mlquant

A reproducible PyTorch research stack for machine-learning multi-factor trading: 213 factors, bias correction, portfolio optimization, vectorized backtesting, and System-One-style portfolio decisions.

Install and run

python -m pip install mlquantx
mlquant demo

The demo needs no market-data account or API key. It runs the deterministic synthetic pipeline from data generation through 213 factor dimensions, model training, portfolio construction, cost-aware backtesting, and Markdown/JSON report generation. The default config ships inside the wheel, so the command works outside a repository checkout.

The AkShare, Baostock, and yfinance public-data loaders are an optional extra:

python -m pip install 'mlquantx[data]'

Customize or contribute

The wheel is the fastest way to try the project. Clone the repository when you want to change factors, models, portfolio constraints, data sources, or backtest assumptions:

git clone https://github.com/initial-d/ml-quant-trading.git
cd ml-quant-trading
python -m pip install -e '.[dev]'

If the demo saves you setup time, consider starring the repository or sharing a reproducible run.

What is included

• 204 hand-crafted factors plus 9 curated Alpha101-style factors
• mask-aware PyTorch tensor primitives for cross-sectional panels
• limit-up, limit-down, halt, and missing-data bias handling
• MLP and Transformer research baselines
• constrained Markowitz portfolio construction
• vectorized backtesting with turnover and transaction costs
• AkShare, Baostock, yfinance, and deterministic synthetic data paths
• auditable public-data validation reports, including negative results
• mlquant systemone decide for typed portfolio-intent decisions from structured research state

Start here

• Source and full documentation
• Google Colab quick start
• Public validation dashboard
• System-One-compatible portfolio decisions
• Research card and limitations
• 100,000-row synthetic dataset
• 213-input MLP checkpoint
• Paper: arXiv:2507.07107

Research boundary

mlquant is research and educational software. It is not investment advice or a production trading system. Synthetic smoke tests verify engineering behavior, not profitability. Public-data backtests depend on data quality, survivorship, transaction costs, slippage, and modeling assumptions and do not represent live or guaranteed out-of-sample performance.

License

MIT. See the repository license.