ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books
The paper presents ClusterLOB, an unsupervised learning framework using K-means++ on high-frequency order book data to improve trading strategies.
What it examines
ClusterLOB uses high-frequency limit order book data and machine learning to cluster trader behavior into directional, opportunistic, and market-making groups using six time-dependent features and K-means++. The study aims to improve return prediction and trading strategies across stocks with different tick sizes.
What it concludes
Results show that clustered trading signals outperform unclustered methods, enhancing prediction and risk management. The research can be applied to algorithmic trading, market analysis, and liquidity management, with future work suggesting online clustering and extra features to further uncover trader behavior dynamics.
Evidence objects
ClusterLOB innovatively clusters high-frequency limit order book data into three trader typesdirectional, opportunistic, and market-makingusing six time-dependent features, enhancing short-term price movement forecasts through improved order flow imbalance signals remarkably.
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Surprisingly, the opportunistic cluster consistently outperforms traditional benchmarks by delivering robust, higher risk-adjusted returns across varying tick-size groups, challenging conventional market dynamics and offering fresh insights into electronic trading strategies.
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Researchers develop a novel base initialization ensuring consistent cluster labels across stocks, accompanied by forward-rolling normalization and rigorous metrics from one year of NASDAQ data, noting dependency on market conditions.
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By clustering market events using machine learning techniques, this paper offers an original perspective on limit order book dynamics and order flow imbalance analysis. Combining well-established methods like $K\text{-}means++$ with novel applications to trading behavior inference, it delivers fresh insights and practical enhancements to trading strategies, making it compelling thereby.
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Raw abstract and provenance
Abstract: In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling… ▽ More In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling market microstructure and participant behavior. We introduce ClusterLOB as a method to cluster individual market events in a stream of market-by-order (MBO) data into different groups. To do so, each market event is augmented with six time-dependent features. By applying the K-means++ clustering algorithm to the resulting order features, we are then able to assign each new order to one of three distinct clusters, which we identify as directional, opportunistic, and market-making participants, each capturing unique trading behaviors. Our experimental results are performed on one year of MBO data containing small-tick, medium-tick, and large-tick stocks from NASDAQ. To validate the usefulness of our clustering, we compute order flow imbalances across each cluster within 30-minute buckets during the trading day. We treat each cluster's imbalance as a signal that provides insights into trading strategies and participants' responses to varying market conditions. To assess the effectiveness of these signals, we identify the trading strategy with the highest Sharpe ratio in the training dataset, and demonstrate that its performance in the test dataset is superior to benchmark trading strategies that do not incorporate clustering. We also evaluate trading strategies based on order flow imbalance decompositions across different market event types, including add, cancel, and trade events, to assess their robustness in various market conditions. This work establishes a robust framework for clustering market participant behavior, which helps us to better understand market microstructure, and inform the development of more effective predictive trading signals with practical applications in algorithmic trading and quantitative finance. △ Less
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