Promotion Kit

July 4, 2026 ยท View on GitHub

Use this page when announcing releases, asking for benchmark results, or sharing the project with research and engineering communities.

Short Description

ml-quant-trading is an end-to-end PyTorch research stack for ML-enhanced multi-factor trading, covering tensor factor computation, A-share bias correction, portfolio optimization, and reproducible backtesting.

Copy-Ready Posts

GitHub / Release Note

I open-sourced ml-quant-trading, a runnable research implementation for ML-enhanced multi-factor trading.

It includes:

  • 213 alpha/factor dimensions
  • masked PyTorch tensor primitives for cross-sectional panels
  • limit-up / limit-down / halt bias correction
  • MLP / Transformer baselines
  • Markowitz portfolio optimization
  • vectorized backtesting and metrics
  • synthetic and public-data demos

Repo: https://github.com/initial-d/ml-quant-trading Paper: https://arxiv.org/abs/2507.07107

I would especially welcome benchmark results, public-data reproductions, and small PRs that improve examples or tests.

X / Short Social Post

I open-sourced ml-quant-trading: an end-to-end PyTorch stack for ML multi-factor research.

213 factors, masked tensor ops, bias correction, Markowitz optimization, backtesting, CI, synthetic demo, and public-data notebook.

Repo: https://github.com/initial-d/ml-quant-trading

LinkedIn

I have open-sourced ml-quant-trading, a research-oriented implementation of ML-enhanced multi-factor quantitative trading.

The project focuses on reproducibility and practical engineering:

  • a complete factor-to-portfolio-to-backtest pipeline
  • 213 factor dimensions
  • PyTorch tensor primitives for masked financial panels
  • limit-up / limit-down / halt bias correction
  • synthetic and public-data demos for users without proprietary data
  • CI, tests, benchmark scripts, and contributor templates

I am looking for feedback from quant researchers, students, and engineers who care about reproducible factor research and backtesting infrastructure.

https://github.com/initial-d/ml-quant-trading

Community Post

I built an open-source research stack for ML-enhanced multi-factor trading and would like feedback from people who work on factor research, backtesting, or tensorized finance data.

The repo includes a runnable synthetic-data pipeline, a public-data notebook, 213 factors, masked PyTorch tensor primitives, bias correction, Markowitz optimization, and backtesting.

I am especially interested in:

  • reproducibility feedback
  • CPU/GPU benchmark results
  • public-data examples
  • factor-engine edge cases
  • documentation improvements

Repo: https://github.com/initial-d/ml-quant-trading Paper: https://arxiv.org/abs/2507.07107

Launch Checklist

Before posting:

  • Confirm CI is green.
  • Confirm the README quick start still works.
  • Create or update a GitHub release.
  • Add a short benchmark result or screenshot.
  • Link to one runnable notebook or command.
  • Ask for one specific contribution.

After posting:

  • Reply to every substantive comment.
  • Convert repeated questions into docs.
  • Convert good suggestions into issues.
  • Thank benchmark contributors in release notes.

Good Calls to Action

  • "Run make benchmark and share your CPU/GPU result."
  • "Try the public-data notebook and report data-source issues."
  • "Suggest one missing factor family."
  • "Open an issue if a reproduction step is unclear."
  • "Send a PR with a small public-data example."

Automation Ideas That Are Safe

  • Weekly draft of one post from recent commits and issues.
  • Monthly release notes generated from merged PRs and closed issues.
  • Scheduled benchmark call asking users to submit results.
  • A recurring maintainer checklist: CI, README quick start, issues, release notes.

Avoid automation that posts to external communities without review. It usually looks like spam and damages trust.