Update app.py

app.py

@@ -10,7 +10,7 @@ warnings.filterwarnings("ignore")
 import os
 
 # Financial Modeling Prep API Key
-FMP_API_KEY  = os.getenv("FMP_API_KEY")
+FMP_API_KEY = os.getenv("FMP_API_KEY")
 
 # Function to get data from Finviz
 def get_finviz_data(ticker):
@@ -34,11 +34,21 @@ def get_finviz_data(ticker):
 
 # Function to calculate beta
 def calculate_beta(ticker, start_date="2018-01-01", end_date=None, market_ticker="^GSPC"):
-    stock_data = yf.download(ticker, start=start_date, end=end_date)['Adj Close']
-    market_data = yf.download(market_ticker, start=start_date, end=end_date)['Adj Close']
+    stock_data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)
+    market_data = yf.download(market_ticker, start=start_date, end=end_date, auto_adjust=False)
     
-    stock_returns = stock_data.pct_change().dropna()
-    market_returns = market_data.pct_change().dropna()
+    if isinstance(stock_data.columns, pd.MultiIndex):
+        stock_data.columns = stock_data.columns.get_level_values(0)
+    if isinstance(market_data.columns, pd.MultiIndex):
+        market_data.columns = market_data.columns.get_level_values(0)
+    
+    if stock_data.empty or market_data.empty:
+        raise ValueError(f"No data retrieved for {ticker} or {market_ticker}")
+    if len(stock_data) < 2 or len(market_data) < 2:
+        raise ValueError("Insufficient data points for beta calculation")
+    
+    stock_returns = stock_data['Adj Close'].pct_change().dropna()
+    market_returns = market_data['Adj Close'].pct_change().dropna()
     
     combined_data = pd.concat([stock_returns, market_returns], axis=1).dropna()
     combined_data.columns = ['Stock_Returns', 'Market_Returns']
@@ -52,14 +62,26 @@ def calculate_beta(ticker, start_date="2018-01-01", end_date=None, market_ticker
 
 # Function to get risk-free rate
 def get_risk_free_rate(risk_free_rate_ticker="^TYX"):
-    treasury_data = yf.download(risk_free_rate_ticker, period="1d")['Close']
-    risk_free_rate = treasury_data.iloc[-1] / 100
+    treasury_data = yf.download(risk_free_rate_ticker, period="1d", auto_adjust=False)
+    if isinstance(treasury_data.columns, pd.MultiIndex):
+        treasury_data.columns = treasury_data.columns.get_level_values(0)
+    if treasury_data.empty:
+        raise ValueError(f"No data retrieved for {risk_free_rate_ticker}")
+    
+    risk_free_rate = treasury_data['Close'].iloc[-1] / 100
     return risk_free_rate
 
 # Function to calculate market risk premium
 def calculate_market_risk_premium(market_ticker="^GSPC", start_date="2018-01-01", end_date=None, risk_free_rate_ticker="^TYX"):
-    market_data = yf.download(market_ticker, start=start_date, end=end_date)['Adj Close']
-    market_returns = market_data.pct_change().dropna()
+    market_data = yf.download(market_ticker, start=start_date, end=end_date, auto_adjust=False)
+    if isinstance(market_data.columns, pd.MultiIndex):
+        market_data.columns = market_data.columns.get_level_values(0)
+    if market_data.empty:
+        raise ValueError(f"No data retrieved for {market_ticker}")
+    if len(market_data) < 2:
+        raise ValueError("Insufficient data points for market risk premium calculation")
+    
+    market_returns = market_data['Adj Close'].pct_change().dropna()
     average_annual_market_return = market_returns.mean() * 252
     
     risk_free_rate = get_risk_free_rate(risk_free_rate_ticker)
@@ -220,7 +242,7 @@ def get_dcf_valuation(ticker, api_key):
 def get_levered_dcf_valuation(ticker, api_key):
     url = f"https://financialmodelingprep.com/api/v4/advanced_levered_discounted_cash_flow?symbol={ticker}&apikey={api_key}"
     response = requests.get(url)
-    if (response.status_code == 200):
+    if response.status_code == 200:
         data = response.json()
         if data:
             return data[0]['equityValuePerShare']
@@ -324,7 +346,13 @@ def perform_dcf_analysis(ticker, start_date="2018-01-01", end_date=None, market_
     required_return = calculate_required_return(risk_free_rate, beta, market_risk_premium)
     cost_of_equity = calculate_cost_of_equity(beta, market_risk_premium, risk_free_rate)
     
-    current_price = yf.download(ticker, period="1d")['Adj Close'].iloc[-1]
+    current_price_data = yf.download(ticker, period="1d", auto_adjust=False)
+    if isinstance(current_price_data.columns, pd.MultiIndex):
+        current_price_data.columns = current_price_data.columns.get_level_values(0)
+    if current_price_data.empty:
+        raise ValueError(f"No current price data retrieved for {ticker}")
+    current_price = current_price_data['Adj Close'].iloc[-1]
+    
     outstanding_shares = get_outstanding_shares(ticker)
     market_cap = current_price * outstanding_shares
     
@@ -367,48 +395,26 @@ st.title("Stock Price Fair Valuation with DCF")
 # Explanation of the analysis
 st.markdown("""
 This app performs a Discounted Cash Flow (DCF) analysis to estimate the intrinsic value of a stock. 
-
 It calculates the present value of expected future cash flows, factoring in growth rates, beta, cost of equity, cost of debt, and market conditions. 
 """)
 
-#st.markdown("### DCF Model Overview")
-
 with st.expander("DCF Analysis Overview", expanded=False):
     st.latex(r"""
     \text{DCF} = \sum_{t=1}^{n} \frac{\text{FCF}_t}{(1 + r)^t} + \frac{\text{TV}}{(1 + r)^n}
     """)
 
-    # st.markdown("""
-    # Where:
-    # - \( FCF_t \) is the Free Cash Flow in year \( t \)
-    # - \( r \) is the discount rate (WACC)
-    # - \( n \) is the number of projected years
-    # - \( TV \) is the Terminal Value
-    # """)
-
     st.markdown("""
     - **Cost of Equity** is calculated using CAPM, considering the risk-free rate, the stock’s beta, and the market risk premium.
-
     - **Cost of Debt** is the effective interest rate on debt, adjusted for the tax benefit.
-
     - **WACC** combines the cost of equity and after-tax cost of debt, weighted by the company’s capital structure. It’s used as the discount rate in the DCF analysis.
-
     - **Free Cash Flow (FCF)** is the cash available after capital expenditures, used for projecting future cash flows.
-
     - **Terminal Value (TV)** estimates the company’s value beyond the forecast period using a terminal growth rate, which is a conservative long-term rate.
-
     - **Growth Rates** are the analysts' forecasts for EPS growth over the next 5 years, which are used to project near-term FCF.
-
     - **Intrinsic Value per Share** is the total present value of future cash flows divided by shares outstanding.
-
     - **Margin of Safety** is the difference between the intrinsic value and the market price, providing a buffer for investment decisions.
-
     For more details on the methodology, [click here](https://entreprenerdly.com/calculate-fair-value-of-stocks-using-dcf-with-public-data/).
     """)
 
-
-
-
 with st.sidebar:
     with st.expander("How to Use the App", expanded=False):
         st.markdown("""
@@ -427,32 +433,37 @@ with st.sidebar:
         risk_free_rate_ticker = st.text_input("Risk-Free Rate Ticker", "^TYX", help="Enter the ticker for the risk-free rate, e.g., '^TYX' for the 30-year Treasury yield.")
 
 if st.sidebar.button("Run DCF Analysis"):
-    with st.spinner("Performing DCF analysis..."):
-        analysis_results = perform_dcf_analysis(
-            ticker_symbol,
-            start_date=start_date_for_beta.strftime('%Y-%m-%d'),
-            market_ticker=market_ticker_for_beta,
-            terminal_growth_rate=terminal_growth_rate,
-            risk_free_rate_ticker=risk_free_rate_ticker
-        )
-    
-    if analysis_results:
-        # Retrieve DCF and Levered DCF valuations
-        automatic_dcf_valuation = get_dcf_valuation(ticker_symbol, FMP_API_KEY)
-        levered_dcf_valuation = get_levered_dcf_valuation(ticker_symbol, FMP_API_KEY)
+    try:
+        with st.spinner("Performing DCF analysis..."):
+            analysis_results = perform_dcf_analysis(
+                ticker_symbol,
+                start_date=start_date_for_beta.strftime('%Y-%m-%d'),
+                market_ticker=market_ticker_for_beta,
+                terminal_growth_rate=terminal_growth_rate,
+                risk_free_rate_ticker=risk_free_rate_ticker
+            )
+        
+        if analysis_results:
+            # Retrieve DCF and Levered DCF valuations
+            automatic_dcf_valuation = get_dcf_valuation(ticker_symbol, FMP_API_KEY)
+            levered_dcf_valuation = get_levered_dcf_valuation(ticker_symbol, FMP_API_KEY)
 
-        # Add DCF valuations to the analysis results
-        analysis_results['Automatic_DCF_Valuation'] = automatic_dcf_valuation
-        analysis_results['Levered_DCF_Valuation'] = levered_dcf_valuation
+            # Add DCF valuations to the analysis results
+            analysis_results['Automatic_DCF_Valuation'] = automatic_dcf_valuation
+            analysis_results['Levered_DCF_Valuation'] = levered_dcf_valuation
 
-        # Plot the results
-        plot_intrinsic_value_vs_stock_price(analysis_results)
+            # Plot the results
+            plot_intrinsic_value_vs_stock_price(analysis_results)
 
-        # Print key metrics
-        print_key_metrics(analysis_results)
+            # Print key metrics
+            print_key_metrics(analysis_results)
 
-        # Display financial dataframes
-        display_financial_dataframes(analysis_results)
+            # Display financial dataframes
+            display_financial_dataframes(analysis_results)
+        else:
+            st.error("DCF analysis failed. Check ticker or API key.")
+    except Exception as e:
+        st.error(f"An error occurred while running the analysis: {e}")
 
 hide_streamlit_style = """
 <style>
@@ -460,4 +471,4 @@ hide_streamlit_style = """
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)      
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)