mlquant

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

CI arXiv Python 3.9+

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.

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

Start here

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.

Project links

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Latest release

1 maintainer

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Avatar for initial-d from gravatar.com initial-d

Credits

Author: Yimin Du

GitHub Statistics

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The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.9

Provides Extra

mosek ecos gpu dev

Tags

quant alpha factor portfolio pytorch markowitz

Classifiers

Report project as malware

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