Calculator
Statistical Arbitrage Capacity Calculator
Estimate maximum strategy AUM from signal half-life, daily volume, slippage, fees, and target Sharpe. Square-root impact closed-form.
- Inputs
- Form inputs / CSV
- Runtime
- Instant
- Privacy
- Client-side · no upload
- API key
- Not required
- Methodology
- Open →
Inputs
Maximum strategy AUM
$80.00B
Practical AUM ($5.00B) — the AUM at which net alpha is half of gross. Implied trades / yr: 50.
Capacity vs slippage
| Slippage | Capacity AUM | vs base |
|---|---|---|
| 0.0 bps | $151.25B | 189% |
| 1.0 bps | $125.00B | 156% |
| 2.0 bps | $101.25B | 127% |
| 3.0 bps | $80.00B | 100% |
| 5.0 bps | $45.00B | 56% |
| 8.0 bps | $11.25B | 14% |
| 12.0 bps | $0 | 0% |
| 18.0 bps | $0 | 0% |
| 25.0 bps | $0 | 0% |
Reading the result
Capacity scales linearly with daily $ volume and quadratically with the gap between gross alpha and friction. K·sqrt(participation) is the canonical square-root impact. See methodology for the derivation and references.
How to use
Step-by-step
- 1
Enter average daily traded volume of your strategy universe, signal half-life, expected per-trade slippage in bps, and current Sharpe.
- 2
Read the capacity-Sharpe curve as capital scales.
- 3
Identify the knee — where Sharpe degradation steepens. The knee is your practical capacity ceiling.
- 4
Compare projected capacity at acceptable Sharpe levels (e.g., capacity at Sharpe 1.0 vs. at Sharpe 0.7).
- 5
Re-run with stress assumptions (higher slippage, smaller universe). Capacity drops fast under stress — important for sizing institutional rollouts.
Glossary references
Terms used by this tool
Questions people ask next
FAQ
What does the tool estimate?
How much capital a stat-arb strategy can deploy before its own market impact eats the alpha. Inputs: average daily traded volume of the universe, signal half-life, expected per-trade slippage, current Sharpe. Output: capacity in dollars and the Sharpe-degradation curve as capital scales.
What's the capacity-Sharpe curve?
As capital scales, slippage per dollar increases (you're a larger share of daily volume), so net per-trade alpha decreases. Sharpe falls accordingly. The curve typically shows a sharp knee — below the knee, Sharpe is roughly flat; above it, Sharpe degrades fast. Knee location is the practical capacity ceiling.
Where does the slippage model come from?
Almgren-Chriss (2000) market impact model with parameters calibrated to the methodology page's referenced studies. Slippage scales with √(order_size / daily_volume). For aggressive (urgent) execution, slippage scales linearly. The tool defaults to the Almgren-Chriss square-root form.
Does the tool work for non-equity stat-arb?
Math works for any liquid asset class — futures, ETFs, FX. Calibration constants change. The methodology page provides defaults for equity and futures; FX defaults are documented but less validated.
Why is capacity so much lower than I'd expect?
Most retail backtests assume zero market impact. In real-world stat-arb, capacity at $10M deployed often shows Sharpe 1.5; at $100M Sharpe 0.8; at $1B Sharpe approaches zero. The capacity ceiling is precisely why institutional stat-arb funds have high barriers — the strategy doesn't scale linearly.
Related deep dive
All articles →Read further
Long-form context behind the tool output.
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