Execution Cost Savings by the Numbers: The Talos Quant Execution Insights Report 2026

By:

Eliad Hoch, Head of Quantitative Execution Services at Talos

May 27, 2026

Slippage is the hidden tax of trading, and in institutional crypto, it is one of the biggest determinants of whether a strategy’s expected alpha actually survives execution. In our 2026 Quant Execution Insights Report, my colleagues Sirui Zhang, Kaan Giray and I analyze the full lifecycle of more than 250,000 parent orders across 600+ assets traded on Talos during 2025, comparing what our algorithms actually achieved against two benchmarks: arrival price and a size-feasible naive sweep. The results show where pre-trade calibration, smart liquidity sourcing and the execution alphas we’ve written about previously translate directly into basis points saved.

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Analysis

ANALYSIS

Execution Cost Savings by the Numbers: The Talos Quant Execution Insights Report 2026

By:

Eliad Hoch, Head of Quantitative Execution Services at Talos

May 27, 2026

Introduction

Key highlights from the report

1. Talos algos beat a naive sweep on average, across strategy types

By strategy type, we found that:

VWAP captured ~38 bps vs. a naive sweep on average
Time-paced taker algos (Time Sliced, Steady Pace) delivered ~7 to 14 bps
Top-of-book algos (Iceberg, Pegged) ~3.1 to 6.8 bps.

These are not marginal numbers when compounded across an institutional book. See Figure 1.

**Figure 1: Arrival slippage and savings vs. sweep by strategy** *Source: Talos data, Talos analysis.*

In addition, when looking at adverse but typical regimes (where markets move up to ~300 bps against the order), Talos algos on average saved 68 bps in execution cost versus an instantaneous sweep.

2. Liquidity sourcing flattens the size penalty for larger tickets

There was strong statistical evidence that venue mix is a driver of execution cost, especially as order sizes scale. For $100K+ tickets, combining exchange + dealer liquidity saved 13.5 bps on average versus exchange-only routing. Liquidity diversification through the smart order router can help to reduce information leakage and can surface more competitive real-time prices for each child slice, flattening the square-root size penalty seen on public books, consistent with the Talos Market Impact Model. See Figure 2.

**Figure 2: Arrival slippage by order amount and market type — Dealer + Exchange (green) vs. Exchange Only (orange).** *Source: Talos data, Talos analysis.*

Why this matters for pre-trade and execution alphas

Every result here is downstream of the same loop: predict microstructure conditions, express them in algorithms, measure outcomes through TCA, and feed learnings back into the models. Overnight forecasts of volume, volatility and spreads – the execution alphas – feed the Talos Market Impact Model, which parameterizes the pre-trade scenario analysis traders use to set horizon and aggressiveness before trading. The 250,000-order dataset behind this report is a year of measurement from that loop, showing where the framework converts into statistically proven slippage outperformance.

At Talos, we believe institutional traders should focus on what to trade while we help them with the how.

Download the full report

‍About the Author

Eliad Hoch is the Head of Quantitative Execution Services at Talos, the premier provider of institutional digital asset technology and data for trading and portfolio management. Based in London, he oversees the front-office lifecycle of algorithmic trading, guiding clients through slippage minimization tactics, trade scenario analysis and TCA, while overseeing the algorithmic execution tools and quantitative models available within the Talos platform. Prior to Talos, Eliad spent 2 years as the Founder of GONLabs, a systematic crypto trading hedge fund, focused on quant and machine learning-driven crypto strategies. Before that, he spent 12 years in the equities, futures and FX markets at Bank Of America Merrill Lynch and Goldman Sachs in portfolio algorithmic execution, quant modeling, central risk trading and systematic internalization market making. Eliad has co-authored several papers on systematic trading strategies and market impact, and published a 2024 paper exploring tokenomics design and DeFi value propositions. He is a guest lecturer at various UK universities on algo trading and quant modeling. Eliad holds a masters in computational finance and artificial intelligence from the University of Southampton, where he received first class honors and the top independent research award.

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