What Are the Primary Data Requirements for Training Robust Quote Survival Predictors? ▴ Question

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References

Arroyo, Álvaro, et al. “Deep Attentive Survival Analysis in Limit Order Books ▴ Estimating Fill Probabilities with Convolutional-Transformers.” arXiv preprint arXiv:2306.05479, 2023.
Gould, Martin D. et al. “Limit order book resiliency and price recovery.” Market Microstructure and Liquidity, vol. 2, no. 01, 2016.
Ntakaris, Adamantios, et al. “Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods.” SSRN Electronic Journal, 2017.
Zaznov, Ilia, and Julian Kunkel. “Predicting Stock Price Changes Based on the Limit Order Book ▴ A Survey.” Mathematics, vol. 10, no. 8, 2022, p. 1333.
Sirignano, Justin, and Rama Cont. “Universal features of price formation in financial markets ▴ perspectives from deep learning.” Quantitative Finance, vol. 19, no. 9, 2019, pp. 1449-1459.

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Reflection

The construction of a quote survival predictor represents a significant commitment of resources and expertise. It moves a trading operation from a reactive to a proactive stance, allowing for a more nuanced and intelligent approach to liquidity provision and order execution. The knowledge gained through this process is not merely a predictive model; it is a deeper, more quantitative understanding of the market’s microstructure. The true value lies in using this system not as a black box, but as a tool for asking more sophisticated questions about your own execution strategy.

How does your order placement strategy interact with prevailing order flow? At what point does the risk of holding a passive order outweigh the potential benefit of capturing the spread? The answers to these questions, illuminated by a robust predictive framework, are the foundation of a durable competitive edge.