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.

翻译：从金融时间序列中学习盈利的日内交易策略具有挑战性，这源于数据中存在严重噪声、非平稳性以及相关资产间强烈的横截面依赖性。我们提出 \emph{WaveLSFormer}，一种基于可学习小波的多空Transformer，它联合执行多尺度分解和面向收益的决策学习。具体而言，一个可学习的小波前端通过端到端训练的滤波器组生成低/高频分量，其训练由鼓励稳定且分离良好的频带的谱正则化器引导。为了融合多尺度信息，我们引入了一个低频引导的高频注入（LGHI）模块，该模块利用高频线索细化低频表示，同时控制训练稳定性。模型输出一个多/空头寸的投资组合，该组合经过重新缩放以满足固定的风险预算，并通过交易目标和风险感知正则化直接进行优化。在六个行业组五年小时数据上进行的广泛实验，基于十个随机种子进行评估，结果表明WaveLSFormer始终优于MLP、LSTM和Transformer骨干网络，无论是否使用固定的离散小波前端。在所有行业的平均表现中，WaveLSFormer实现了 $0.607 \pm 0.045$ 的累计总策略收益和 $2.157 \pm 0.166$ 的夏普比率，相较于最强基线，在盈利能力和风险调整收益方面均有显著提升。