Mitigando Slippage em Serviços RFQ
Resumo
O presente trabalho descreve um estudo voltado para a mitigação do problema de slippage em serviços RFQ. Slippage refere-se à diferença entre o preço esperado e o preço executado em uma cotação de compra ou venda de uma determinada quantia em um par de moedas. As duas principais estratégias consideradas foram o ajuste do spread e o cancelamento de cotações. O estudo envolve a coleta e o processamento de dados para inferir estatisticamente o spread ideal que, simultaneamente, mantém um preço competitivo, reduz os prejuízos da empresa e respeita o SLA. O resultado é o estabelecimento de uma heurística simples e razoável para abordar o problema e um webapp para visualizar a heurística proposta.
Palavras-chave:
Slippage, RFQ, Heurística
Referências
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Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4):987–1007.
Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11):1709–1742.
Kaiko (2023). Execution strategies and slippage in crypto markets. [Online; acessado em abril de 2025].
Lim, T. (2022). Predictive crypto-asset automated market making architecture for decentralized finance using deep reinforcement learning. arXiv, (2211.01346).
Luan Martins (2025a). Mitigando slippage. [link]. [Online; accessed 26-March-2025].
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Martins, L. (2023). Entendendo e evitando o slippage problem na web3.0. Pantheon.
Nevmyvaka, Y., Feng, Y., & Kearns, M. (2006). Reinforcement learning for optimized trade execution. In Proceedings of the 23rd International Conference on Machine Learning (pp. 673–680).
Parfin (2025). Empowering you with full life cycle digital asset solutions to shape the future of finance. [link]. [Online; acessado em abril de 2025].
Research, P. (2023). Estimating market impact in crypto markets. [Online; acessado em abril de 2025].
Shah, J., Javare, P., & Khetan, D. (2020). Slipswap: Reduce the slippage that is incurred during the swap of tokens using algorithmic analysis. IEEE, 131–134.
Spooner, T., Savani, R., & Megson, S. (2018). Market making via reinforcement learning. arXiv preprint arXiv:1804.04216.
Street, J. (2023). Latency arbitrage and quote protection. [Online; acessado em abril de 2025].
Trading, R. (2023). Rl for mm strategies. [Online; acessado em abril de 2025].
Wintermute (2023). Crypto liquidity 101. [Online; acessado em abril de 2025].
Almgren, R., & Chriss, N. (2000). Optimal execution of portfolio transactions. Journal of Risk, 3(2):5–40.
Aoyagi, Y., & Ibuka, N. (2021). Simulation-based estimation of execution cost in crypto market using Monte Carlo methods. arXiv preprint arXiv:2109.12549.
Bitstamp (2025a). Bitstamp live order book. [link]. [Online; accessed 20-March-2025].
Bitstamp (2025b). Bitstamp public API (v2). [link]. [Online; accessed 20-March-2025].
Bitstamp (2025c). Buy & trade with ease on the trusted crypto exchange. [link]. [Online; accessed 20-March2025].
Bouchaud, J.-P., Bonart, J., Donier, J., & Gould, M. (2018). Trades, Quotes and Prices: Financial Markets Under the Microscope. Cambridge University Press.
Cartea, Á., Jaimungal, S., & Penalva, J. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press.
Coinbase (2022). Machine learning at coinbase. [Online; acessado em abril de 2025].
Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2):223.
Dixon, M., Halperin, I., & Bilokon, P. (2020). Machine Learning in Finance: From Theory to Practice. Springer.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4):987–1007.
Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11):1709–1742.
Kaiko (2023). Execution strategies and slippage in crypto markets. [Online; acessado em abril de 2025].
Lim, T. (2022). Predictive crypto-asset automated market making architecture for decentralized finance using deep reinforcement learning. arXiv, (2211.01346).
Luan Martins (2025a). Mitigando slippage. [link]. [Online; accessed 26-March-2025].
Luan Martins (2025b). Repositório do notebook Jupyter. [link]. [Online; accessed 26-March-2025].
Luan Martins (2025c). Repositório do webapp mitigando slippage. [link]. [Online; accessed 26-March-2025].
Martins, L. (2023). Entendendo e evitando o slippage problem na web3.0. Pantheon.
Nevmyvaka, Y., Feng, Y., & Kearns, M. (2006). Reinforcement learning for optimized trade execution. In Proceedings of the 23rd International Conference on Machine Learning (pp. 673–680).
Parfin (2025). Empowering you with full life cycle digital asset solutions to shape the future of finance. [link]. [Online; acessado em abril de 2025].
Research, P. (2023). Estimating market impact in crypto markets. [Online; acessado em abril de 2025].
Shah, J., Javare, P., & Khetan, D. (2020). Slipswap: Reduce the slippage that is incurred during the swap of tokens using algorithmic analysis. IEEE, 131–134.
Spooner, T., Savani, R., & Megson, S. (2018). Market making via reinforcement learning. arXiv preprint arXiv:1804.04216.
Street, J. (2023). Latency arbitrage and quote protection. [Online; acessado em abril de 2025].
Trading, R. (2023). Rl for mm strategies. [Online; acessado em abril de 2025].
Wintermute (2023). Crypto liquidity 101. [Online; acessado em abril de 2025].
Publicado
19/05/2025
Como Citar
FELIX, Luan Martins; MENASCHÉ, Daniel Sadoc.
Mitigando Slippage em Serviços RFQ. In: WORKSHOP EM BLOCKCHAIN: TEORIA, TECNOLOGIAS E APLICAÇÕES (WBLOCKCHAIN), 8. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 182-195.
DOI: https://doi.org/10.5753/wblockchain.2025.9509.
