#!/usr/bin/env python3 """ 01_fetch_financials.py - M&A Target Financial Screening Fetches real-time financial data for cybersecurity M&A targets and applies screening criteria for market cap and revenue growth. Targets: SentinelOne (S), Zscaler (ZS), CyberArk (CYBR), Tenable (TENB), Rapid7 (RPD) Screening Criteria: - Market Cap: $2B - $15B - Revenue Growth: > 20% """ import yfinance as yf import pandas as pd import numpy as np from datetime import datetime import warnings warnings.filterwarnings('ignore') # Configuration SESSION_DIR = "/app/sandbox/session_20260203_085757_d926b2c7d1fb" RESULTS_DIR = f"{SESSION_DIR}/results" # M&A Target Tickers TARGETS = { 'S': 'SentinelOne', 'ZS': 'Zscaler', 'CYBR': 'CyberArk', 'TENB': 'Tenable', 'RPD': 'Rapid7' } # Screening Criteria MARKET_CAP_MIN = 2e9 # $2 Billion MARKET_CAP_MAX = 15e9 # $15 Billion REVENUE_GROWTH_MIN = 0.20 # 20% def format_billions(value): """Format value in billions with 2 decimal places.""" if pd.isna(value) or value is None: return "N/A" return f"${value/1e9:.2f}B" def format_percentage(value): """Format value as percentage.""" if pd.isna(value) or value is None: return "N/A" return f"{value*100:.1f}%" def fetch_financial_data(ticker_symbol, company_name): """ Fetch comprehensive financial data for a single ticker. Parameters ---------- ticker_symbol : str Stock ticker symbol company_name : str Company name for display Returns ------- dict Dictionary containing all financial metrics """ print(f" Fetching data for {company_name} ({ticker_symbol})...") try: ticker = yf.Ticker(ticker_symbol) info = ticker.info # Extract key metrics current_price = info.get('currentPrice') or info.get('regularMarketPrice') market_cap = info.get('marketCap') enterprise_value = info.get('enterpriseValue') # Revenue metrics total_revenue = info.get('totalRevenue') revenue_growth = info.get('revenueGrowth') # Quarterly YoY # Alternative revenue growth calculation if not directly available if revenue_growth is None: # Try to get from financials try: financials = ticker.quarterly_financials if financials is not None and not financials.empty: if 'Total Revenue' in financials.index: revenues = financials.loc['Total Revenue'].dropna() if len(revenues) >= 5: # Need at least 5 quarters for YoY current_q = revenues.iloc[0] year_ago_q = revenues.iloc[4] if year_ago_q > 0: revenue_growth = (current_q - year_ago_q) / year_ago_q except Exception as e: print(f" Warning: Could not calculate revenue growth: {e}") # Forward estimates for revenue growth forward_pe = info.get('forwardPE') earnings_growth = info.get('earningsGrowth') # Calculate EV/Revenue multiple ev_revenue = None if enterprise_value and total_revenue and total_revenue > 0: ev_revenue = enterprise_value / total_revenue return { 'Ticker': ticker_symbol, 'Company': company_name, 'Current_Price': current_price, 'Market_Cap': market_cap, 'Enterprise_Value': enterprise_value, 'LTM_Revenue': total_revenue, 'Revenue_Growth': revenue_growth, 'EV_Revenue_Multiple': ev_revenue, 'Forward_PE': forward_pe, 'Earnings_Growth': earnings_growth, 'Sector': info.get('sector'), 'Industry': info.get('industry'), 'Data_Timestamp': datetime.now().isoformat() } except Exception as e: print(f" Error fetching {ticker_symbol}: {e}") return { 'Ticker': ticker_symbol, 'Company': company_name, 'Current_Price': None, 'Market_Cap': None, 'Enterprise_Value': None, 'LTM_Revenue': None, 'Revenue_Growth': None, 'EV_Revenue_Multiple': None, 'Forward_PE': None, 'Earnings_Growth': None, 'Sector': None, 'Industry': None, 'Data_Timestamp': datetime.now().isoformat(), 'Error': str(e) } def apply_screening_criteria(df): """ Apply M&A screening criteria to the dataset. Parameters ---------- df : pd.DataFrame Financial data for all targets Returns ------- pd.DataFrame DataFrame with screening results added """ print("\n Applying screening criteria...") # Market Cap criteria: $2B - $15B df['MarketCap_Pass'] = df['Market_Cap'].apply( lambda x: MARKET_CAP_MIN <= x <= MARKET_CAP_MAX if pd.notna(x) else False ) # Revenue Growth criteria: > 20% df['RevenueGrowth_Pass'] = df['Revenue_Growth'].apply( lambda x: x > REVENUE_GROWTH_MIN if pd.notna(x) else False ) # Overall pass (both criteria) df['Overall_Pass'] = df['MarketCap_Pass'] & df['RevenueGrowth_Pass'] return df def generate_screening_summary(df): """ Generate a markdown summary of the screening results. Parameters ---------- df : pd.DataFrame Financial data with screening results Returns ------- str Markdown formatted summary """ summary_lines = [ "# M&A Target Screening Summary", "", f"**Date**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", "", "## Screening Criteria", "", f"- **Market Cap Range**: $2B - $15B", f"- **Revenue Growth**: > 20% (YoY)", "", "## Results Overview", "", f"- **Total Targets Analyzed**: {len(df)}", f"- **Targets Passing All Criteria**: {df['Overall_Pass'].sum()}", f"- **Targets Failing**: {(~df['Overall_Pass']).sum()}", "", "## Detailed Results", "", "| Company | Ticker | Market Cap | Revenue Growth | EV/Revenue | Market Cap Pass | Growth Pass | Overall |", "|---------|--------|------------|----------------|------------|-----------------|-------------|---------|" ] for _, row in df.iterrows(): mc_formatted = format_billions(row['Market_Cap']) rg_formatted = format_percentage(row['Revenue_Growth']) ev_rev = f"{row['EV_Revenue_Multiple']:.1f}x" if pd.notna(row['EV_Revenue_Multiple']) else "N/A" mc_pass = "PASS" if row['MarketCap_Pass'] else "FAIL" rg_pass = "PASS" if row['RevenueGrowth_Pass'] else "FAIL" overall = "PASS" if row['Overall_Pass'] else "FAIL" summary_lines.append( f"| {row['Company']} | {row['Ticker']} | {mc_formatted} | {rg_formatted} | " f"{ev_rev} | {mc_pass} | {rg_pass} | **{overall}** |" ) summary_lines.extend([ "", "## Individual Analysis", "" ]) for _, row in df.iterrows(): summary_lines.extend([ f"### {row['Company']} ({row['Ticker']})", "", f"- **Current Price**: ${row['Current_Price']:.2f}" if pd.notna(row['Current_Price']) else "- **Current Price**: N/A", f"- **Market Cap**: {format_billions(row['Market_Cap'])}", f"- **Enterprise Value**: {format_billions(row['Enterprise_Value'])}", f"- **LTM Revenue**: {format_billions(row['LTM_Revenue'])}", f"- **Revenue Growth (YoY)**: {format_percentage(row['Revenue_Growth'])}", f"- **EV/Revenue Multiple**: {row['EV_Revenue_Multiple']:.2f}x" if pd.notna(row['EV_Revenue_Multiple']) else "- **EV/Revenue Multiple**: N/A", "" ]) # Analysis notes notes = [] if not row['MarketCap_Pass']: mc = row['Market_Cap'] if pd.notna(mc): if mc < MARKET_CAP_MIN: notes.append(f"Market cap below minimum threshold (${mc/1e9:.1f}B < $2B)") else: notes.append(f"Market cap above maximum threshold (${mc/1e9:.1f}B > $15B)") if not row['RevenueGrowth_Pass']: rg = row['Revenue_Growth'] if pd.notna(rg): notes.append(f"Revenue growth below threshold ({rg*100:.1f}% < 20%)") else: notes.append("Revenue growth data not available") if notes: summary_lines.append(f"**Notes**: {'; '.join(notes)}") summary_lines.append("") elif row['Overall_Pass']: summary_lines.append("**Status**: Meets all screening criteria - potential acquisition target") summary_lines.append("") summary_lines.extend([ "---", "", "*Generated by K-Dense M&A Target Screening Tool*" ]) return "\n".join(summary_lines) def main(): """Main execution function.""" print("=" * 60) print("M&A TARGET FINANCIAL SCREENING") print("Cybersecurity Sector Analysis") print("=" * 60) print(f"\nTimestamp: {datetime.now().isoformat()}") print(f"\nTargets: {', '.join([f'{v} ({k})' for k, v in TARGETS.items()])}") print("\nScreening Criteria:") print(f" - Market Cap: ${MARKET_CAP_MIN/1e9:.0f}B - ${MARKET_CAP_MAX/1e9:.0f}B") print(f" - Revenue Growth: > {REVENUE_GROWTH_MIN*100:.0f}%") # Fetch financial data print("\n" + "-" * 40) print("STEP 1: Fetching Financial Data") print("-" * 40) results = [] total = len(TARGETS) for i, (ticker, company) in enumerate(TARGETS.items(), 1): print(f"\nProgress: {i}/{total}") data = fetch_financial_data(ticker, company) results.append(data) # Create DataFrame df = pd.DataFrame(results) # Apply screening criteria print("\n" + "-" * 40) print("STEP 2: Applying Screening Criteria") print("-" * 40) df = apply_screening_criteria(df) # Save raw financial data print("\n" + "-" * 40) print("STEP 3: Saving Results") print("-" * 40) csv_path = f"{RESULTS_DIR}/financial_screening_data.csv" df.to_csv(csv_path, index=False) print(f" Saved: {csv_path}") # Generate and save summary summary_md = generate_screening_summary(df) summary_path = f"{RESULTS_DIR}/screening_summary.md" with open(summary_path, 'w') as f: f.write(summary_md) print(f" Saved: {summary_path}") # Print summary to console print("\n" + "=" * 60) print("SCREENING RESULTS SUMMARY") print("=" * 60) passed = df[df['Overall_Pass']] failed = df[~df['Overall_Pass']] print(f"\nTargets PASSING all criteria ({len(passed)}):") for _, row in passed.iterrows(): print(f" - {row['Company']} ({row['Ticker']}): " f"MC={format_billions(row['Market_Cap'])}, " f"Growth={format_percentage(row['Revenue_Growth'])}") print(f"\nTargets FAILING criteria ({len(failed)}):") for _, row in failed.iterrows(): reasons = [] if not row['MarketCap_Pass']: reasons.append("Market Cap") if not row['RevenueGrowth_Pass']: reasons.append("Revenue Growth") print(f" - {row['Company']} ({row['Ticker']}): Failed {', '.join(reasons)}") print("\n" + "=" * 60) print("EXECUTION COMPLETE") print("=" * 60) return df if __name__ == "__main__": main()