How to Backtest with Realistic Slippage

Charge the spread on every fill

The most basic and unavoidable cost is the bid-ask spread: a buy fills near the ask, a sell near the bid, so a round trip pays roughly the full spread before any impact. Backtests that fill at the mid price quietly hand you half the spread for free on every trade, which compounds with frequency. Start by charging at least the half-spread per side, using a spread estimate appropriate to the instrument and time of day.

Spreads widen at the open, the close, and during news. A flat average spread understates cost for strategies that trade in exactly those windows.

Use the Execution Simulator: Estimate execution cost in closed form — square-root permanent impact, linear temporary impact, half-spread, and a latency-drift band. See the slippage a naive backtest hides.

Add temporary market impact

When you take liquidity, your own order pushes the price against you, and this temporary impact grows with how aggressively you trade relative to available volume. A common model makes temporary impact scale linearly with your participation rate. The larger your order relative to the volume in the window, the worse the average fill. This is the cost that turns a strategy that works on paper at small size into one that cannot scale.

Model impact as a function of size, not a flat fee. A fixed cost per trade hides exactly the size-dependence that limits capacity.

Account for permanent impact

Beyond the temporary push that decays after you stop trading, large orders also move the price permanently by revealing information to the market. Permanent impact commonly scales with the square root of order size relative to daily volume. It does not decay, so it raises the cost basis of the whole position. For institutional-size strategies this term dominates and is the main reason capacity is finite.

Square-root permanent impact means doubling your size does not double the cost per share, but it does keep raising it. There is no size at which impact stops mattering.

Model latency between signal and fill

A signal computed at one price does not fill at that price; time passes, and the market moves. The faster your strategy and the more it competes on speed, the more this latency cost matters, because the edge you saw may be gone by the time you act. Add a realistic delay between signal and execution in the backtest and fill at the price that prevailed then, not the price that triggered the signal.

If your edge disappears once you add a realistic latency delay, it was a latency-arbitrage edge you cannot capture without the infrastructure to win the race.

Model partial fills and queue position

Resting limit orders do not always fill, and when they do you wait in a queue behind earlier orders at the same price. A backtest that assumes every limit order fills completely and instantly overstates a maker strategy badly. Model the probability of a fill given your queue position and the volume that traded at your price, and treat unfilled orders as missed trades rather than free ones.

Maker strategies live or die on fill probability. Assuming full fills turns adverse selection invisible: you get filled exactly when the price is about to move against you.

Use the Order Book Replay Visualizer: Drop a Level-2 snapshot CSV and replay the book snapshot by snapshot. Animated depth bars, best bid/ask, spread and imbalance. Understand microstructure before.

Find the capacity where the edge breaks

Put the cost components together and re-run the backtest at increasing sizes to see where the net edge goes to zero. This capacity analysis is the bottom line: it tells you the maximum size at which the strategy is still worth trading after realistic costs. An edge that exists only below a size you cannot reach is not deployable, and capacity, not raw Sharpe, is what determines how much capital a strategy can actually carry.

Report the strategy's capacity in dollars, not just its Sharpe. A high Sharpe at trivial size is a research curiosity, not a business.

Use the Statistical Arbitrage Capacity Calculator: Maximum strategy AUM from alpha, slippage, fees, daily volume and volatility, and signal half-life. Square-root impact closed-form.