Paper: arXiv 2507.07107

Authors: Yimin Du

Abstract

This paper presents a comprehensive machine learning framework for quantitative trading that achieves superior risk-adjusted returns through systematic factor engineering, real-time computation optimization, and cross-sectional portfolio construction. Our approach integrates multi-factor alpha discovery with bias correction techniques, leveraging PyTorch-accelerated factor computation and advanced portfolio optimization. The system processes 500-1000 factors derived from open-source alpha101 extensions and proprietary market microstructure signals. Key innovations include tensor-based factor computation acceleration, geometric Brownian motion data augmentation, and cross-sectional neutralization strategies. Empirical validation on Chinese A-share markets (2010-2024) demonstrates annualized returns of $20%$ with Sharpe ratios exceeding 2.0, significantly outperforming traditional approaches. Our analysis reveals the critical importance of bias correction in factor construction and the substantial impact of cross-sectional portfolio optimization on strategy performance. Code and experimental implementations are available at: https://github.com/initial-d/ml-quant-trading

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents advanced mathematical frameworks including tensor operations, geometric Brownian motion augmentation, and complex portfolio optimization formulations, while demonstrating high empirical rigor with detailed performance metrics, backtest data over a 14-year period, and publicly available code implementations.

Research Flowchart

  flowchart TD
  A["Research Goal: ML-Enhanced Quant Trading with<br>Factor Optimization & Bias Correction"] --> B["Data & Inputs<br>1000+ Factors: Alpha101 + Microstructure<br>Chinese A-Shares (2010-2024)"]
  B --> C["Methodology: Tensor-Accelerated<br>Factor Computation via PyTorch"]
  C --> D["Processing: Cross-Sectional<br>Portfolio Optimization & Neutralization"]
  D --> E["Innovation: Bias Correction<br>+ Geometric Brownian Motion Augmentation"]
  E --> F["Outcomes: 20% Annualized Return<br>Sharpe Ratio > 2.0"]
  F --> G["Key Finding: Bias Correction &<br>Portfolio Optimization Critical to Performance"]