two-sigma-ml-at-scale
Build trading systems in the style of Two Sigma, the systematic investment manager pioneering machine learning at scale. Emphasizes alternative data, distributed computing, feature engineering, and rigorous ML infrastructure. Use when building ML pipelines for alpha research, feature stores, or large-scale backtesting systems.
npx skills add majiayu000/claude-skill-registry --skill two-sigma --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Two Sigma Style Guide ## Overview Two Sigma is a systematic investment manager with ~$60B AUM, known for applying machine learning, distributed computing, and alternative data to financial markets. They operate like a technology company, investing heavily in data infrastructure, ML platforms, and research tooling. ## Core Philosophy > "We're a technology company that happens to be in finance." > "Data is the new oil, but only if you can refine it." > "The best model is worthless without the infrastructure to deploy it." Two Sigma believes that competitive advantage comes from data infrastructure and research velocity—the ability to test more ideas faster than competitors. ## Design Principles 1. **Data Platform First**: Build the platform, then the models. 2. **Feature Store**: Features are first-class citizens, versioned and shared. 3. **Reproducibility**: Every experiment must be reproducible. 4. **Scale Horizontally**: Design for 1000x more data than you have today. 5. **Research Velocity**: Reduce time from idea to tested hypothesis. ## When Building ML Trading Systems ### Always - Version everything: data, features, models, code - Store point-in-time snapshots (no lookahead
- Overview
- Core Philosophy
- Design Principles
- When Building ML Trading Systems
- Always
- Never
- Prefer
- Code Patterns
- Feature Store Architecture
- Distributed Backtesting
- Alternative Data Pipeline
- Model Monitoring and Drift Detection
- Mental Model
- Signature Two Sigma Moves
What does the two-sigma-ml-at-scale skill do?
Build trading systems in the style of Two Sigma, the systematic investment manager pioneering machine learning at scale. Emphasizes alternative data, distributed computing, feature engineering, and rigorous ML infrastructure. Use when building ML pipelines for alpha research, feature stores, or large-scale backtesting systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill two-sigma --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.