ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books

Paper: arXiv 2504.20349

Authors: Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.0/10 (capped from 8.5: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs standard statistical methods (K-means clustering) and simple signals (order flow imbalance) without advanced mathematical derivations, resulting in moderate math complexity; however, it demonstrates high empirical rigor through the use of one year of MBO data from NASDAQ, explicit backtesting protocols, Sharpe ratio optimization, and the provision of source code for reproducibility.

Research Flowchart

  flowchart TD
  A["Research Goal"] --> B["Data Preparation"]
  B --> C["Feature Engineering"]
  C --> D["Clustering K-means++"]
  D --> E["Signal Generation"]
  E --> F["Strategy Evaluation"]
  F --> G["Key Findings"]

  subgraph A ["Research Goal"]
      A1["How can clustering orders in<br>Limit Order Books enhance<br>trading strategies?"]
  end

  subgraph B ["Data Inputs"]
      B1["1 Year NASDAQ MBO Data"]
      B2["Small/Medium/Large-Tick Stocks"]
  end

  subgraph C ["Methodology"]
      C1["Augment Orders with<br>6 Time-Dependent Features"]
  end

  subgraph D ["Computational Process"]
      D1["K-means++ Clustering<br>3 Clusters Identified:"]
      D2["Directional<br>Opportunistic<br>Market-Making"]
  end

  subgraph E ["Signal Generation"]
      E1["Order Flow Imbalance<br>per Cluster (30-min buckets)"]
  end

  subgraph F ["Validation"]
      F1["Training: Optimize Sharpe Ratio"]
      F2["Testing: Benchmark Comparison<br>(vs Non-Clustered Strategies)"]
  end

  subgraph G ["Key Outcomes"]
      G1["Superior Performance with Clustering"]
      G2["Robust Framework for<br>Market Microstructure Analysis"]
      G3["Practical Application in<br>Algorithmic Trading"]
  end

Cited References (24)

1. The price impact of order book events (2014)
2. Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book (2023)
3. Limit order books (2013)
4. Cross-impact of order flow imbalance in equity markets (2023)
5. Improving Deep Learning of Alpha Term Structures from the Order Book (2024)
6. Order Flow Decomposition for Price Impact Analysis in Equity Limit Order Books (2023)
7. Lobster: Limit order book reconstruction system (2011)
8. The price impact of order book events: market orders, limit orders and cancellations (2012)

5 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.