', 'eps':'EPS', 'roe':'ROE','pe':'P/E','ps':'P/S', 'pb':'P/B','pfcf':'P/FCF','beta':'Beta'}, inplace=True, axis=1)
         st.write(df_final)
 
@@ -187,7 +168,7 @@ with st.form(key="selecting columns"):
           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}
+          ps_dict = {'ps': annual_series_data['ps'][0]['v'] if 0 in annual_series_data else 0}
           pb_dict = {'pb': annual_series_data['pb'][0]['v'] if 'pb' in annual_series_data else 0}
 
           # merge all dictionary keys per ticker