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Community Outreach Targets

Use this page to share the project with relevant communities without spam.

Primary Audience

  • Quant researchers and students.
  • ML engineers interested in tensorized financial panels.
  • Research software users who care about reproducibility.
  • Portfolio and backtesting tool builders.

Recommended Communities

Post only where you already have an account and can reply to comments.

Channel Angle Suggested ask
GitHub Issues / Discussions Project launch and benchmark call Submit CPU/GPU benchmark results
LinkedIn Research software announcement Feedback from quant/ML engineers
X / Twitter Short technical launch Star, benchmark, or try notebook
Reddit r/algotrading Practical backtesting/factor pipeline Reproducibility and caveat feedback
Reddit r/quant Research baseline Public-data reproduction feedback
Hacker News Show HN Open-source research stack Try quick start and critique design
Quant StackExchange chat / communities Factor-engine discussion Edge cases and assumptions
University quant clubs Student-friendly baseline Run Start Here and report issues

Short Post

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

It includes 213 factors, masked tensor ops, bias correction, MLP/Transformer baselines, Markowitz optimization, vectorized backtesting, a public-data notebook, CI, and benchmark tooling.

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

I am looking for CPU/GPU benchmark results and public-data reproduction feedback.

Longer Community Post

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

The goal is to provide a reproducible baseline rather than a trading signal claim. The repo contains:

  • 213 factor dimensions
  • mask-aware PyTorch tensor primitives
  • limit-up / limit-down / halt bias correction
  • MLP and Transformer baselines
  • Markowitz portfolio construction
  • vectorized backtesting and metrics
  • synthetic and public-data demos
  • CI, tests, benchmark scripts, and contribution templates

The paper used proprietary data, so the public repo focuses on synthetic and public-data reproduction paths. I would especially appreciate:

  • CPU/GPU benchmark reports
  • public-data case studies
  • factor-engine edge cases
  • documentation and setup feedback

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

Posting Rules

  • Customize the first paragraph for each community.
  • Disclose limitations clearly.
  • Do not imply live trading profitability.
  • Do not ask directly for stars before asking for useful feedback.
  • Reply to comments and convert repeated questions into docs.