Growth Plan

July 27, 2026 ยท View on GitHub

This repository is most likely to grow by becoming the easiest public reference for machine-learning multi-factor research workflows: runnable demos, transparent assumptions, benchmarks, and small extension points.

Positioning

One sentence:

An end-to-end PyTorch research stack for ML-enhanced multi-factor trading: factor tensors, bias correction, portfolio optimization, and reproducible backtests.

Primary audience:

  • Quant researchers who want a runnable baseline.
  • Students learning factor research, IC evaluation, and backtesting.
  • Engineers who need masked tensor primitives for cross-sectional finance data.
  • Contributors who can add public-data examples, factor tests, and benchmark results.

What Makes the Project Shareable

  • It has a paper-backed story and an arXiv link.
  • It is runnable without proprietary data through synthetic and public-data demos.
  • It has a clear factor library: 213 factors, including 204 legacy factors.
  • It includes CI, tests, issue templates, contribution docs, and benchmark tooling.
  • It has enough real engineering depth to be fork-worthy, not just a notebook.

Current High-Leverage Work

  1. Add a polished social preview image in GitHub repository settings.
  2. Publish one short launch post with a reproducible claim: "Clone, install, and run a full synthetic factor-to-backtest pipeline in one command."
  3. Invite benchmark submissions through the benchmark issue template.
  4. Add one public-data case study with a small universe and clear caveats.
  5. Add a small Hugging Face demo artifact or model card that uses synthetic or clearly redistributable data only.
  6. Convert repeated external questions into docs, issues, or benchmark board entries.
  7. Follow up on high-relevance awesome-list submissions only after maintainers have had time to review.

Already done:

  • Repository topics are filled out for quantitative finance, PyTorch, backtesting, and reproducibility discovery.
  • Tagged releases exist for v0.1.0, v0.2.0, and v0.2.1.
  • Hugging Face Papers has a verified paper entry.

Ethical Promotion Rules

Do:

  • Share real results, runnable demos, and honest limitations.
  • Ask for benchmark results, reproducibility reports, and factor examples.
  • Post in communities only when the project is relevant and the post adds value.
  • Batch updates into releases instead of pushing tiny changes every few minutes.

Do not:

  • Buy stars, fork the project from fake accounts, or exchange stars.
  • Auto-post the same message across many communities.
  • Send cold direct messages asking for stars.
  • Overstate trading performance or imply financial advice.

Weekly Growth Loop

Every week:

  1. Ship one visible improvement: demo, benchmark, docs, issue, or release note.
  2. Turn it into one post and one GitHub discussion or issue.
  3. Ask for one concrete contribution: benchmark, public-data reproduction, factor request, or doc fix.
  4. Track stars, forks, issues, PRs, and which posts brought useful contributors.

The aim is not viral noise. The aim is to make the repository easier to trust, easier to run, and easier to recommend.