提出稀疏谱线性交易策略，提升预测矩阵的特征探索与鲁棒性。
Linear Trading Position with Sparse Spectrum
- 基于稀疏谱设计线性交易位置，扩大对预测矩阵的谱域覆盖。
- 采用Krasnosel'skií-Mann算法，实现目标值线性收敛。
- 实验验证在多种场景下表现优异且稳定，适合量化交易研究者。
主流信号驱动的组合投资方法虽有发展，但其主成分组合往往缺乏多样性，难以充分挖掘预测矩阵的关键特征，且对不同市场情境适应性不足。为此，本文提出一种新型稀疏谱线性交易位置，可有效拓展对预测矩阵的谱域探索范围。同时，设计了Krasnosel'skií-Mann型不动点算法进行优化，该算法具备下降性质，并首次证明了目标函数值的线性收敛速率，为同类算法提供了新的理论结果。大量实验证明，所提方法在多种市场条件下均表现出良好的性能和强鲁棒性。
原文摘要 · Abstract (English)
The principal portfolio approach is an emerging method in signal-based trading. However, these principal portfolios may not be diversified to explore the key features of the prediction matrix or robust to different situations. To address this problem, we propose a novel linear trading position with sparse spectrum that can explore a larger spectral region of the prediction matrix. We also develop a Krasnosel'ski\u ı-Mann fixed-point algorithm to optimize this trading position, which possesses the descent property and achieves a linear convergence rate in the objective value. This is a new theoretical result for this type of algorithms. Extensive experiments show that the proposed method achieves good and robust performance in various situations.
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