Treynor Ratio Calculator

Upload price or return series, pick frequency, and estimate beta on excess returns. See rolling Treynor and regression diagnostics, compare asset versus benchmark, and export clean CSV or a polished PDF report. Educational tool, mobile friendly, fast, and ready to embed across your calculator network. Includes presets, manual beta mode, and annualization control options.

Inputs

Data mode

Drop CSV (or click) — columns: date, asset_price, bench_price or date, asset_ret, bench_ret. Optional: rf.

Frequency

Return type

Risk-free (annual, %)

Periods/year

Beta mode

Manual β

Rolling window

Annualize

Tip: For series mode, if your CSV has rf per-period, it will override the annual Rf setting.

Formula: Treynor = (Return − Rf) / β. Uses excess-return OLS for β when estimating.

Treynor

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Beta (β)

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Coverage

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Summary

Metric	Value

Rolling Treynor

Excess Return vs Benchmark (Regression)

Cumulative Growth (Asset vs Benchmark)

Diagnostics

About this calculator

The Treynor ratio measures a portfolio’s return per unit of systematic risk. It divides the portfolio’s excess return by its beta to a chosen benchmark. In symbols: Treynor = (R_p − R_f) / β_p. R_p is the portfolio return over the period, R_f the risk‑free return for the same period, and β_p the slope from regressing the portfolio’s excess returns on the benchmark’s excess returns. A higher value means the portfolio delivered more excess return for each unit of market risk taken.

How the tool works

Data ingestion. Upload prices (we compute returns) or pre‑computed returns for the asset and benchmark. Optionally include a per‑period risk‑free column; otherwise we convert your annual risk‑free rate to the selected frequency.

Alignment and transformation. The app aligns dates, applies the chosen frequency (daily, weekly, or monthly) and return type (simple or log), and constructs excess returns by subtracting R_f from both series.

Beta estimation. Using ordinary least squares on excess returns, we estimate α and β with R² and the standard error of β. You can override β manually when needed.

Treynor computation. Per‑period Treynor equals mean excess return divided by β. If annualization is enabled, we transform average asset, benchmark, and risk‑free returns to annual rates and apply the same formula.

Rolling analytics. A rolling window tracks how β and Treynor evolve through time, revealing regime shifts or drift in market exposure.

Interpreting results

Positive and large Treynor indicates attractive reward per unit of systematic risk.

β near zero makes Treynor unstable or undefined; the tool flags this condition.

Negative β can flip the interpretation; a positive Treynor may reflect gains while the market falls.

When to use Treynor vs alternatives

Treynor focuses on systematic risk (β) and is most informative when the portfolio sits inside a diversified program. If you want reward per total volatility, prefer Sharpe. If downside risk matters more, consider Sortino, which penalizes only negative deviations.

Inputs and settings (quick reference)

Setting	Purpose
Frequency	Aggregate returns to daily, weekly, or monthly before analysis.
Return type	Choose simple or log; charts and ratios honor this choice.
Risk‑free	Provide annual R_f or per‑period series; we align to your frequency.
Beta mode	Estimate via OLS on excess returns or enter a manual β.
Rolling window	Controls the lookback for rolling Treynor and β curves.
Annualize	Switch between per‑period and annualized reporting.

Metric comparison

Metric	Risk denominator	Formula (conceptual)	Best for
Treynor	Systematic risk (β)	(R_p − R_f) / β_p	Diversified programs benchmarked to a market index
Sharpe	Total volatility (σ)	(R_p − R_f) / σ_p	Standalone funds where total risk matters
Sortino	Downside volatility (σ_down)	(R_p − MAR) / σ_down	Downside‑sensitive mandates

Limitations and good practice

Backtests and ratios depend on data quality. Keep benchmarks consistent, remove bad ticks, and avoid mixing frequencies. Check β’s standard error and sample size for reliability. Always corroborate with strategy logic, trading costs, taxes, and implementation constraints. Validate results with independent calculations when possible.