Release Draft: v0.1.0

Title:

v0.1.0 - Public research baseline for ML-enhanced multi-factor trading

Body:

ml-quant-trading is now ready as a public research baseline for people interested in factor research, tensorized financial data, portfolio optimization, and reproducible backtesting.

What is included

• 213 factor dimensions.
• Mask-aware PyTorch tensor factor primitives.
• A-share oriented bias correction for limit-up, limit-down, and halt cases.
• MLP and Transformer baselines.
• AdjMSE, IC, and RankIC losses.
• Cross-sectional Markowitz optimization.
• Vectorized backtesting and metrics.
• Synthetic-data end-to-end pipeline.
• Public-data factor IC notebook.
• CPU/GPU tensor factor benchmark script.
• CI, tests, citation metadata, and contribution templates.

Quick start

git clone https://github.com/initial-d/ml-quant-trading.git
cd ml-quant-trading
pip install -e .[dev]
make paper CONFIG=configs/small.yaml

Community ask

I would especially appreciate:

• benchmark results from different CPUs/GPUs
• public-data reproduction reports
• factor-engine edge cases
• documentation fixes
• small examples that make the project easier to learn

Use the benchmark issue template to submit performance results.

Disclaimer

This repository is for research and engineering experimentation. It is not financial advice, investment advice, or a trading recommendation.