Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading
arXiv:2606.08228v1 Announce Type: cross Abstract: Algorithmic trading systems on decentralised exchanges (DEXs) reject most candidate tokens they evaluate. The counterfactual outcome of rejected candidates (what would have happened had the system entered) is rarely measured. This paper introduces Post-Rejection Follow-up Sampling (PRFS). A separate tracking subsystem samples each rejected token's price and liquidity at a configurable cadence, over a horizon of up to twenty-four hours. PRFS produces the data needed to evaluate filter precision against actual market outcomes of rejected candidat
The proliferation of algorithmic trading on DEXs has exposed a critical measurement gap for evaluating the true performance of these complex systems.
This methodology provides a robust way to analyze the unseen costs and missed opportunities of algorithmic trading, leading to more efficient and profitable decentralised finance strategies.
Algorithmic trading performance can now be evaluated with a much higher degree of accuracy by accounting for the counterfactual outcomes of rejected trades, shifting from a success-oriented bias to a holistic view.
- · DEX algorithmic traders
- · Quantitative finance researchers
- · Decentralised exchanges
- · Inefficient algorithmic trading strategies
- · Arbitrageurs relying on less sophisticated models
Improved algorithmic trading system performance metrics and decision-making on DEXs.
Increased capital efficiency and reduced slippage in decentralised finance markets as algorithms become more precise.
Potential for new financial products and services that leverage superior counterfactual outcome analysis in DEX environments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG