--- name: portfolio-analysis description: Analyzing fund portfolios including AUM extraction, holdings counts, and portfolio composition --- # Portfolio Analysis for Hedge Funds ## Overview Portfolio analysis involves extracting fund-level information (AUM, total holdings, composition) from 13-F filings and comparing across time periods. ## Key Metrics ### 1. Assets Under Management (AUM) **Location**: SUMMARYPAGE.tsv ```python import pandas as pd def get_fund_aum(accession_number, summarypage_df): """ Extract AUM for a specific fund """ fund_summary = summarypage_df[ summarypage_df['ACCESSION_NUMBER'] == accession_number ] # Look for AUM-related fields in the data # Common field names: AUM, ASSETS_UNDER_MANAGEMENT, TOTALASSETS, etc. # Check available columns first available_cols = fund_summary.columns.tolist() print(f"Available columns: {available_cols}") # If found, extract and convert to numeric for col in available_cols: if 'AUM' in col.upper() or 'ASSET' in col.upper() or 'TOTAL' in col.upper(): try: aum_value = pd.to_numeric( fund_summary[col].iloc[0], errors='coerce' ) return aum_value except: continue return None # Usage q3_summary = pd.read_csv('/root/2025-q3/SUMMARYPAGE.tsv', sep='\t') aum = get_fund_aum('specific_accession_number', q3_summary) ``` ### 2. Portfolio Holdings Count ```python def get_holdings_count(accession_number, infotable_df): """ Count the number of stocks held by a fund """ fund_holdings = infotable_df[ infotable_df['ACCESSION_NUMBER'] == accession_number ] return len(fund_holdings) # Usage q3_infotable = pd.read_csv('/root/2025-q3/INFOTABLE.tsv', sep='\t') holdings_count = get_holdings_count('specific_accession_number', q3_infotable) print(f"Renaissance Technologies holds {holdings_count} stocks") ``` ### 3. Portfolio Composition ```python def analyze_portfolio_composition(accession_number, infotable_df): """ Analyze fund's portfolio composition """ fund_holdings = infotable_df[ infotable_df['ACCESSION_NUMBER'] == accession_number ].copy() # Ensure VALUE is numeric (in thousands) fund_holdings['VALUE'] = pd.to_numeric(fund_holdings['VALUE'], errors='coerce') # Calculate statistics total_portfolio_value = fund_holdings['VALUE'].sum() * 1000 # Convert to dollars num_holdings = len(fund_holdings) avg_position = total_portfolio_value / num_holdings if num_holdings > 0 else 0 # Get top positions top_10 = fund_holdings.nlargest(10, 'VALUE')[ ['NAMEOFISSUER', 'CUSIP', 'VALUE', 'SSHPRNAMT'] ] top_10['VALUE_MILLIONS'] = top_10['VALUE'].astype(float) / 1000 return { 'total_value_dollars': total_portfolio_value, 'num_holdings': num_holdings, 'avg_position_dollars': avg_position, 'top_10_positions': top_10, 'concentration': (top_10['VALUE'].sum() / fund_holdings['VALUE'].sum() * 100) } # Usage composition = analyze_portfolio_composition('accession_number', q3_infotable) print(f"Total Portfolio Value: ${composition['total_value_dollars']:,.0f}") print(f"Number of Holdings: {composition['num_holdings']}") print(f"Top 10 Positions Concentration: {composition['concentration']:.2f}%") print("\nTop 10 Positions:") print(composition['top_10_positions']) ``` ### 4. Finding Funds with Specific Stocks ```python def find_funds_holding_stock(cusip, infotable_df, coverpage_df=None): """ Find all funds holding a specific stock (by CUSIP) """ holdings = infotable_df[infotable_df['CUSIP'] == cusip].copy() if len(holdings) == 0: return pd.DataFrame() # Optional: Enrich with fund names from coverpage if coverpage_df is not None: holdings = holdings.merge( coverpage_df[['ACCESSION_NUMBER', 'FILINGMANAGER_NAME']], on='ACCESSION_NUMBER', how='left' ) # Sort by value (descending) holdings['VALUE'] = pd.to_numeric(holdings['VALUE'], errors='coerce') return holdings.sort_values('VALUE', ascending=False) # Usage q3_infotable = pd.read_csv('/root/2025-q3/INFOTABLE.tsv', sep='\t') q3_coverpage = pd.read_csv('/root/2025-q3/COVERPAGE.tsv', sep='\t') # Find who's holding Palantir (CUSIP: 69608A702) palantir_holders = find_funds_holding_stock('69608A702', q3_infotable, q3_coverpage) print(palantir_holders[['FILINGMANAGER_NAME', 'VALUE', 'SSHPRNAMT']]) ``` ## Complete Fund Analysis Workflow ```python import pandas as pd def comprehensive_fund_analysis(accession_number, q_infotable, q_summarypage, q_coverpage): """ Complete analysis of a fund for a quarter """ # Get fund info fund_info = q_coverpage[ q_coverpage['ACCESSION_NUMBER'] == accession_number ].iloc[0] # Get holdings holdings = q_infotable[ q_infotable['ACCESSION_NUMBER'] == accession_number ].copy() holdings['VALUE'] = pd.to_numeric(holdings['VALUE'], errors='coerce') # Get AUM summary = q_summarypage[ q_summarypage['ACCESSION_NUMBER'] == accession_number ] analysis = { 'fund_name': fund_info['FILINGMANAGER_NAME'], 'accession_number': accession_number, 'quarter': fund_info['REPORTCALENDARORQUARTER'], 'num_holdings': len(holdings), 'total_portfolio_value_thousands': holdings['VALUE'].sum(), 'top_5_positions': holdings.nlargest(5, 'VALUE')[ ['NAMEOFISSUER', 'CUSIP', 'VALUE'] ].to_dict('records') } return analysis # Usage q3_infotable = pd.read_csv('/root/2025-q3/INFOTABLE.tsv', sep='\t') q3_summarypage = pd.read_csv('/root/2025-q3/SUMMARYPAGE.tsv', sep='\t') q3_coverpage = pd.read_csv('/root/2025-q3/COVERPAGE.tsv', sep='\t') analysis = comprehensive_fund_analysis('0000320193-25-000069', q3_infotable, q3_summarypage, q3_coverpage) print(f"Fund: {analysis['fund_name']}") print(f"Holdings: {analysis['num_holdings']}") print(f"Portfolio Value: ${analysis['total_portfolio_value_thousands'] * 1000:,.0f}") ``` ## Extracting AUM from SUMMARYPAGE ```python def extract_aum_from_summarypage(accession_number, summarypage_df): """ Extract AUM from summarypage with fallback methods """ fund_row = summarypage_df[ summarypage_df['ACCESSION_NUMBER'] == accession_number ] if fund_row.empty: return None fund_row = fund_row.iloc[0] # Try different possible column names for AUM possible_columns = [ 'TABLE_OF_CONTENTS', # Sometimes contains AUM info 'AUM', 'TOTALASSETS', 'VALUE' ] # Inspect all columns for col in summarypage_df.columns: if col in fund_row.index: value = fund_row[col] if pd.notna(value): try: numeric_val = pd.to_numeric(value, errors='coerce') if numeric_val and numeric_val > 1000000: # Likely an AUM value (> 1M) return numeric_val except: pass return None ``` ## Performance Metrics ```python def calculate_performance_metrics(accession_q2, accession_q3, infotable_q2, infotable_q3): """ Calculate portfolio changes between quarters """ # Get holdings for both quarters holdings_q2 = infotable_q2[infotable_q2['ACCESSION_NUMBER'] == accession_q2].copy() holdings_q3 = infotable_q3[infotable_q3['ACCESSION_NUMBER'] == accession_q3].copy() # Standardize column names and numeric types for df in [holdings_q2, holdings_q3]: df['VALUE'] = pd.to_numeric(df['VALUE'], errors='coerce') df['SSHPRNAMT'] = pd.to_numeric(df['SSHPRNAMT'], errors='coerce') # Merge on CUSIP comparison = pd.merge( holdings_q2[['CUSIP', 'NAMEOFISSUER', 'VALUE', 'SSHPRNAMT']], holdings_q3[['CUSIP', 'VALUE', 'SSHPRNAMT']], on='CUSIP', how='outer', suffixes=('_q2', '_q3') ).fillna(0) # Calculate changes comparison['value_change'] = comparison['VALUE_q3'] - comparison['VALUE_q2'] comparison['pct_change'] = (comparison['value_change'] / comparison['VALUE_q2'] * 100).replace([float('inf'), float('-inf')], 0) return comparison.sort_values('value_change', ascending=False) # Usage comparison = calculate_performance_metrics( accession_q2, accession_q3, q2_infotable, q3_infotable ) top_increases = comparison[comparison['value_change'] > 0].head(5) print(top_increases[['NAMEOFISSUER', 'CUSIP', 'value_change']]) ``` ## Best Practices 1. **Always convert VALUE to numeric**: `pd.to_numeric(df['VALUE'], errors='coerce')` 2. **Remember VALUE is in thousands**: Multiply by 1000 for actual dollar amounts 3. **Handle missing accession numbers**: Some funds may not file all quarters 4. **Validate cusip codes**: Check for null or malformed CUSIPs 5. **Compare matching quarters**: Q2 2025 vs Q3 2025, not Q2 2024 vs Q3 2025