Update app.py

app.py

@@ -49,6 +49,10 @@ def calculate_ratios(market_cap, total_revenue, price, dividend_amount, eps=5.0,
     roe = np.random.uniform(5, 25)
     free_cash_flow = np.random.uniform(50000000, 500000000)
     beta = np.random.uniform(0.8, 1.5)
+    ev_ebitda = np.random.uniform(8, 20)  # Placeholder random value
+    price_cash_flow = np.random.uniform(10, 25)
+    operating_margin = np.random.uniform(10, 30)
+    revenue_growth = np.random.uniform(5, 20)
     return {
         'P/E Ratio': pe,
         'P/S Ratio': ps,
@@ -58,7 +62,11 @@ def calculate_ratios(market_cap, total_revenue, price, dividend_amount, eps=5.0,
         'Debt/Equity Ratio': debt_equity,
         'Return on Equity (%)': roe,
         'Free Cash Flow ($)': free_cash_flow,
-        'Beta (Volatility)': beta
+        'Beta (Volatility)': beta,
+        'EV/EBITDA': ev_ebitda,
+        'Price/Cash Flow': price_cash_flow,
+        'Operating Margin (%)': operating_margin,
+        'Revenue Growth (%)': revenue_growth
     }
 
 def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
@@ -83,7 +91,11 @@ def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
 
     info_table = pd.DataFrame(info.items(), columns=["Metric", "Value"])
     ratios_table = pd.DataFrame(ratios.items(), columns=["Metric", "Value"])
-    financial_health = ratios_table[ratios_table["Metric"].isin(["Debt/Equity Ratio", "Return on Equity (%)", "Free Cash Flow ($)", "Beta (Volatility)"])]
+
+    financial_health_metrics = [
+        "Debt/Equity Ratio", "Return on Equity (%)", "Free Cash Flow ($)", "Beta (Volatility)"
+    ]
+    financial_health = ratios_table[ratios_table["Metric"].isin(financial_health_metrics)]
 
     recommendation = "Hold"
     if ratios['P/E Ratio'] < 15 and ratios['Debt/Equity Ratio'] < 1.0 and ratios['Return on Equity (%)'] > 10 and ratios['Beta (Volatility)'] < 1.2:
@@ -92,22 +104,25 @@ def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
         recommendation = "Sell"
 
     report = (
-    f"Company Overview:\n"
-    f"Name: {info.get('Name', 'N/A')}\n"
-    f"Industry: {info.get('Industry', 'N/A')}\n"
-    f"Sector: {info.get('Sector', 'N/A')}\n"
-    f"Market Cap: ${info.get('Market Cap', 0):,.2f}\n\n"
-    f"Financial Metrics:\n"
-    f"P/E Ratio: {ratios.get('P/E Ratio', 'N/A')}\n"
-    f"P/S Ratio: {ratios.get('P/S Ratio', 'N/A')}\n"
-    f"P/B Ratio: {ratios.get('P/B Ratio', 'N/A')}\n"
-    f"PEG Ratio: {ratios.get('PEG Ratio', 'N/A')}\n"
-    f"Dividend Yield: {ratios.get('Dividend Yield', 'N/A')}%\n"
-    f"Debt/Equity Ratio: {ratios.get('Debt/Equity Ratio', 'N/A')}\n"
-    f"Return on Equity: {ratios.get('Return on Equity (%)', 'N/A')}%\n"
-    f"Free Cash Flow: ${ratios.get('Free Cash Flow ($)', 0):,.2f}\n"
-    f"Beta (Volatility): {ratios.get('Beta (Volatility)', 'N/A')}\n"
-
+        f"Company Overview:\n"
+        f"Name: {info.get('Name', 'N/A')}\n"
+        f"Industry: {info.get('Industry', 'N/A')}\n"
+        f"Sector: {info.get('Sector', 'N/A')}\n"
+        f"Market Cap: ${info.get('Market Cap', 0):,.2f}\n\n"
+        f"Financial Metrics:\n"
+        f"P/E Ratio: {ratios.get('P/E Ratio', 'N/A')}\n"
+        f"P/S Ratio: {ratios.get('P/S Ratio', 'N/A')}\n"
+        f"P/B Ratio: {ratios.get('P/B Ratio', 'N/A')}\n"
+        f"PEG Ratio: {ratios.get('PEG Ratio', 'N/A')}\n"
+        f"Dividend Yield: {ratios.get('Dividend Yield', 'N/A')}%\n"
+        f"Debt/Equity Ratio: {ratios.get('Debt/Equity Ratio', 'N/A')}\n"
+        f"Return on Equity: {ratios.get('Return on Equity (%)', 'N/A')}%\n"
+        f"Free Cash Flow: ${ratios.get('Free Cash Flow ($)', 0):,.2f}\n"
+        f"Beta (Volatility): {ratios.get('Beta (Volatility)', 'N/A')}\n"
+        f"EV/EBITDA: {ratios.get('EV/EBITDA', 'N/A')}\n"
+        f"Price/Cash Flow: {ratios.get('Price/Cash Flow', 'N/A')}\n"
+        f"Operating Margin: {ratios.get('Operating Margin (%)', 'N/A')}%\n"
+        f"Revenue Growth: {ratios.get('Revenue Growth (%)', 'N/A')}%\n"
     )
 
     summary_prompt = f"Summarize this financial report clearly and briefly:\n\n{report}"
@@ -119,8 +134,6 @@ def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
     ], ignore_index=True)
 
     return ai_summary, info_table, ratios_table, financial_health, sector_comp, fig
-
-
 # Theme Selection
 selected_theme = os.getenv("APP_THEME", "light")
 if selected_theme == "dark":