5 papers analyzed
These studies suggest that deep learning models and execution probability models can effectively estimate fill probabilities in limit order books, considering factors like order size, queue lengths, and market depth.
The fill probability of a limit order in a limit order book (LOB) is a critical factor in trading strategies. It determines the likelihood that an order will be executed within a certain time frame, influencing decisions between using market orders and limit orders. This synthesis explores various models and approaches to estimate and understand the fill probability in LOBs.
Data-Driven Approaches:
Stochastic Models:
Execution Probability Models:
Queueing Theory and Order Book Shape:
The fill probability of limit orders in a limit order book can be effectively estimated using various approaches, including data-driven neural networks and stochastic models. These methods highlight the importance of market conditions, order sizes, and queue lengths in determining execution probabilities. Understanding these factors can significantly enhance trading strategies and decision-making processes.
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