Playground
Execution Simulator
Square-root market impact + linear temporary impact + latency jitter. See the realistic slippage of any trade size before you route it. Free.
- Inputs
- Paste + configure
- Runtime
- 1–15 s
- Privacy
- Client-side · no upload
- API key
- Not required
- Methodology
- Open →
Simplified square-root + linear impact model from Almgren-Chriss (2000) and Kissell (2006). Real execution costs depend on regime, venue, HFT counter-trading, news, and microstructure quirks not captured here. Use this to size the order of magnitudeof your slippage, not to approve a block trade.
1 · Order & market inputs
Estimated slippage
−6.1bps
$3.05k on $5.00M notional · 50.0k buy · 1.00% of ADV · fills in 39.0 min
Permanent: 2.0 bps · Temporary: 0.1 bps · Half-spread: 4.0 bps · Latency drift: ±0.51 bps
2 · Fill schedule (participation-weighted)
Linear participation model: the order tracks a constant fraction of market volume until filled. Real VWAP/TWAP engines shape the curve (U-shape for VWAP, flat for TWAP) to match intraday liquidity.
Formulas
permanent_bps = η · σ · √(X / V) η = 0.10 temporary_bps = ε · σ · (X / V) ε = 0.05 half_spread = spread_bps / 2 total_bps = permanent + temporary + half_spread duration_min = X / (participation · V · 390 / 390) latency_drift = σ · √(latency_ms / ms_per_day) · 10_000 (bps, 1σ)
See methodology for coefficient sources, model limits, and when to stop trusting these numbers.
How to use
Step-by-step
- 1
Set the order: side, order size in shares, and the stock's average daily volume — order size relative to ADV drives the impact.
- 2
Set the market conditions: bid-ask spread (bps), daily volatility (%), and participation rate (% of volume).
- 3
Set latency (ms) and latency jitter to see the drift band the delay adds, and enter a reference price to convert basis points into dollars.
- 4
Read the cost decomposition: permanent (square-root) impact, temporary (linear) impact, and half-spread, summed to total slippage in basis points and dollars, plus expected trade duration. The output is closed-form and deterministic — same inputs, same result, no random paths.
- 5
Increase order size to find the 'capacity ceiling' where slippage exceeds expected alpha. This is your strategy's practical capacity limit.
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/execution-simulator.js";Contract: /contracts/execution-simulator.json Full agent guide →
Glossary references
Terms used by this tool
Questions people ask next
FAQ
What does the simulator model that backtests skip?
It decomposes the execution cost most backtests ignore into three parts: permanent (square-root) market impact, temporary (linear) impact, and the half-spread you cross — plus a latency-drift band. Most backtests assume instant, complete fills at the mid-quote, which overstates achievable returns. It does not model an order book, queue position, or partial fills — those are outside a closed-form impact estimate.
Where do the impact coefficients come from?
The permanent (square-root) coefficient defaults to 0.1 and the temporary (linear) coefficient to 0.05 — round numbers in the Almgren-Chriss tradition. The methodology page is explicit that they are not calibrated to any specific venue, day, or regime, so treat the output as an order-of-magnitude estimate of impact cost, not a venue-accurate fill.
Does the simulator handle venues or dark pools?
No. There is no order book, no lit-versus-dark distinction, and no venue selection — the tool is a closed-form estimate of aggregate impact cost from your order size relative to daily volume. For venue-specific fill behavior you need order-book replay, not this.
What's the impact-cost output?
Estimated price displacement caused by your order, in basis points, split into permanent impact, temporary impact, and half-spread. For a 1,000-share order in a stock with 100,000-share daily volume, impact is small. For an order that's 5%+ of daily volume, impact dominates the trade. The model is a square-root permanent + linear temporary decomposition in the Almgren-Chriss tradition; the exact form is on the methodology page.
Is the output deterministic?
Yes. The calculation is closed-form — identical inputs always produce identical output, with no Monte Carlo or random paths. What varies in real markets (arrival timing, book depth, adverse selection) is not simulated, so re-running the same inputs won't build a distribution; the tool gives a single point estimate of expected impact cost.
Related deep dive
All articles →Read further
Long-form context behind the tool output.
- Methodology · Opinion·7 min
Almgren-Chriss Optimal Execution: A Worked Example
Almgren-Chriss optimal execution on a 250k-share sell: the permanent and temporary impact decomposition, the mean-variance frontier, and a 19.40-bps cost.
Read - Tutorial · Runnable·12 min
Backtest to Paper to Live: Deployment Playbook
Backtest to paper to live — the gates that separate each stage, the metrics that trigger rollback, and the kill-switch you should already have.
Read - Methodology · Opinion·9 min
Real-Time vs End-of-Day Trading Systems
Real-time vs end-of-day trading systems — the decision rule, the 20-50x cost delta, and the four signal types where real-time is genuinely load-bearing.
Read
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