Abstract
Learning profitable intraday trading policies from financial time series is challenging due to heavy noise, non-stationarity, and strong cross-sectional dependence among related assets. We propose \emph{WaveLSFormer}, a learnable wavelet-based long-short Transformer that jointly performs multi-scale decomposition and return-oriented decision learning. Specifically, a learnable wavelet front-end generates low-/high-frequency components via an end-to-end trained filter bank, guided by spectral regularizers that encourage stable and well-separated frequency bands. To fuse multi-scale information, we introduce a low-guided high-frequency injection (LGHI) module that refines low-frequency representations with high-frequency cues while controlling training stability. The model outputs a portfolio of long/short positions that is rescaled to satisfy a fixed risk budget, and is optimized directly with a trading objective and risk-aware regularization. Extensive experiments on five years of hourly data across six industry groups, evaluated over ten random seeds, demonstrate that WaveLSFormer consistently outperforms MLP, LSTM and Transformer backbones, with and without fixed discrete wavelet front-ends. On average in all industries, WaveLSFormer achieves a cumulative overall strategy return of $0.607 \pm 0.045$ and a Sharpe ratio of $2.157 \pm 0.166$, substantially improving both profitability and risk-adjusted returns over the strongest baselines.
Abstract (translated)
从金融时间序列中学习具有盈利能力的日内交易策略是一项挑战，因为存在大量噪声、非平稳性和相关资产间的强横截面依赖。我们提出了一种名为**WaveLSFormer**的方法，这是一种基于可学习小波的长短期Transformer模型，它可以同时进行多尺度分解和以收益为导向的决策学习。 具体来说，WaveLSFormer通过一个端到端训练的小波滤波器组生成低频/高频分量，并使用谱正则化器来鼓励稳定的、间隔良好的频率带。为了融合多尺度信息，我们引入了一个低频引导高频注入（LGHI）模块，该模块在控制训练稳定性的同时用高频提示改进低频表示。 模型输出一个包含长短仓位置的资产组合，并对其进行调整以满足固定的风控预算，在直接优化交易目标和风险感知正则化的基础上进行学习。在五个年度每小时数据集上进行了广泛的实验，包括六个行业组，通过十个随机种子评估结果表明，WaveLSFormer在所有行业中均一致优于MLP、LSTM和Transformer骨干模型，无论是否使用固定离散小波前端。在整个行业中，平均而言，WaveLSFormer实现了0.607±0.045的累计策略总收益以及2.157±0.166的夏普比率，在利润性和风险调整后的回报方面相对于最强基线都有显著改善。
URL
https://arxiv.org/abs/2601.13435
