An Empirical Model of Market Impact in Cryptocurrency Trading
An empirical model of market impact in cryptocurrency trading is presented, decomposing execution costs into physical impact and time-risk components. The model is estimated using Talos’s proprietary high-frequency trade and quote data for the top 60 spot and perpetual contracts (June 2024–July 2025), encompassing over 50,000 parent (meta) orders and 50 million child orders. The dataset uniquely captures the complete lifecycle of long-duration orders, enabling estimation of impact over the full execution horizon, a feature rarely available in public datasets and critical for modeling time-dependent price effects. The framework provides actionable insights for optimal execution strategy selection in digital asset markets.
An Empirical Model of Market Impact in Cryptocurrency Trading
Introduction
An empirical model of market impact in cryptocurrency trading is presented, decomposing execution costs into physical impact and time-risk components. The model is estimated using Talos’s proprietary high-frequency trade and quote data for the top 60 spot and perpetual contracts (June 2024–July 2025), encompassing over 50,000 parent (meta) orders and 50 million child orders. The dataset uniquely captures the complete lifecycle of long-duration orders, enabling estimation of impact over the full execution horizon, a feature rarely available in public datasets and critical for modeling time-dependent price effects. The framework provides actionable insights for optimal execution strategy selection in digital asset markets.
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Related Links:
- Understanding Market Impact in Crypto Trading: The Talos Model for Estimating Execution Costs →
- VWAP or TWAP for Crypto Execution? A Market Impact Perspective →
- Execution Alphas in Crypto Markets: Predicting Volume, Volatility, and Spreads to Reduce Slippage →
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 quantitative trading strategies offered by the firm. 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.
Disclaimer: Talos Global, Inc., together with its affiliates (collectively, “Talos”), is not an investment advisor or broker/dealer. No Talos product or service constitutes an offer to buy or sell, or a promotion or recommendation of, any digital asset, security, derivative, commodity, financial instrument or product or trading strategy. Further, No Talos product or service is intended to constitute investment advice or a recommendation to make (or refrain from making) any kind of investment decision and may not be relied on as such. Talos offers data and software as a service products that provide connectivity tools for institutional clients.
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