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Strategy #0000000140 · Market making and liquidity

Order anticipation and back-running scenarios
Enter hypothetical impact bp, captured fraction, reversal, execution/risk costs and notional.

Large institutional orders may be split over time, and temporary price impact and liquidity demand affect execution costs. Research studies high-frequency trading around large orders, but trading ahead on confidential order information or manipulating markets can violate rules. This page models hypothetical price-impact economics only; it gives no method for detecting, front-running or back-running orders.

Beginner-friendlyAbout 8 minutesEvidence and practical analysis

Three ways in

Understand the story. Then inspect the risk.

Start with a familiar situation, check the payoff, then test how the numbers change.

The idea · in two minutes

If part of a hypothetical price impact is captured, what remains after reversal, execution and risk costs?

Large institutional orders may be split over time, and temporary price impact and liquidity demand affect execution costs. Research studies high-frequency trading around large orders, but trading ahead on confidential order information or manipulating markets can violate rules. This page models hypothetical price-impact economics only; it gives no method for detecting, front-running or back-running orders.

Enter hypothetical impact bp, captured fraction, reversal, execution/risk costs and notional.

No order flow or live microstructure is loaded; the tool does not teach trading on undisclosed orders.

  • Input assumptionsEnter hypothetical impact bp, captured fraction, reversal, execution/risk costs and notional.
  • Model outputNo order flow or live microstructure is loaded; the tool does not teach trading on undisclosed orders.

Illustrative research examples only. They are not live signals or a recommendation.

Variants · same family, different risk levers

Models, contract terms and implementation can change results.

Compare relevant mechanisms and execution assumptions.

Related strategyRelationshipWhat changesNew cost or risk
Base scenarioManual inputsLarge institutional orders may be split over time, and temporary price impact and liquidity demand affect execution costs. Research studies high-frequency trading around large orders, but trading ahead on confidential order information or manipulating markets can violate rules. This page models hypothetical price-impact economics only; it gives no method for detecting, front-running or back-running orders.No order flow or live microstructure is loaded; the tool does not teach trading on undisclosed orders.
Conservative versionHigher cost/lossStress higher costs, larger losses or lower liquidity.May lower scenario returns.
Validation versionData reviewTest model inputs with consistent contracts and timestamps.Needs licensed, representative data.

No order flow or live microstructure is loaded; the tool does not teach trading on undisclosed orders.

Read beyond the summary

References

Primary sources and research used to describe the strategy and its limits.

  1. 01van Kervel & Menkveld (2019), High-Frequency Trading around Large Institutional Ordersresearch