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. Free.
- Inputs
- Form inputs / CSV
- Runtime
- Instant
- Privacy
- Client-side · no upload
- API key
- Not required
- Methodology
- Open →
1 · Upload a returns CSV
Long format: date,strategy,benchmark (benchmark optional). Returns interpreted as simple daily returns. Annualization uses √252. Computation is entirely client-side; nothing is uploaded.
What this tool computes
Standard risk-adjusted performance metrics for a daily returns series. Load the synthetic demo for a walk-through or upload your own strategy + optional benchmark columns. Everything runs in your browser; nothing is uploaded.
How to use
Step-by-step
- 1
Upload your strategy's return series and (optionally) a benchmark return series for IR computation.
- 2
Read all five metrics: Sharpe, Sortino, Calmar, Information Ratio, Treynor.
- 3
Sortino > Sharpe materially → strategy has positive skew (upside-volatile, not symmetric).
- 4
Information Ratio with mismatched benchmark is meaningless — pick a benchmark you'd hold without the strategy.
- 5
Pair with the Returns Distribution Analyzer or VaR Backtest for tail-risk validation. Risk-adjusted returns alone can hide tail risk.
For agents
Use in an agent
Same math, same result shape as the UI above — as a static ES module. No HTTP request, no auth, no rate limit.
import { compute } from "https://aifinhub.io/engines/risk-adjusted-returns.js"; Contract: /contracts/risk-adjusted-returns.json Full agent guide →
Glossary references
Terms used by this tool
Questions people ask next
FAQ
Which risk-adjusted metrics does the tool compute?
Five from the methodology page: Sharpe (return per total volatility), Sortino (return per downside volatility), Calmar (return per max drawdown), Information Ratio (excess return per tracking-error vs. benchmark), and Treynor (return per beta). All annualized.
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?
At least 36 monthly observations for Sharpe to be statistically meaningful (2 sigma SE). For Calmar, you need at least one full drawdown-recovery cycle (typically 3-7 years). Below those thresholds, the metrics are descriptive but not robust. The tool flags low-N estimates.
Can risk-adjusted returns be misleading?
Yes. A strategy with rare large losses (option selling, short-vol) can show high Sharpe in calm regimes and catastrophic Sharpe when the regime breaks. The methodology page warns about this and recommends pairing risk-adjusted metrics with explicit tail-risk measures (VaR, CVaR, drawdown duration).
Related deep dive
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Long-form context behind the tool output.
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Used in
Decision workflows that use this tool
Goal-driven flows that bundle this tool with adjacent ones.
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Users of this tool often explore
Returns Distribution Analyzer
Paste a returns CSV. Histogram, normal-overlay, QQ plot, skewness, excess kurtosis, Jarque-Bera test, tail-weight index. See why Sharpe alone misleads.
Backtest Overfitting Score
Upload a backtest trade log and compute Probability of Backtest Overfitting (PBO), Deflated Sharpe Ratio, and the odds your edge survives live trading.
Correlation Matrix Visualizer
Paste a multi-asset returns CSV. See the Pearson correlation heatmap, condition number, average absolute correlation, and eigenvalue concentration.