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加密货币交易合成时间序列基准测试

文章 arXiv papers · 作者: Yihao Ang et al.
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总结

CTBench是一项用于评估加密货币合成时间序列的基准测试，其设计考虑了市场持续交易、高波动率和状态快速变化等特点。该基准汇集了452种代币的数据，在四种市场状态下评估八种有代表性的生成模型。其13项指标涵盖预测准确度、横截面排名、交易结果、风险和计算成本。

该基准包含两项任务：预测效用检验生成数据是否保留了有助于预测的模式；统计套利检验重建的序列是否支持均值回复交易信号。报告的分析发现，统计拟合度与盈利能力之间存在权衡，并提供比较排名和模型选择指导。该基准提供了评估合成数据的框架，但其发现取决于所选模型、指标、市场状态和评估策略；合成数据高度相似本身并不能证明其具有实盘交易价值。

核心观点

  • CTBench评估生成的加密货币时间序列在预测和交易用途上的表现。
  • 其指标涵盖预测、排名拟合度、交易表现、风险和计算效率。
  • 预测效用任务检验合成数据是否保留了有助于预测的模式。
  • 统计套利任务评估数据是否支持均值回复交易信号。
  • 报告的比较显示，统计拟合度与盈利能力可能并不一致。

标签

加密资产机器学习统计学回测均值回归
全文
# CTBench: Cryptocurrency Time Series Generation Benchmark


# CTBench: Cryptocurrency Time Series Generation Benchmark









Synthetic time series are essential tools for data augmentation, stress testing, and algorithmic prototyping in quantitative finance. However, in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, jeopardizing practical utility. Most prior work (1) targets non-financial or traditional financial domains, (2) focuses narrowly on classification and forecasting while neglecting crypto-specific complexities, and (3) lacks critical financial evaluations, particularly for trading applications. To address these gaps, we introduce \textsf{CTBench}, the first comprehensive TSG benchmark tailored for the cryptocurrency domain. \textsf{CTBench} curates an open-source dataset from 452 tokens and evaluates TSG models across 13 metrics spanning 5 key dimensions: forecasting accuracy, rank fidelity, trading performance, risk assessment, and computational efficiency. A key innovation is a dual-task evaluation framework: (1) the \emph{Predictive Utility} task measures how well synthetic data preserves temporal and cross-sectional patterns for forecasting, while (2) the \emph{Statistical Arbitrage} task assesses whether reconstructed series support mean-reverting signals for trading. We benchmark eight representative models from five methodological families over four distinct market regimes, uncovering trade-offs between statistical fidelity and real-world profitability. Notably, \textsf{CTBench} offers model ranking analysis and actionable guidance for selecting and deploying TSG models in crypto analytics and strategy development.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。