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Evaluate your AI investment strategy

Zero-instrumentation scoring based on the methodology described in Aldridge, Performance Evaluating AI Investment Strategies (SSRN). Paste a return series below — everything computes in your browser; nothing is uploaded.

Inputs

Comma-, space-, or newline-separated. Use decimals, e.g. 0.02 for +2%.

One number per period — e.g. daily or weekly returns of the strategy being evaluated.
Same length as strategy returns. Enables alpha and beta.
One line per period — the other options that were available at that decision point. Enables suboptimality loss and regret.
Sharpe
–
Annualized
Alpha
–
Annualized, vs. benchmark
Beta
–
Covariance-based
Max Drawdown
–
Peak-to-trough
Total Regret
–
Cumulative suboptimality loss

Equity curve

Cumulative growth of $1 invested, compounding the entered returns.

Cumulative regret

Running sum of suboptimality loss — the gap between the best available choice and the one taken, each period.

Methodology: Sharpe is the annualized mean excess return divided by its standard deviation. Alpha and beta come from a covariance-based single-factor regression against the benchmark series. Suboptimality loss at each period is the best return available (including the one chosen) minus the return actually realized — so it is always ≥ 0 — and total regret is its running sum. This is a reference implementation of the general zero-instrumentation approach; see the linked paper for the full published methodology.