sbic-tracker 0.1.0

SBIC investment portfolio analyzer — fund-level IRR/TVPI/DPI, licensee tracking, and SBA program data analysis

sbic-tracker

Professional SBIC investment portfolio analyzer for Python.

Model and analyze Small Business Investment Company (SBIC) portfolios: fund-level IRR, TVPI, DPI, RVPI, vintage-year cohort analysis, peer benchmarking, and sector/state concentration — all built on pure Python with no external dependencies.

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Why sbic-tracker?

SBICs deploy over $6 billion annually into U.S. small businesses through SBA-leveraged funds. Portfolio managers, fund-of-funds analysts, and SBA examiners need consistent, auditable metrics across heterogeneous portfolios. sbic-tracker provides typed Python data structures and financial functions that match SBA and LP reporting standards.

Installation

pip install sbic-tracker

No external dependencies — pure Python 3.9+.

Quickstart

from datetime import date
from sbictracker import (
    SBICLicensee, Investment,
    SBICPortfolio,
    load_sample_investments, load_sample_licensees,
    irr, tvpi, dpi, rvpi,
    vintage_year_analysis, peer_quartile_ranking,
    sector_breakdown, state_breakdown, top_naics,
)

# Load sample data (or plug in your own)
licensees = load_sample_licensees()
investments = load_sample_investments()

# Build a portfolio
portfolio = SBICPortfolio("Apex Growth Fund I")
portfolio.add_many(investments)
print(portfolio.summary())
# === Apex Growth Fund I ===
#   Investments      : 20 (10 realized / 10 unrealized)
#   Called capital   : $78,150,000
#   Distributed      : $85,410,000
#   NAV (unrealized) : $30,550,000
#   TVPI             : 1.48x
#   DPI              : 1.09x
#   RVPI             : 0.39x

# IRR from custom cash flows
fund_flows = [-10_000_000, 0, 500_000, 2_000_000, 8_000_000, 5_000_000]
print(f"Fund IRR: {irr(fund_flows):.2%}")

# Vintage cohort analysis
cohorts = vintage_year_analysis(investments)
for yr, data in sorted(cohorts.items()):
    print(f"  {yr}: {data['count']} investments, TVPI {data['tvpi']:.2f}x")

# Peer quartile ranking
ranking = peer_quartile_ranking(fund_tvpi=1.8, peer_tvpis=[1.2, 1.4, 1.6, 1.9, 2.1])
print(f"Quartile: Q{ranking['quartile']}  ({ranking['percentile']}th percentile)")

# Sector concentration
top = top_naics(investments, n=3)
for code, name, invested in top:
    print(f"  NAICS {code} ({name}): ${invested:,.0f}")

Key Features

Feature	Detail
IRR	Newton-Raphson solver for arbitrary annual cash-flow vectors
TVPI / DPI / RVPI	Industry-standard multiples from first principles
Write-off tracking	Net cost basis automatically reflects partial/full write-offs
Vintage cohort analysis	Group and compare by investment year
Peer quartile ranking	Percentile and Q1–Q4 ranking vs a peer TVPI distribution
SBICPortfolio	Add/remove investments; filter by sector, state, or instrument type
Sector/state breakdown	NAICS 2-digit concentration with portfolio % weights
Sample data	10 licensees + 20 investments for immediate prototyping
SBA URL loader	load_from_sba_url() attempts live SBA data with sample fallback

Use Cases

• Fund managers — Track called/distributed capital and compute NAV-based multiples for LP reporting.
• SBA examiners — Audit licensee leverage ratios and investment-level MOIC across the portfolio.
• Fund-of-funds analysts — Compare SBIC fund vintage cohorts and rank against peer TVPIs.
• Policy researchers — Analyze SBIC capital deployment by sector, state, and instrument type.
• Limited partners — Build DPI/RVPI waterfalls and sensitivity models in Python.

API Reference

Data Classes

SBICLicensee(license_number, fund_name, fund_manager, license_date,
             license_status, license_type, total_capital, sba_leverage, private_capital)
  .leverage_ratio   # sba_leverage / private_capital
  .vintage_year

Investment(investee_company, investment_date, investment_amount, naics_code, state,
           exit_date, exit_proceeds, write_off_amount, instrument_type)
  .is_realized       # bool
  .realized_value    # exit_proceeds if realized, else 0
  .net_cost_basis    # amount - write_off_amount
  .moic              # exit_proceeds / amount (realized only)
  .naics_sector      # human-readable sector name
  .vintage_year

Fund Metrics

irr(cash_flows)                   # Newton-Raphson IRR
tvpi(investments)                 # (distributed + NAV) / called
dpi(investments)                  # distributed / called
rvpi(investments)                 # NAV / called
called_capital(investments)
distributed_capital(investments)
nav(investments)                  # unrealized positions at net cost
total_value(investments)

Portfolio & Analysis

SBICPortfolio(name)
  .add(investment)
  .add_many(investments)
  .remove(investee_company)
  .filter_by_sector(naics_prefix)
  .filter_by_state(state)
  .filter_realized() / .filter_unrealized()
  .summary_stats()
  .summary()

vintage_year_analysis(investments)          # → {year: {count, tvpi, dpi, ...}}
peer_quartile_ranking(fund_tvpi, peer_tvpis) # → {quartile, percentile, peer_median}

sector_breakdown(investments)               # → {naics_2: {sector_name, invested, pct}}
state_breakdown(investments)                # → {state: {count, invested, pct}}
top_naics(investments, n=5)                 # → [(code, name, invested), ...]

License

MIT © Jay Patel