portfolio-analysis

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

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

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

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'])