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. Unlike standard time-series forecasting that optimizes prediction error and typically requires a separate position-sizing or portfolio-construction step, our model directly outputs a market-neutral long/short portfolio and is trained end-to-end on a trading objective with risk-aware regularization. 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，主要源于数据噪声大、非平稳性以及相关资产间存在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。与标准时间序列预测方法（通常优化预测误差并需要单独的仓位规模确定或投资组合构建步骤）不同，我们的模型直接输出一个市场中性的多/空投资组合，并通过一个具有风险感知正则化的交易目标进行端到端训练。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，并受到频谱正则化器的引导，这些正则化器鼓励生成稳定且分离良好的频带。为了融合多尺度信息，我们引入了一种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，该组合经过重新调整以满足固定的风险预算，并直接通过交易目标和风险感知正则化进行优化。在跨越六个行业组的五年小时数据上进行的广泛实验（评估了十个随机种子）表明，WaveLSFormer 在有无固定离散小波前端的情况下，均持续优于 MLP、LSTM 和 Transformer 骨干网络。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，相较于最强的基线模型，在盈利能力和风险调整收益方面均有显著提升。