Execution benchmark: $4.68 slippage
Phoenix AI published a V4 TWAP engine benchmark showing an average slippage of $4.68 on ~15 BTC trades using predictive deep learning on tick data—concrete numbers for ML-driven execution. Those results offer a rare, replicable slippage datapoint to compare algorithmic execution approaches in backtests and trading projects. (x.com)
Phoenix’s technical dossier includes an “Implementations of Dynamic TWAP” whitepaper that compares reinforcement-learning and predictive deep‑learning execution architectures for TWAP-style orders. (alphanet.phoenix.global) That whitepaper explicitly contrasts RL/PO/DDPG approaches with the classical Almgren‑Chriss control framework and reports simulated execution-cost reductions on the order of ~10–15% versus baseline models in their backtests. (alphanet.phoenix.global) Phoenix’s product pages and community announcements describe an “AI‑enhanced order execution” stack that targets measurable TCA improvements and, in marketing material, cites the potential for negative slippage and single‑digit to multi‑tens of basis‑point savings depending on asset liquidity. (phoenix.global) Transaction‑cost analysis practitioners recommend a clear, auditable arrival‑price definition when reporting slippage and note that using a consolidated top‑of‑book mid over a short interval (e.g., 1 second) materially changes implementation‑shortfall calculations. (talos.com) Industry execution guides stress that reproducible backtests require consolidated venue aggregation (a “global” TWAP), tick‑level timestamps, and a consistent mid‑quote construction; open tools and repos for consolidated crypto execution sims exist to support that workflow. (blog.amberdata.io) Independent TCA and exchange whitepapers emphasize the need for robust sample sizes and per‑regime analysis when validating execution claims, so a rigorous replication would re‑run models across hundreds of tick‑level fills and multiple liquidity regimes to estimate confidence intervals and peer comparisons. (talos.com)
