added new metrics to Value strat

pages/1_Fundamentals.py

@@ -7,6 +7,7 @@ import streamlit as st
 import yfinance as yf
 from datetime import timedelta
 from dotenv import load_dotenv
+from pandas import to_datetime
 
 load_dotenv()
 
@@ -73,17 +74,19 @@ def get_equity_gains(ticker, period):
   sp_hist = sp.history(period=f'{period}d')[['Close','Dividends']]
   sp500 = sp_hist.reset_index()
 
-  todays_close = sp500.sort_values('Date', ascending=False).iloc[0]
-  sp500_new_index = sp500.set_index('Date')
+  todays_close = sp500.iloc[-1]
+  todays_close_date = todays_close.Date.tz_localize(None)
+  sp500.set_index('Date', inplace=True)
+  sp500.index = sp500.index.tz_localize(None)
 
   val = {}
 
-  if todays_close[0] - timedelta(days=period) in sp500_new_index.index:
-      delta = todays_close['Close'] - sp500_new_index.loc[todays_close[0] - timedelta(days=period)]['Close'] 
-      val[f'5Y_change'] = (delta / sp500_new_index.loc[todays_close[0] - timedelta(days=period)]['Close'])
+  if todays_close_date - timedelta(days=period) in sp500.index:
+      delta = todays_close.Close - sp500.loc[todays_close_date - timedelta(days=period)]['Close'] 
+      val[f'5Y_change'] = (delta / sp500.loc[todays_close_date - timedelta(days=period)]['Close'])
   else: 
-      delta = todays_close['Close'] - sp500_new_index.loc[todays_close[0] - timedelta(days=period-4)]['Close'] 
-      val[f'5Y_change'] = (delta / sp500_new_index.loc[todays_close[0] - timedelta(days=period-4)]['Close'])
+      delta = todays_close.Close - sp500.loc[todays_close_date - timedelta(days=period-4)]['Close'] 
+      val[f'5Y_change'] = (delta / sp500.loc[todays_close_date - timedelta(days=period-4)]['Close'])
 
 
   # add in dividend
@@ -131,53 +134,43 @@ with st.form(key="selecting columns"):
         hash_map = {}
         
         for ticker in symbols:
-            st.spinner(text=f"Loading {ticker} Data...")
             # 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)
-            st.write(annual_series_data)
-            
+
             # reformat all JSON returns to be flattened dictionaries 
             roe_dict = {'roe': annual_series_data['roe'][0]['v'] if 'roe' in annual_series_data else 0}
-            totalDebtToTotalCapital_y_dict = {'totalDebtToTotalCapital_y' : annual_series_data['totalDebtToTotalCapital'][0]['v'] if 'totalDebtToTotalCapital' in  annual_series_data else 0}
-            totalDebtToEquity_y_dict = {'totalDebtToEquity_y' :   annual_series_data['totalDebtToEquity'][0]['v'] if 'totalDebtToEquity'in annual_series_data else 0}
-            eps_dict = {'eps' :   annual_series_data['eps'][0]['v'] if 'eps' in annual_series_data else 0}
-            totalDebtToTotalCapital_q_dict = {'totalDebtToTotalCapital_q' :   quarterly_series_data['totalDebtToTotalCapital'][0]['v'] if 'totalDebtToTotalCapital' in quarterly_series_data else 'N/A'}
-            totalDebtToEquity_q_dict = {'totalDebtToEquity_q' :   quarterly_series_data['totalDebtToEquity'][0]['v'] if 'totalDebtToEquity' 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}
+            pb_dict = {'pb': annual_series_data['pb'][0]['v'] if 'pb' in annual_series_data else 0}
+            pfcf_dict = {'pfcf': annual_series_data['pfcf'][0]['v'] if 'pfcf' in annual_series_data else 0}
+
             # 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
+            combined_info = combined_info | metric_data | roe_dict | eps_dict | pe_dict | ps_dict | pb_dict | pfcf_dict
             
             hash_map[ticker] = combined_info
             
-            # beta calculations
-            df_b = calc_beta(ticker)
-            beta_dfs.append(df_b)
-            
             # equity gains
             _, div, close_price = get_equity_gains(ticker=ticker, period=1810)
             gains_data[ticker] = [div, close_price]
         
 
         # 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_1 = pd.DataFrame.from_dict(hash_map, orient='index')[['finnhubIndustry','beta','pe','ps','pb','pfcf','eps','roe','roeTTM','dividendGrowthRate5Y','epsGrowth5Y','payoutRatioAnnual','payoutRatioTTM']]
         df_2 = pd.DataFrame.from_dict(gains_data, orient='index', columns=['Recent Dividend','Price'])
 
-        df_apis = df_1.join(beta_df)
-        df_final = df_apis.join(df_2)
+        df_final = df_1.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['P/E Ratio'] = df_final['Price'] / df_final['eps']
         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['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','P/E Ratio','epsGrowth5Y','payoutRatioAnnual','payoutRatioTTM','roeTTM','totalDebtToEquity_y','totalDebtToEquity_q', 'totalDebtToTotalCapital_y','totalDebtToTotalCapital_q','Beta','Recent Dividend','90_day_tbill','5Y_SP500_growth','dividendGrowthRate5Y','CAPM','DDM']]
-        df_final.rename({'finnhubIndustry':'Industry', 'eps':'EPS', 'roe':'ROE'}, inplace=True, axis=1)
+        df_final = df_final[['finnhubIndustry','Price','eps','roe','roeTTM','pe','ps','pb','pfcf','epsGrowth5Y','payoutRatioAnnual','payoutRatioTTM','beta','Recent Dividend','90_day_tbill','5Y_SP500_growth','dividendGrowthRate5Y','CAPM','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)
 
     if submit_button and symbols and strategy_selection == 'Growth':