We present a new approach to study big data in finance (specifically, in limit order books), based on stochastic modelling of price changes associated with high-frequency and algorithmic trading. We introduce a big data in finance, namely, limit order books (LOB), and describes them by Lobster data-academic data for studying LOB. Numerical results, associated with Lobster and other data, are presented, and explanation and justification of our method of studying of big data in finance are considered. We also describe various stochastic models for mid-price changes in the market, and explain how to use these models in practice, highlighting the methodological aspects of using the models.

我们基于与高频交易和算法交易相关的价格变化的随机建模，提供了一种新的方法来研究金融中的大数据（特别是在限价单中）。我们介绍金融中的大数据，即限价订单簿（LOB），并通过Lobster数据-学术数据对它们进行描述，以研究LOB。提出了与龙虾和其他数据相关的数值结果，并考虑了我们对金融大数据研究方法的解释和合理性。我们还描述了市场中价变化的各种随机模型，并解释了如何在实践中使用这些模型，重点介绍了使用这些模型的方法论方面。