Mechanism
Slippage is not evenly distributed over time. It spikes during volatility, low liquidity, rollovers, and whenever the book is thin.
Stops and market orders become liquidity takers. In stress, liquidity withdraws and the next available price can be far away.
So slippage is correlated with exactly when you most need precision.
Model risk: small parameter changes that flip your results are a warning, not a feature. Robustness is a survival requirement.
- Look for wide plateaus, not sharp peaks.
- Measure drawdown shape, not just final equity.
- Assume the future will be different in the exact way that hurts.
How it kills accounts
Assume 'normal' fills → size too large → stress regime arrives → slippage jumps → losses exceed plan → margin compresses → forced exit or panic.
How it kills accounts:
- Model works on clean history.
- Regime changes and execution friction increases.
- Performance decays slowly, so you rationalize.
- You optimize parameters instead of reducing risk.
- Drawdown becomes the teacher.
Rule that survives
Model slippage as a tail, not a constant.
If you must use stops, reduce size so worst-case slippage is survivable.
Avoid entering during known slippage clusters (major data, opens, roll).
Rule that survives:
- Stress test tails and execution, not averages.
- Prefer robust plateaus over optimized peaks.
- When performance decays, reduce risk before “fixing” the model.
Example archetype
Your average slippage is 0.2 pips, so you ignore it. Then a volatility window arrives and you take 5 pips of slippage three times in a row. The problem was not the event. It was the assumption.
Tell: if small parameter tweaks flip your results, your model is fragile.
Deep dive
Deep dive
Think of slippage as a tax that becomes progressive when markets are stressed. The better you do in calm regimes, the more you are tempted to size up right before the tax rate changes.
Glossary: slippage, sequence risk, tail risk, margin.
Variants merged
This page consolidates closely-related entries into one stronger canonical reference. Retired versions now redirect here.
Variant merged: Slippage Is a Regime
Why it’s included: Variant emphasis: slippage behaves like a regime shift, not noise. It clusters during volatility expansion, quote-pulls, and thin sessions. Budget for it explicitly or it will quietly consume expectancy.
Truth line: Slippage is information about market state. Use it as a filter.
Mechanism add-on: Slippage spikes when: Liquidity is pulled (uncertainty, asymmetric information). Your size becomes meaningful relative to depth.
Failure add-on: Ignoring slippage → same strategy, same size → worse entries + worse exits → higher trade count to “make it back” → cost-dominated equity curve.
Rule add-on: Measure slippage in pips/ticks per trade and as % of planned stop distance. If slippage exceeds a threshold, switch playbook: smaller, fewer, or sit out.
Field checklist
- Stress-test the tails. The worst days define survival.
- Use variable spreads and slippage in testing.
- Prefer robust plateaus over optimized peaks.
- Look at drawdown shape, not only profit.
- If a tiny parameter change breaks the model, the model is fragile.