Ask any systematic trader what their biggest hidden cost is, and slippage barely gets a mention — until they audit six months of fills and feel physically ill. Everyone obsesses over signal accuracy while the execution layer quietly eats the alpha. Routing fill quality data back into signal evaluation is one of the most underrated edges in algorithmic trading, and it's harder to build than it sounds.

The direct answer is this: slippage isn't just a transaction cost to shrug at — it's information. Every fill that lands worse than your model expected is your execution environment telling you something about signal timing, liquidity conditions, or order routing logic that deserves to be heard. Ignoring it is like having a conversation where someone keeps correcting you and you just talk louder.

CONCEPTFill quality data is signal feedback in disguise — treat every slipped fill as a data point, not just a cost.
WARNINGBacktests that assume zero slippage or fixed-cost models will overstate strategy performance — sometimes dramatically.
KEY IDEAA feedback loop between your execution layer and signal engine lets your system learn from its own market impact.

The mechanics work like this. When a signal fires and an order is sent, three numbers matter: the signal price (what the model saw), the decision price (when the order was submitted), and the fill price (what actually happened). The gap between signal price and fill price is total slippage. Decomposing that into timing slippage versus market-impact slippage is where the real diagnostic power lives.

0 5 10 15 Slippage (bps) Timing Mkt Impact Total Slippage Decomposition Timing Mkt Impact Total

Once you can decompose slippage reliably, the feedback loop becomes practical. High timing slippage on a signal consistently means you're acting too slowly — maybe the signal generation lag is killing you in fast markets. High market-impact slippage on larger orders means your position sizing isn't accounting for liquidity depth. Both are inputs your signal evaluation engine can use to re-weight or filter signals by market condition. Academic market microstructure research formalises exactly this — the relationship between order flow and price impact is well-documented and quantifiable. Traders wanting to understand the cost framework should also review what slippage means in a formal execution context, and the broader concept of algorithmic trading infrastructure explains how modern systems handle execution feedback architecturally.

The practical takeaway is simple: build a fill log that captures signal price, order submission timestamp, and actual fill price on every trade. Even a spreadsheet version beats nothing. Once you have 50 to 100 trades of clean data, run the decomposition — you'll almost certainly find one slippage type dominating, and that tells you exactly where to fix your system first.

Your signals are only as good as your fills. If the execution layer is a black box, you're flying blind with a very expensive autopilot.

This content is for educational purposes only and does not constitute financial product advice. Past performance is not indicative of future results. Profit Logic Ltd (ACN 688 669 936) accepts no responsibility for errors or omissions in this content or anywhere on this website. Always seek advice from a licensed financial adviser before making investment decisions.