{"repo":"initial-d/ml-quant-trading","free":true,"listed":false,"github":"https://github.com/initial-d/ml-quant-trading","clone":"git clone https://github.com/initial-d/ml-quant-trading.git","description":"PyTorch research stack for ML multi-factor trading: 213 factors, bias correction, portfolio optimization, and vectorized backtesting.","language":"Python","stars":73,"topics":["a-shares","algorithmic-trading","alpha-factors","backtesting","china-stock-market","cross-sectional","data-augmentation","deep-learning","factor-investing","gpu-computing"],"license":"MIT","category":"trading","readme_excerpt":"ml-quant-trading A reproducible PyTorch stack for cross-sectional factor research — from 213 mask-aware factors to cost-aware portfolios, backtests, and auditable reports. Languages: English 简体中文 繁體中文 Run in Colab · Inspect the benchmark · See cost-aware results · Read the paper Quick Start No market-data account or API key is required. In 30–90 seconds, the deterministic demo runs data → factors → model → portfolio → backtest and writes shareable Markdown and JSON reports. 213 factors 4 data paths 97 tests CPU/GPU benchmark ---: ---: ---: ---: Mask-aware PyTorch tensors Synthetic, AkShare, Baostock, yfinance Deterministic engineering checks Reproducible across machines See the benchmark board for complete environments, commands, and raw results. Cross-machine snapshots are reported separately and are not presented as controlled hardware rankings. --- Why this repository? You get Why it matters --- --- 213 factor dimensions Mask-aware PyTorch factors with documented families and tensor primitives One end-to-end path Data → factors → models → portfolio → cost-aware backtest → report Public and synthetic data Start without proprietary data or an API key, then move to AkShare, Baostock, or yfinance Evidence, including failures Costs, turnover, baselines, caveats, and negative results stay visible A contribution path CI, tests, report templates, Colab, and newcomer-sized research tasks Try the live Hugging Face artifacts: the 100,000-row synthetic dataset and 213-input MLP checkp","default_branch":null,"files":null,"tree":[],"storefront":"/r/initial-d","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/initial-d/ml-quant-trading/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}