Calculator
Risk-Adjusted Returns Calculator
Returns CSV → Sharpe, Sortino, Calmar, Omega, alpha, beta, tracking error, information ratio, max drawdown, tail moments.
Runs in your browser. Nothing you enter is uploaded, and no account or API key is needed.
1. Upload a returns CSV
Columns: date,strategy,benchmark (benchmark optional). Returns are simple daily returns as decimals (0.012 = 1.2%); annualization uses 252 days. Computation is entirely client-side; nothing is uploaded.
Sharpe ratio (annualized)
0.531
Marginal — verify with OOS, 4.8% return on 5.3% vol over ~2.0 yrs.
Excess of 2.00% annual risk-free, 504 observations.
Other ratios
Sortino (ann)
0.786
Downside-only vol
Calmar
1.013
CAGR ÷ max drawdown
Omega
1.09
gains / losses
Drawdown + distribution
Max drawdown
4.74%
Skewness
0.112
< 0: fat left tail
Excess kurtosis
-0.227
> 0: fat tails
Benchmark-relative
Beta
0.049
Alpha (CAPM, ann)
2.82%
Tracking error
6.32%
Information ratio
0.102
Formulas
sharpe_ann = (mean(r) - rf_daily) / stdev(r) × √252 sortino_ann = (mean(r) - rf_daily) / downside_stdev × √252 omega = sum(positive excess) / sum(|negative excess|) ann_return = (Π(1 + r))^(252 / n) - 1 (gross CAGR, not excess of rf) calmar = ann_return / max_drawdown maxDD = max over t of (peak_t - nav_t) / peak_t With benchmark: beta = cov(r, b) / var(b) alpha_ann = (1 + mean(r))^252 - 1 - rf_ann - β · ((1 + mean(b))^252 - 1 - rf_ann) tracking_err = stdev(r - b) × √252 info_ratio = mean(r - b) × 252 / tracking_err
How to use it
- Upload a CSV with date, strategy and an optional benchmark column of simple daily returns as decimals. A two-year demo loads by default.
- Set the annual risk-free rate. Sharpe, Sortino and Omega use returns in excess of it.
- Read the annualized Sharpe ratio with the compound annual return and volatility.
- Check Sortino, Calmar (CAGR ÷ max drawdown), Omega, max drawdown, skewness and excess kurtosis.
- With a benchmark column, read beta, CAPM alpha, tracking error and the annualized information ratio.
Questions people ask
Which risk-adjusted metrics does the tool compute?
Annualized Sharpe and Sortino on returns in excess of your risk-free rate, Omega at the risk-free threshold, Calmar (compound annual return ÷ max drawdown), max drawdown, skewness and excess kurtosis; with a benchmark column also beta, CAPM alpha, tracking error and the annualized information ratio. Treynor is not reported, but it follows from the excess return and the beta shown.
When is Sortino better than Sharpe?
When the strategy has asymmetric returns. Sortino penalizes downside volatility only; Sharpe penalizes both up and down volatility equally. For long-only equity, Sharpe and Sortino track closely. For strategies with positive skew (trend-following, options buying), Sortino is more representative.
What benchmark should I use for Information Ratio?
Whatever benchmark you'd hold if you didn't have the strategy. For US equity strategies, SPY is typical. For multi-asset, a 60/40 stock/bond mix. For style-specific (small-cap value), a style-matched index (IWM, VTV). Information Ratio against a mismatched benchmark is meaningless.
How long a sample do I need?
Several years. For independent returns the standard error of an annualized Sharpe ratio is roughly 1 / √(years of data), so one year cannot tell a Sharpe of 1 from 0. Calmar needs at least one full drawdown and recovery. The tool reports whatever your sample gives and does not flag short samples, so read the ratios against the sample length shown.
Can risk-adjusted returns be misleading?
Yes. A strategy with rare large losses (option selling, short volatility) can show a high Sharpe in calm regimes and a disastrous one when the regime breaks. Read the skewness, excess kurtosis and max drawdown next to the ratios, and check tails with the Returns Distribution Analyzer or the VaR Backtest.
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Use it from code
The same calculation as a JavaScript module you can import. It runs where you import it, with no request, key or rate limit.
import { compute } from "https://aifinhub.io/engines/risk-adjusted-returns.js";