Tutorials¶

Interactive Jupyter notebooks to learn the Financial Health Calculator.

Colab Notebooks¶

These notebooks run in Google Colab - no installation required!

1. CEFR Basics¶

Learn the fundamentals of CEFR:

What CEFR measures
Defining assets with tax and liquidity characteristics
Defining liabilities and spending needs
Calculating and interpreting your CEFR
Visualizing the haircut breakdown

2. Time Distribution Analysis¶

Explore Monte Carlo simulations:

Running basic simulations
Understanding success rates
Interpreting percentile bands
Visualizing portfolio trajectories
Sensitivity analysis

3. Withdrawal Strategy Comparison¶

Compare withdrawal approaches:

Fixed SWR (4% rule)
Guardrails strategy
Variable Percentage Withdrawal
RMD-style withdrawals
Trade-offs between stability and spending

Running Locally¶

Clone the repo and run notebooks locally:

git clone https://github.com/engineerinvestor/financial-health-calculator.git
cd financial-health-calculator
pip install -e ".[dev]"
jupyter notebook examples/

Example Code¶

Quick CEFR Check¶

from fundedness import Asset, BalanceSheet, Liability, compute_cefr
from fundedness.models.assets import AccountType, LiquidityClass, ConcentrationLevel

# Simple example
result = compute_cefr(
    balance_sheet=BalanceSheet(assets=[
        Asset(
            name="Portfolio",
            value=1_000_000,
            account_type=AccountType.TAXABLE,
            liquidity_class=LiquidityClass.MARKETABLE,
            concentration_level=ConcentrationLevel.DIVERSIFIED,
        ),
    ]),
    liabilities=[
        Liability(name="Spending", annual_amount=40_000, is_essential=True),
    ],
    planning_horizon=30,
)

print(f"CEFR: {result.cefr:.2f} - {result.get_interpretation()}")

Quick Simulation¶

from fundedness import run_simulation

results = run_simulation(
    initial_portfolio=1_000_000,
    annual_spending=40_000,
    years=30,
)

print(f"Success Rate: {results.success_rate:.1%}")

Quick Visualization¶

from fundedness import run_simulation
from fundedness.viz import plot_simulation_fan_chart

results = run_simulation(
    initial_portfolio=1_000_000,
    annual_spending=40_000,
    years=30,
)

fig = plot_simulation_fan_chart(results)
fig.show()