对比LSTM与梯度提升在微纳斯达克期货分钟级数据上的预测表现，发现均无法显著超越基础准确率。
Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ
- 用梯度提升与LSTM处理五分鐘OHLCV序列，进行滚动窗口验证
- 所有模型在三个外样本期的准确率均未超过51.8%基线，最高50.89%
- 结果表明单一品种四年数据仍不足以支持可靠时序预测，适合关注金融时序建模边界的研究者
本文比较了梯度提升与长短期记忆（LSTM）架构在微型纳斯达克100期货（MNQ）日内方向预测中的表现。受近期金融蜡烛图基础模型研究（如Kronos架构）启发，我们检验五分鐘OHLCV序列是否在单个品种数据集中蕴含可挖掘的时序预测结构。基于2021–2025年共944个交易日的数据，采用严格扩展窗口滚动验证，在三个外样本期评估四种模型配置。目标变量为收盘价是否高于10:30开市价10点以上。所有配置均未产生统计上显著优于51.8%基准的外样本准确率。组合外样本准确率范围为50.00%至50.89%，LSTM达到50.59%。置换检验显示最优梯度提升模型p值为0.135，LSTM为0.515，均无显著预测优势。特征重要性在滚动折叠间不稳定，表明存在噪声拟合而非稳定结构捕捉。结果表明，四年的单品种五分钟OHLCV数据不足以实现可靠的机器学习驱动日内预测。主要贡献在于对受Kronos启发架构在真实世界受限数据集上的实证评估，提供了时序金融机器学习所需数据规模的实证下限。
原文摘要 · Abstract (English)
This paper compares gradient boosting and long short-term memory (LSTM) architectures for intraday directional prediction in Micro E-Mini Nasdaq 100 futures (MNQ). Motivated by recent foundation-model research on financial candlestick data, including the Kronos architecture, we test whether five-minute OHLCV bar sequences contain exploitable sequential predictive structure at the scale of a single instrument dataset. Using 944 trading days from 2021-2025, four model configurations are evaluated under strict expanding-window walk-forward validation across three out-of-sample periods. The target variable is whether the session close exceeds the 10:30 AM open by more than ten points. No configuration produces statistically significant out-of-sample accuracy above the 51.8% base rate. Combined OOS accuracies range from 50.00% to 50.89% across gradient boosting variants, while the LSTM achieves 50.59%. Permutation tests yield p-values of 0.135 for the best gradient boosting model and 0.515 for the LSTM, indicating no statistically significant predictive edge. Feature importance instability across walk-forward folds suggests noise fitting rather than stable structural signal capture. The results indicate that four years of single-instrument five-minute OHLCV data are insufficient for reliable sequential ML-based intraday forecasting. The primary contribution is a documented evaluation of a Kronos-inspired architecture on a constrained real-world dataset, providing an empirical lower bound on data scale requirements for sequential financial ML.
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