Below is a concrete, “repo-ready” spec for (1) an open-source Python package that implements **CEFR** + **Victor-style (lifetime utility) financial health**, and (2) a **Streamlit calculator** with visuals that make it *feel* obvious why net worth is insufficient.

---

## 1) Package concept

**Name (suggestions):**

* `cefr` (simple, maybe taken)
* `fundedness`
* `elmish` (probably avoid trademark vibes)
* `lifefunded`
* `cefrkit`

**Tagline**

> Quantify “how financially secure am I?” with after-tax, liquidity, and risk adjustments + lifetime utility planning.

**Core outputs**

1. **CEFR**:
   [
   \text{CEFR}=\frac{\text{certainty-equivalent, after-tax, liquid resources}}{PV(\text{liabilities})}
   ]
2. **Time / Runway distribution** (P10/P50/P90) under uncertainty + sequence risk.
3. **Victor-style policies**: spending + risky share over time (or over “funding state”) that maximize expected lifetime utility subject to a subsistence floor.
4. **Stress tests & sensitivity**: what breaks first (taxes, concentration, illiquidity, liabilities).

**Important positioning**

* This is *not* “your retirement number.” It’s a toolkit for computing **fundedness and robustness** under explicit assumptions.

---

## 2) Scope tiers (so it’s shippable)

### MVP (v0.1–0.3): “CEFR + Monte Carlo runway”

* CEFR with transparent haircuts + decomposition
* Liability PV calculator
* Monte Carlo with:

  * correlated asset returns
  * inflation
  * spending shocks (optional)
  * “time-to-floor” + “time-to-ruin” + “max drawdown in spending”
* Streamlit app with compelling visuals

### v0.4–0.7: “Victor-style utility engine (approximate)”

* CRRA utility + subsistence floor
* Solve for spending & risky share **via simulation + optimization** (policy search)

  * (Start with simple parametric rules; don’t jump straight to full dynamic programming unless you want a research project.)
* Produce **policy curves** and fan charts of spending/wealth

### v1.0+: “Tax lots + account flows”

* Account types (taxable, traditional, Roth)
* withdrawals ordering, cap gains, RMD-ish modeling
* Roth conversions as optional action
* richer liability schedules

---

## 3) Package architecture

### Top-level modules

```
fundedness/
  __init__.py
  cefr.py
  liabilities.py
  taxes.py
  liquidity.py
  risk.py
  markets.py
  simulate.py
  utility.py
  policies.py
  optimize.py
  reporting.py
  viz.py
  cli.py
  data/ (optional: sample assumptions)
tests/
docs/
examples/
streamlit_app/
```

### Key data models (use `pydantic` for validation)

* `Household`: age(s), horizon, survival model config, income streams
* `BalanceSheet`: list of `Asset` + list of `Liability`
* `Asset`: value, type, account_type, concentration, liquidity_class, tax_treatment
* `Liability`: amount schedule (date/value), type, inflation linkage, priority
* `MarketModel`: expected returns, covariance, fat-tail option, inflation model
* `TaxModel`: effective rates by account, cap gains assumptions, state/fed
* `UtilityModel`: CRRA gamma, subsistence floor, discount rate, bequest weight
* `SimulationConfig`: n_paths, steps_per_year, seed, rebalancing rules

---

## 4) CEFR spec (functions + methodology)

### CEFR calculation

**Inputs**

* Assets with:

  * `value`
  * `tax_rate_effective` (or derived from TaxModel)
  * `liquidity_factor` λ in [0,1]
  * `reliability_factor` ρ in [0,1] (risk & concentration haircut)
* Liabilities schedule (or PV directly)

**Output**

* `cefr_ratio`
* `numerator_breakdown`: raw assets → after-tax → liquid → reliability
* `denominator_breakdown`: PV by bucket (debt, near-term, long-term)

### API sketch

```python
from fundedness import cefr, BalanceSheet, TaxModel, HaircutModel

result = cefr.compute(
    balance_sheet=bs,
    tax_model=tax_model,
    haircut_model=HaircutModel(
        liquidity_map={"cash":1.0, "taxable_index":0.95, "home_equity":0.5},
        reliability_map={"diversified":0.9, "single_stock":0.6, "startup":0.3},
    ),
    discount_rate_real=0.02,
)
print(result.ratio, result.breakdown)
```

### Reliability factor (ρ) options (choose one for MVP)

1. **Stress haircut** (simple, intuitive): apply a drawdown haircut by asset class

   * e.g., equities “count” at 70–85% depending on user preference
2. **CVaR / tail-based** (more quant):

   * compute one-year 5% CVaR and map to a discount
3. **Concentration penalty**:

   * Herfindahl index or “max position %” penalty on equity bucket

MVP: start with (1) + simple concentration penalty.

---

## 5) “Victor-style” engine spec (utility + optimal policy)

This is the part that makes it feel different from “4% rule calculators.”

### State & decisions

At each period (t):

* State: wealth (W_t), age, inflation state, maybe “floor funding status”
* Decisions:

  * spending (C_t)
  * risky share (a_t)

### Utility function (CRRA with subsistence floor)

One robust formulation:

* Define floor (F) (real dollars/year). Enforce (C_t \ge F) or penalize below floor strongly.
* Utility:
  [
  u(C) = \frac{(C - F)^{1-\gamma}}{1-\gamma}
  ]
  with (C>F). If (C\le F), assign a large negative penalty (or use a smooth approximation).

### Solving approach (practical open-source path)

Full dynamic programming is doable, but heavy. A very workable approach:

**Step 1 (v0.4): parametric policy search**

* Assume spending rule: (C_t = \alpha \cdot W_t) with guardrails + floor
* Assume risky share rule: (a_t = \sigma(\beta_0 + \beta_1 \cdot \log(W_t / PV(Floor))))
* Optimize parameters (\theta=(\alpha,\beta_0,\beta_1,\dots)) by Monte Carlo maximizing expected discounted utility.

This gives you:

* “Victor-like” adaptive spending & risk that respond to fundedness
* policy curves you can plot
* clean confidence bands

**Step 2 (v0.7+): approximate dynamic programming**

* Fit a value function (V(W,t)) with regression (or neural net)
* Improve policy iteratively (policy iteration)

### API sketch

```python
from fundedness import solve_policy, UtilityModel, MarketModel

policy = solve_policy(
    utility=UtilityModel(gamma=4, floor_real=60000, discount=0.01),
    market=market_model,
    horizon_years=60,
    method="parametric_policy_search",
    n_paths=50_000,
)
# policy provides functions spend(W, age), risky_share(W, age)
```

---

## 6) Simulation engine spec (confidence intervals on “time”)

**Key idea:** “Confidence intervals” here are **scenario percentiles** conditional on assumptions. Report P10/P50/P90.

### Definitions of “time”

Let the user choose:

* **Time-to-floor breach**: first year (C_t < F)
* **Time-to-ruin**: first year liquid wealth < 0 (or total wealth < 0)
* **Time-to-X% cut**: first year (C_t) falls > X% from baseline

### Outputs

* `time_p10, time_p50, time_p90`
* survival curve: (P(\text{still above floor at } t))
* distribution plot (hist / KDE)
* fan chart of wealth and spending

---

## 7) Streamlit app spec (pages + killer visuals)

### App structure (multi-page)

1. **Inputs**

   * Assets (table editor): value, type, account, liquidity class, concentration flag
   * Liabilities (table editor): debt + future goals (college, retirement floor)
   * Assumptions:

     * expected returns / vol / correlation (simple sliders + presets)
     * inflation mean/vol
     * discount rate
     * tax rates (simple first)
     * subsistence floor
     * risk aversion γ
2. **CEFR Dashboard**
3. **Time / Runway**
4. **Victor-style Policy & Outcomes**
5. **Sensitivity**
6. **Compare Scenarios** (save/load configs)

### Compelling visuals (Plotly recommended)

**CEFR Waterfall (must-have)**

* Raw assets → after-tax → liquidity haircut → reliability haircut = numerator
* Adjacent: liability PV stack by category
* Then big CEFR number + interpretation band

**“Same net worth, different CEFR” compare card**

* Let user create Scenario A/B and show side-by-side waterfalls

**Runway fan chart**

* Spending and wealth over time with percentile bands (P10–P90)

**Survival curve**

* Probability of staying above floor vs time (very intuitive)

**Tornado sensitivity chart**

* Which assumption moves CEFR most (tax rate, liquidity haircut, equity drawdown haircut, liability PV discount rate)

**Policy curves**

* “Optimal” risky share vs fundedness
* “Optimal” spending rate vs fundedness
* Show guardrails (floor, max cut, etc.)

**Stress test tiles**

* “2008-style drawdown year 1”
* “Lost job for 12 months”
* “Inflation spike”
  Each tile shows CEFR impact + runway P50 change

---

## 8) CLI + notebooks (developer ergonomics)

### CLI commands

* `fundedness cefr config.yaml`
* `fundedness simulate config.yaml --out results.parquet`
* `fundedness policy-search config.yaml`

### Example notebooks

* `examples/01_cefr_basics.ipynb`
* `examples/02_time_distribution.ipynb`
* `examples/03_policy_search.ipynb`
* `examples/04_sensitivity.ipynb`

---

## 9) Testing & quality bar

* Unit tests:

  * PV math (liability discounting)
  * CEFR decomposition sums correctly
  * simulation reproducibility (seeded)
* Property tests (optional): CEFR monotonicity (more assets ↑, more liabilities ↓)
* Benchmarks:

  * numba acceleration optional for Monte Carlo

**Docs**

* MkDocs site:

  * “What CEFR is / isn’t”
  * “What ‘confidence intervals’ mean here”
  * “Assumptions cookbook”
  * “Model risk / limitations”

**License**

* MIT or Apache-2.0 (Apache if you want more explicit patent protections)

---

## 10) Suggested MVP defaults (so the app is usable instantly)

**Preset assumptions**

* “Conservative”, “Base”, “Optimistic” market presets
* Liquidity map defaults:

  * cash 1.0
  * taxable diversified 0.95
  * retirement accounts 0.85 (penalty for access + taxes)
  * home equity 0.5
  * private business 0.3
* Reliability map defaults:

  * diversified bonds/cash 0.95
  * diversified equity 0.85
  * single stock 0.60
  * startup 0.30

(Users can override; the point is immediate intuition.)

---

## 11) Repo deliverables checklist

### Python package

* [ ] `cefr.compute()` + breakdown objects
* [ ] `liabilities.pv()` with schedules
* [ ] `simulate.run()` returning percentiles + time-to-event
* [ ] `policy_search.solve()` (v0.4)
* [ ] plotting helpers (optional, but Streamlit will handle most)

### Streamlit app

* [ ] input forms + editable tables
* [ ] CEFR waterfall + compare view
* [ ] runway distribution + survival curve
* [ ] scenario save/load (JSON)
* [ ] export report (HTML/PDF later)

---

If you want, I can take this one step further and draft:

* a full `README.md` (with screenshots plan + examples),
* the exact `pydantic` schemas,
* and a skeleton repo tree + “first working” Streamlit app (CEFR waterfall + runway percentiles) you can paste into your GitHub.


----


Absolutely — this is a great “comparison layer” to bake into the package/app, because it lets users see *why* CEFR + Victor-style planning diverge from the classic 3–4% framing.

## Package additions: “Withdrawal Strategy Lab”

### New module: `withdrawals/`

Create a first-class abstraction for withdrawal rules so you can run **apples-to-apples sims** across:

* fixed real 3–4% baseline
* guardrails
* VPW
* RMD-style
* tax-aware sequencing variants
* floor/flex + bucket/TIPS ladder overlays

**Core interface**

```python
class WithdrawalPolicy(Protocol):
    def initialize(self, context: InitContext) -> None: ...
    def decide(self, state: SimState) -> WithdrawalDecision: ...
```

**State inputs** should include at least:

* current portfolio value by account (taxable / pre-tax / Roth)
* last year spending, inflation, and portfolio return
* floor & flex targets (if enabled)
* age, horizon, benefit start dates (optional)
* tax state (realized gains, ordinary income, bracket target)

**Decision output**

* total spending this year (real $)
* how spending is split (floor vs flex)
* withdrawals by account + optional conversions
* optional “raise/cut” flag for reporting

---

## 1) Baseline: classic fixed real 3–4% rule

### `FixedRealSWRPolicy`

* **Year 1**: spend = `initial_rate * initial_portfolio`
* **Future years**: spending grows with inflation (fixed real)
* Optional toggles:

  * `inflation_cap` (e.g., 0–3%)
  * `skip_inflation_if_negative_return=True`
  * `horizon_years` explicit (30 vs 60)

**Why include it:** it’s the benchmark everyone recognizes, and it highlights sequence-of-returns risk + tax blind spots.

---

## 2) Floor vs Flex spending (overlay, not a single policy)

### `FloorFlexOverlay`

This wraps any base policy and splits spending into:

* **Floor** (non-negotiable essentials)
* **Flex** (adjustable)

Mechanics:

* enforce floor first (or penalize breaches)
* apply cuts/bonuses primarily to flex
* report “floor breach events” as a key risk metric

This overlay is where CEFR and Victor-style planning connect cleanly:

* floor resembles *subsistence* in utility
* flex is where adaptive strategies shine

---

## 3) Guardrails (Guyton–Klinger family)

### `GuardrailsPolicy` (Guyton–Klinger style)

Inputs:

* `initial_rate` (e.g., 3.5–4.0%)
* guardrails like ±20% around the initial withdrawal rate
* `cut_pct` and `raise_pct` (e.g., 10%)
* inflation handling:

  * `skip_inflation_after_down_year`
  * `inflation_cap`

Each year:

1. compute **current withdrawal rate** = planned spending ÷ current portfolio
2. if above upper guardrail → cut spending (preferably flex)
3. if below lower guardrail → allow raise/bonus
4. apply inflation rule (skip/cap)

Include a doc note that this is associated with **Jonathan Guyton** and **William Klinger**.

---

## 4) VPW (Variable Percentage Withdrawal)

### `VPWPolicy`

* Spend = `age_based_pct(age, risk_level) * current_portfolio`
* Optionally smooth with:

  * trailing average portfolio (e.g., 3-year average)
  * floor/flex overlay so essentials don’t bounce as much

This is ideal for demonstrating “you won’t ‘run out’ in the same way, but spending is variable.”

---

## 5) RMD-style spending (pre-RMD too)

### `RMDStylePolicy`

* Spend = `current_portfolio / divisor(age)`
* Divisors can be:

  * a provided table (user-supplied)
  * a simple parametric approximation

Note: actual Required Minimum Distribution tables relate to **Internal Revenue Service**, but your method is “RMD-style” (spirit, not tax advice).

---

## 6) Horizon and glidepath support

### New module: `allocation/`

Add a standard interface for equity/bond glidepaths so withdrawal policies and Victor-style policies can share it.

Include:

* `ConstantAllocation`
* `RisingEquityGlidepath` (sequence-risk mitigation concept)
* `BucketStrategy` (1–3 years cash/bonds for withdrawals)
* `TIPS_LadderFloor` (simple deterministic floor funding for years 1–N)

The “floor funding” instruments can be simplified to “bond ladder” for MVP; don’t overfit product realism early.

---

## 7) Tax-aware withdrawals (where 4% is silently wrong)

### New module: `tax/strategy.py`

Implement tax-aware mechanics as *separable* from withdrawal rules:

**`TaxAwareWithdrawalEngine`**

* choose withdrawals to target a marginal bracket ceiling
* decide account order dynamically (not just taxable → pre-tax → Roth)
* optional Roth conversions in low-income years
* basic capital gains realization planning (MVP-level)

Keep it explicitly “model-based planning,” not tax advice.

---

# Comparison framework: how the package evaluates strategies

## A) Standard outputs (consistent across strategies)

For each strategy, compute:

* **Time-to-floor-breach** distribution (P10/P50/P90)
* **Time-to-ruin** distribution (optional)
* **Probability of floor breach by year t** (survival curve)
* **Spending volatility** (std dev of real spending, downside semi-vol)
* **Max spending drawdown** (peak-to-trough)
* **Utility score** (if utility model enabled)
* **CEFR trajectory** over time (how “fundedness” evolves)

## B) “Fair comparison” settings

Make comparisons meaningful by standardizing:

* same market model + inflation model + fees
* same starting portfolio + account mix + tax assumptions
* same floor/flex targets
* same horizon + mortality model (optional)

---

# Streamlit app additions: “Withdrawal Strategy Lab” page

### Controls

* Choose baseline strategy:

  * Fixed real SWR (3%, 3.5%, 4%)
  * Guardrails
  * VPW
  * RMD-style
* Toggles:

  * Floor/Flex overlay (on/off + floor amount)
  * Inflation rule (cap / skip after down year)
  * Bucket strategy / ladder years
  * Tax-aware engine (on/off + bracket target)

### Killer visuals (this is where it becomes compelling)

1. **Strategy fan chart (spending)**: P10/P50/P90 real spending vs time
2. **Floor breach survival curve**: P(above floor) vs time
3. **Spending drawdown plot**: distribution of max spending cut
4. **“4% robot vs adaptive” side-by-side**: two panels, same assumptions
5. **Tax drag decomposition**: pre-tax vs after-tax spending capability over time
6. **CEFR trajectory bands**: how fundedness evolves under each policy

### Compare mode

Let users select 2–4 strategies and show:

* headline tiles: P10/P50/P90 runway, floor breach prob at 30/40/60 yrs
* spending volatility + max cut
* (optional) expected utility ranking

---

# Concrete API surface to implement

### `compare_strategies()`

```python
results = compare_strategies(
    household=household,
    balance_sheet=bs,
    market=market,
    tax_model=tax_model,
    strategies={
        "Fixed 4% real": FixedRealSWRPolicy(rate=0.04),
        "Guardrails": GuardrailsPolicy(initial_rate=0.038, band=0.20, cut=0.10),
        "VPW": VPWPolicy(risk_level="60/40"),
        "RMD-style": RMDStylePolicy(),
    },
    overlays=[FloorFlexOverlay(floor_real=60_000)],
    sim_config=SimulationConfig(n_paths=50_000, steps_per_year=12, seed=7),
)
```

### `search_initial_rate()` (optional but powerful)

For a given strategy, solve for the initial rate that hits a target:

* “≤ 5% chance of floor breach by year 40”
* “P10 runway ≥ 35 years”
  This is a clean way to contextualize “4%” without dunking on it.

---

# Docs content to ship with it

Add a docs section literally titled:

### “Why 3–4% is a starting point, not a plan”

* fixed real spending assumption
* horizon sensitivity (30 vs 60 years)
* taxes/account types
* lack of adaptation to markets
* how guardrails/VPW/RMD-style change the failure mode

…and then show one canonical example across strategies.

---

If you want the fastest “first wow” MVP: implement **FixedRealSWR vs Guardrails vs VPW**, with **Floor/Flex overlay**, and show **(1) spending fan chart + (2) floor survival curve**. That combination makes the limitations of the 4% rule obvious without needing heavy tax/account machinery on day one.



----

A better “one-number” metric is **after-tax, liquidity- and risk-adjusted *fundedness*** — basically: *how much dependable, spendable wealth you have relative to the liabilities you’re actually on the hook for.*

## Certainty-Equivalent Funded Ratio (CEFR)

**CEFR = (Spendable, risk-adjusted resources) / (Present value of liabilities)**

### 1) Numerator: *Certainty-equivalent, after-tax, liquid resources*

Start with your assets, then apply **three haircuts**:

* **After-tax haircut** (what you keep if you ultimately spend it)
* **Liquidity haircut** (how spendable it is without major friction)
* **Risk haircut** (how reliable it is under stress)

A practical approximation:

[
\text{Resources}=\sum_i \big( A_i \times (1-\tau_i) \times \lambda_i \times \rho_i \big)
]

Where:

* (A_i) = asset value
* (\tau_i) = effective tax rate on eventual spending/liquidation (e.g., retirement withdrawals, cap gains)
* (\lambda_i) = liquidity factor (cash ~1.0; taxable index funds ~0.9–1.0; home equity maybe ~0.3–0.6; private equity could be lower)
* (\rho_i) = risk reliability factor (diversified bonds/cash higher; concentrated single stock/lumpy business equity lower)

*(You can set (\rho_i) using stress-test logic: “what fraction still feels real after a bad year?”)*

### 2) Denominator: *Present value of liabilities*

Include:

* **Near-term fixed liabilities** (debt, upcoming known expenses)
* **Long-duration liabilities** (retirement spending needs, education goals, etc.), discounted to present value

[
\text{Liabilities} = \text{Debt} + PV(\text{future spending needs not covered by guaranteed income})
]

### Interpretation

* **CEFR < 1.0**: underfunded (your dependable resources don’t cover liabilities)
* **CEFR ≈ 1.0–1.5**: funded but fragile
* **CEFR > 2.0**: robust (depending on assumptions)

## Why this beats net worth

It directly bakes in what net worth ignores:

* **Taxes** (after-tax reality)
* **Liquidity** (can you actually use it when needed?)
* **Risk/volatility & concentration** (how dependable is it?)
* **Liability duration and magnitude** (what you owe *and when*)

## If you want an even simpler metric

Use a **two-number dashboard** that’s hard to game:

1. **CEFR (fundedness)**
2. **Liquidity Runway = liquid after-tax assets / monthly essential spend** (months)

Those two numbers usually separate “looks rich” from “is financially secure” fast.

If you tell me your rough asset buckets (taxable / retirement / home equity / concentrated stock / business) and major liabilities (debt + retirement spending goal), I can show you how the CEFR would be computed with reasonable default haircuts.


----


Think of **CEFR** as a *balance-sheet version of “can my resources reliably fund my obligations?”*—but with three real-world adjustments: **taxes, liquidity, and risk**.

## What CEFR “means” in plain English

* **CEFR = 1.0**: Your *certainty-equivalent* (after-tax, usable, stress-adjusted) resources roughly equal the present value of your liabilities. You’re *mathematically funded* under your assumptions, but you may not be comfortable if a bad sequence of returns hits or if cashflow is tight.
* **CEFR > 1.0**: You have a *buffer* (“margin of safety”). The bigger it is, the more you can absorb shocks: market drawdowns, job loss, medical event, higher rates, etc.
* **CEFR < 1.0**: You’re *underfunded*—you’d need higher savings, higher returns, lower spending, more guaranteed income, or some mix.

The key is: **it’s not “how rich am I?”** It’s **how robust is my funding status after reality haircuts?**

---

## Interpret it like an engineer: “margin of safety”

CEFR is essentially a **solvency ratio**:

* **Numerator** = “resources that will still feel spendable when things go wrong”
* **Denominator** = “obligations, weighted by when they hit”

So you can read it as:

* **(CEFR − 1)** ≈ your *safety margin* as a fraction of liabilities.

Example: CEFR = 1.25 ⇒ about a **25% cushion** (given your haircut assumptions).

---

## How the ratio behaves as your situation changes

### Taxes

Two people with $1M “net worth” can have wildly different CEFR:

* $1M in Roth / taxable basis-heavy index funds (low future tax) → higher numerator
* $1M in pre-tax 401(k) with a high effective withdrawal rate → lower numerator
  CEFR makes those differences explicit.

### Liquidity

Same assets, different usability:

* $800k home equity is not the same as $800k taxable brokerage.
  CEFR will *downweight* illiquid assets because they can’t fund near-term liabilities without friction.

### Risk / concentration

CEFR is sensitive to “how fragile is this wealth?”

* $1M diversified index → higher reliability factor
* $1M in one stock or one startup stake → lower reliability factor
  This captures the “my net worth is real… unless it isn’t” problem.

### Duration of liabilities

The denominator is *PV-weighted*:

* A $50k bill next month hits almost 1-for-1.
* A $50k need 25 years from now is discounted and weighs less today.

So CEFR naturally favors people who have long-dated obligations and stable funding plans.

---

## Practical interpretation bands (rule-of-thumb)

These depend on how conservative your haircuts are, but as a mental model:

* **< 0.9**: Underfunded (needs changes soon)
* **0.9 – 1.1**: Barely funded / fragile (assumption-sensitive)
* **1.1 – 1.5**: Reasonably funded (moderate shocks survivable)
* **1.5 – 2.5**: Robust (strong margin of safety)
* **> 2.5**: Very robust (more “optional” choices: risk-taking, charity, early retirement, etc.)

If you use aggressive haircuts (heavy risk + liquidity discounts), your “good” CEFR threshold will be lower; if you use optimistic haircuts, it should be higher.

---

## What CEFR does *not* tell you (important!)

CEFR is **balance-sheet solvency**, not full financial health. It doesn’t fully capture:

* **Cashflow timing** (you can be “funded” but cash-poor)
* **Sequence-of-returns risk** in retirement (unless you bake it into (\rho))
* **Human capital** (future earning power) unless you explicitly include it
* **Insurance gaps** (health/disability/life) unless treated as liabilities
* **Tail risks** (lawsuit, catastrophic health costs) unless incorporated

That’s why I like pairing CEFR with a second metric: **Liquidity Runway** (months of essential spend covered by liquid after-tax assets).

---

## A simple way to “feel” CEFR: stress test story

Imagine two households, both “$2M net worth”:

**Household A**

* $1.6M taxable index funds + $400k cash/bonds
* Low debt, stable spending
* CEFR haircuts barely reduce numerator
  **CEFR maybe ~1.8–2.2** → robust.

**Household B**

* $1.4M home equity + $600k in a single stock
* Big mortgage, college obligations soon
* Liquidity + concentration haircuts crush numerator
  **CEFR maybe ~0.9–1.2** → fragile despite same net worth.

This is exactly the intuition you’re aiming for.

---

## How to use CEFR in decision-making

* **CEFR rising over time**: you’re building resilience (even if net worth is noisy).
* **CEFR flat**: you may be accumulating assets, but liabilities/spend are rising too.
* **CEFR falling**: lifestyle creep, leverage, concentration, or new long-term commitments are outpacing resources.

If you want, give me a rough breakdown like:

* taxable / Roth / pre-tax / home equity / concentrated stock %
* debt (mortgage/other)
* big goals (retirement spend, college, etc.)
  …and I’ll show a concrete CEFR calculation and how to interpret the result under conservative vs moderate haircuts.


----

Elm’s “Victor-style” answer is basically: **don’t put a CI on “time” directly from a back-of-the-napkin runway**. Instead, **model the whole lifetime spending + investing problem**, then *derive* a distribution for “time” from the simulated paths.

Here’s the approach they describe:

### 1) Define the objective and the *floor*

They frame planning as choosing a **spending policy + investment policy over time** to maximize **Expected Lifetime Utility**, not to hit some arbitrary “success rate.” ([Advisor Perspectives][1])
A key input is your **“subsistence spending”** floor (a level you strongly prefer not to fall below). ([Advisor Perspectives][1])

### 2) Build a forward-looking stochastic model

They explicitly model (and calibrate with you):

* safe asset returns + risky portfolio risk/return assumptions ([Advisor Perspectives][1])
* longevity (single/joint) ([Advisor Perspectives][1])
* taxes + account location (taxable / traditional IRA / Roth / real estate) ([Advisor Perspectives][1])
* earned income + uncertainty + Social Security ([Advisor Perspectives][1])

### 3) Use computation + optimization (not a fixed rule)

They assume each year you choose:

1. **fraction of wealth in risky assets**
2. **fraction of wealth to spend** ([Advisor Perspectives][1])
   …and then numerically evaluate Expected Lifetime Utility and use an optimizer to improve the policies until they’re “optimal” under the assumptions. ([Advisor Perspectives][1])

(Conceptually tied to the classic result that optimal spending is a *proportional* rule driven by risk-adjusted/“certainty-equivalent” returns, time preference, and risk aversion, per Robert Merton.) ([Elm Wealth][2])

### 4) Confidence intervals = simulated bands / percentiles

Their outputs are explicitly **distributions over time** (median + “clouds” around it). They describe **1- and 2-standard-deviation ranges** around median paths for things like spending and wealth. ([Advisor Perspectives][1])

So to put “confidence intervals” on **Time**, you’d define *what counts as “survive”* and then compute it across scenarios, e.g.:

* **Time-to-floor:** first year spending falls below subsistence
* **Time-to-ruin:** first year wealth hits ~0 (or a minimum liquidity threshold)
* **Time-to-X% cut:** first year spending must drop by >25% from baseline

Then report **P10 / P50 / P90** years (or the 1–2σ bands), based on the simulated paths.

One important nuance: these are **scenario bands conditional on your assumptions**, not a frequentist “95% CI” from sampling error. Elm’s framing is: *make the assumptions explicit, simulate the uncertainty that matters, and show the distribution.*

If you want, tell me what your “survival” definition is (floor vs ruin vs max cut), and I’ll suggest the cleanest version of “time CIs” that matches Elm’s framework.

[1]: https://www.advisorperspectives.com/articles/2025/09/15/riding-wheel-fortune-practical-guide-lifetime-investing-spending "Riding the Wheel of Fortune: A Practical Guide to Lifetime Investing and Spending - Articles - Advisor Perspectives"
[2]: https://elmwealth.com/spending-like-youll-live-forever/ "Spending Like You'll Live Forever Elm Wealth"


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