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dddyym/ml-quant-trading-synthetic

ml-quant-trading deterministic synthetic panel This dataset is the zero-account smoke-test panel generated by ml-quant-trading. It contains no real instruments, proprietary market data, or investment signals. Run the full pipeline Install the latest verified release and run the end-to-end synthetic demo: python -m pip install --upgrade mlquantx mlquant demo The PyPI distribution is mlquantx; the import package and CLI remain mlquant. The demo covers data… See the full description on the dataset page: https://huggingface.co/datasets/dddyym/ml-quant-trading-synthetic.

sourceHugging Facemitupdated 2mo agoView on Hugging Face
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Dataset Card

ml-quant-trading deterministic synthetic panel

This dataset is the zero-account smoke-test panel generated by `ml-quant-trading`. It contains no real instruments, proprietary market data, or investment signals.

Run the full pipeline

Install the latest verified release and run the end-to-end synthetic demo:

bash
python -m pip install --upgrade mlquantx
mlquant demo

The PyPI distribution is `mlquantx`; the import package and CLI remain mlquant. The demo covers data generation, the 213-factor pipeline, a PyTorch model, portfolio construction, and a cost-aware backtest. For source code, tests, and research documentation, visit the GitHub repository or the v0.2.6 release.

This is a research and teaching workflow, not a production trading system or a claim of live profitability.

Associated paper

This artifact accompanies Machine Learning Enhanced Multi-Factor Quantitative Trading (arXiv:2507.07107).

Dataset structure

  • —Rows: 100,000 (500 dates × 200 synthetic assets)
  • —Columns: date, asset_id, OHLCV, vwap, and tradable
  • —Generator commit: `faababb851b22061759f748c252f1cca1eaf0202`
  • —Data SHA-256: 2a76fa09d2567f707abd1f48f2e0d8d59e140ad20083de2e92313ecadc2e7186

Asset identifiers are synthetic labels and do not map to listed securities. Prices come from the repository's deterministic GBM-style generator. Missing and untradable cells are simulated to exercise masks and pipeline behavior.

Load

python
from datasets import load_dataset

dataset = load_dataset("dddyym/ml-quant-trading-synthetic")
print(dataset["train"][0])

Intended use

  • —test installation and data-loading paths;
  • —reproduce the public synthetic demo;
  • —exercise factor, model, portfolio, and backtest code without market-data access;
  • —build CI or teaching examples.

Not intended for

  • —estimating real-market performance;
  • —training or evaluating a deployable trading strategy;
  • —mapping synthetic asset IDs to real securities;
  • —investment decisions.

Regenerate the bundle from the source repository:

bash
mlquant demo
python scripts/export_huggingface_artifacts.py

See the project's Research Card and Reality Check.