AI 中文总结
本研究对比加密货币市场中基于刻度与分钟的信息条，发现刻度数据的优势具条类型特异性，频率匹配后刻度美元条表现最优，可恢复原始过采样序列拒绝的序列独立性。
AI 中文摘要
本文针对2020年1月至2025年12月期间币安BTCUSDT USDT保证金永续期货市场，基于原始aggTrade刻度数据和一分钟OHLCV数据构建了六种信息条类型（美元条、成交量条、波动率条、范围条、Renko条和混合条），并与固定间隔时间条基准进行了受控比较。两种流程共享一个共同的自适应EMA校准框架；刻度流程额外使用严格的刻度原生活动信号，将数据分辨率作为唯一实验变量。针对八项统计质量标准的结果显示，刻度数据的优势具有条类型特异性，在对分钟内价格动态最敏感的活动信号的条类型中最为显著：刻度Renko条实现了记录的最小随机游走偏差（|VR(4)-1|=0.020，滞后1阶自相关=0.002），刻度波动率条相对于分钟基准降低了69%的序列相关性（|VR(4)-1|：0.028对比0.089）。在六年多制度样本中，正态性改善具有制度依赖性且为次要因素：2020-2022年的极端市场事件使所有条类型的肥尾膨胀，且在研究的样本量下所有序列均拒绝Ljung-Box独立性。匹配频率稳健性分析显示，刻度数据在分布标准上的表观劣势主要是采样频率的人工产物：当刻度序列被粗化为分钟流程的条数量时，频率匹配的刻度美元条在所有六项标准中领先，匹配的刻度波动率条获得LB p=0.51，恢复了原始过采样序列拒绝的序列独立性。
英文摘要
This paper provides a controlled comparison of six information bar types (dollar, volume, volatility, range, Renko, and hybrid bars) constructed from both raw Binance aggTrade tick data and one-minute OHLCV bars for the BTCUSDT USDT-margined perpetual futures market over a six-year period spanning January 2020 to December 2025, and evaluated against fixed-interval time-bar baselines. Both pipelines share a common adaptive EMA calibration framework; the tick pipeline additionally uses strictly tick-native activity signals, isolating data resolution as the sole experimental variable. Results across eight statistical quality criteria reveal that the tick advantage is bar-type-specific and most pronounced in bar types whose activity signals are most sensitive to intra-minute price dynamics: tick Renko bars achieve the smallest random-walk deviation recorded ($|\mathrm{VR}(4){-}1| = 0.020$, lag-1 autocorrelation $= 0.002$), and tick volatility bars reduce serial dependence by 69\% relative to the minute baseline ($|\mathrm{VR}(4){-}1|: 0.028$ versus $0.089$). In the multi-regime six-year sample, normality improvements are regime-dependent and secondary: the extreme market events of 2020--2022 inflate fat tails across all bar types, and Ljung-Box independence is rejected for all series at the sample sizes studied. A matched-frequency robustness analysis shows that the apparent tick underperformance on distributional criteria is largely a sampling-frequency artefact: when tick series are coarsened to the minute pipeline's bar count, frequency-matched tick dollar bars lead on all six criteria and matched tick volatility bars attain LB $p = 0.51$, recovering serial independence that the raw oversampled series rejects.
