通过复杂性度量检测加密货币交易所的异常交易模式
Detecting unusual trading patterns on cryptocurrency exchanges by means of complexity measures
AI总结：
该研究针对加密货币交易所人工交易致市场操纵问题，基于高频交易数据的复杂性和统计结构度量，提出诊断框架，应用于多种加密货币，发现2025年5月中旬后Bitget上比特币和以太坊有异常，基于复杂性指标能检测隐藏交易异常。
AI中文摘要：
人工交易生成仍是加密货币交易所潜在市场操纵的重要来源，可能扭曲报告的流动性并降低市场透明度。本研究基于高频交易级数据得出的复杂性和统计结构度量，提出了一个检测异常交易模式的诊断框架。分析考虑对数收益率、交易量和交易数量，使用尾部分布、自相关函数、多重分形特征、近似熵和去趋势交叉相关性。该方法应用于2025年4月1日至6月30日在币安、Bitget、库币和 Kraken 上交易的比特币、以太坊和瑞波币。结果显示2025年5月中旬后，Bitget上的比特币和以太坊出现明显异常。交易量和回报波动没有相应增加，但交易数量急剧增加。这种情况的特点是大量低交易量交易、自相关性较弱、多重分形组织减少、短模式不规则性较高以及与交易数量序列的交叉相关性较弱。这些特征与交易活动中的类似噪声成分一致，可能表明交易数量人为增加，尽管不能直接证明是洗盘交易。研究结果表明，基于复杂性的指标有助于检测基于价格的度量中隐藏的特定交易所交易异常。
英文摘要：
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
