Screener, Optimizer & Backtest

Which fund deserves each slot — and does the whole model hold up?

Two questions, one workflow. First pick the fund that fills each slice on evidence rather than on a star rating; then prove the resulting allocation against real history before you commit money to it.

Screener, Optimizer & Backtest

"Which fund deserves each slot?"

Keep the workflow you already trust: run Fidelity's or Schwab's fund screener, pick your categories, check ETFs and no-transaction-fee funds — because paying a fee every rebalance is how good plans die. Then bring the candidates into RangefinderInvest and let the evidence sort them.

1

Heatmap your universe

Trailing & calendar-year returns plus risk, green to red, ranked by a tunable, Sharpe-like Score.

Screener
Fund Screener heatmap across the fund universe: trailing and calendar-year returns plus risk, colored green to red
2

Rank by slice fit

Narrow to one slice — "U.S. Large Blend" — and see SPY / VOO / SPLG / SCHX scored and graded Exact / Close / Broad.

Screener — Slice fit
Screener slice-fit ranking for the U.S. Large Blend slice with scored and grade-fitted candidates
3

Swap it into the model

Confirm the swap and the model updates in place — screening and editing are one motion.

Screener — Confirm swap
The Confirm swap dialog: swap a fund into a slice with Split percent, Preferred and New name
  • An honest Score, not a star rating — blended 10Y/5Y/3Y/1Y/YTD returns over a risk-free hurdle, divided by volatility, shrunk for thin track records, penalized for expense ratio, drawdown and inconsistency. Every weight is a visible, tunable setting.
  • NTF & Morningstar data alongside — bulk-import Fidelity/Schwab fund lists so candidates carry no-transaction-fee flags, expense ratios and star ratings; filter by the broker you actually use.
  • An Optimizer for the whole model — ranks substitution candidates for every slice at once on composite return, risk and cost, with attribution that shows what each swap gains and gives up. Deterministic, so two runs are comparable.
Then prove it

Backtest any allocation like a fund factsheet

Before you commit money to a model — yours, or the one an advisor is charging you 1% for — replay it against history and read the result like a tearsheet. Compare several models at once over the honest common window:

ModelCAGRVolatilityMax DDTotal
Fidelity-Inspired 60/405.75%9.18%−14.38%+11.97%
Schwab Core Enhanced ETF · 40/605.67%6.48%−8.47%+11.80%
  • Growth of $10,000 with a synced drawdown subchart — peak → trough → recovered — plus CAGR, volatility, Sharpe, Sortino, Calmar, best/worst periods and monthly-return views.
  • Benchmark overlay — any ticker or another of your models. A target-date fund makes a natural yardstick: overlay the one you'd hold by default and see excess CAGR, beta, tracking error, information ratio and up/down capture against it.
  • An honest window — a banner states the true common data window; presets it can't cover are disabled, not silently truncated, and any extrapolation is disclosed on the chart.
Model Backtest
Model Backtest tearsheet: growth curve, synced drawdown subchart and a statistics table

Score your own universe

Load a sample and open the Screener — the ranking, the slice-fit grades and the backtest tearsheet are all populated before you enter anything of your own.

30 days, every feature, no card and no account — then read-only, never locked.