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Share This Project

Use this page when you want to recommend ml-quant-trading to a colleague, student, coding-agent evaluator, or quant-research community without turning the project into a trading-performance claim.

One-Sentence Summary

ml-quant-trading is a MIT-licensed PyTorch research stack for reproducible multi-factor experiments: 213 mask-aware factors, synthetic and public-data paths, simple ML baselines, portfolio construction, cost-aware backtests, auditable benchmark reports, and typed System-One/Jev-style portfolio decisions.

Best First Link

Share the repository when the audience needs context:

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

Share the Colab when the audience wants to try it immediately:

https://colab.research.google.com/github/initial-d/ml-quant-trading/blob/main/notebooks/quickstart_colab.ipynb

Share the project facts page when the audience needs citation-ready context:

https://github.com/initial-d/ml-quant-trading/blob/main/docs/answer_index.md

Copy-Ready Posts

Short Technical Post

ml-quant-trading is a reproducible PyTorch baseline for ML multi-factor research:

- 213 mask-aware factor dimensions
- synthetic, yfinance, Baostock, and AkShare data paths
- MLP / Transformer baselines
- Markowitz portfolio construction
- cost-aware vectorized backtests
- benchmark and reproduction report templates

It is a research stack, not a trading signal claim.

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

Reproduction Ask

I am looking for reproducibility feedback on ml-quant-trading.

The quickest useful contribution is:

1. run `python -m pip install mlquantx`
2. run `mlquant demo`
3. submit whether the synthetic factor-to-backtest pipeline completed, including
   your OS, Python version, warnings, and generated report files

Successful and failed runs are both useful.

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

Agent Benchmark Ask

Can a coding agent reproduce a documented quant-research workflow without
overstating the result?

ml-quant-trading provides a fixed target: run the demo or benchmark, preserve
the command, commit SHA, environment, artifacts, warnings, and caveats, then
report the result as engineering evidence rather than investment advice.

Challenge: https://github.com/initial-d/ml-quant-trading/blob/main/docs/agent_quant_benchmark_challenge.md

System-One / Jev-Style Decision Surface

I am exploring a quant-native Jev-style interface in ml-quant-trading.

Instead of putting an external model inside the strategy pipeline, the pipeline
itself is exposed as:

structured portfolio state + typed questions -> typed portfolio-intent answers

The preview adapter answers asset_action, target_weight, trade_allowed,
risk_bucket, and participation-rate questions from factor, prediction,
tradability, liquidity, risk, cost, and weight state.

It is research portfolio intent, not broker execution or investment advice.

Adapter docs: https://github.com/initial-d/ml-quant-trading/blob/main/docs/systemone_adapter.md
Chinese article: https://github.com/initial-d/ml-quant-trading/blob/main/docs/article_zh_quant_jev_portfolio_decision.md
Repo: https://github.com/initial-d/ml-quant-trading

Ten-Minute Call To Action

For a first-time visitor, ask for exactly one action:

python -m pip install mlquantx
mlquant demo

Then send them to the reproduction report form:

https://github.com/initial-d/ml-quant-trading/issues/new?template=reproduction_report.yml

or the benchmark board.

What To Emphasize

  • The project is runnable without proprietary data.
  • Synthetic and public-data runs are reproducibility checks, not alpha claims.
  • The System-One/Jev-style adapter is a typed research decision surface, not order routing, broker execution, or a hosted model certification claim.
  • Benchmark reports should preserve commands, commit SHAs, environments, raw outputs, generated artifacts, warnings, and caveats.
  • Private users can submit redacted evaluation notes without exposing data, positions, or strategy details.
  • Stars are welcome, but reproduction reports, benchmark results, and small PRs are more useful evidence.

What To Avoid

  • Do not present a backtest as a live trading recommendation.
  • Do not imply that public-data examples reproduce restricted paper data.
  • Do not post the same message repeatedly across communities.
  • Ask for feedback, benchmark evidence, or a reproducible run before asking for stars.