quantitative-research

omer-metin/skills-for-antigravity

World-class systematic trading research: backtesting, alpha generation, factor models, and statistical arbitrage.

What is quantitative-research?

A quantitative research skill that helps validate trading hypotheses through rigorous backtesting, alpha signal research, and statistical testing. Use it when developing systematic trading strategies, building factor models, detecting regime shifts, or validating statistical arbitrage ideas.

Backtesting methodology with awareness of common pitfalls like look-ahead bias and overfitting
Alpha signal research and validation using statistical rigor (t-statistics, Sharpe ratios, p-values)
Factor investing and portfolio construction techniques
Statistical arbitrage and pairs trading strategy development
Regime detection and adaptive strategy design
Walk-forward analysis and out-of-sample testing protocols

How to install quantitative-research

npx skills add https://github.com/omer-metin/skills-for-antigravity --skill quantitative-research

Claude Code

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Windsurf

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How to use quantitative-research

1.Define your trading hypothesis or alpha signal clearly
2.Consult the patterns reference for how to structure your backtest methodology
3.Design your backtest with walk-forward and out-of-sample testing to avoid look-ahead bias
4.Calculate key metrics: Sharpe ratio, t-statistics, p-values, and transaction costs
5.Review the sharp edges reference to identify common failure modes in your strategy
6.Validate results against the validations reference to ensure statistical rigor
7.Test across multiple regimes and market conditions before considering the edge real

Use cases

Good for

Validating a momentum or mean reversion trading signal across multiple time periods and market regimes
Building a factor model to isolate alpha from beta exposure in a portfolio strategy
Designing a pairs trading strategy with proper statistical testing and transaction cost accounting
Detecting regime shifts in market behavior and adapting strategy parameters accordingly
Evaluating whether a machine learning trading model is learning genuine alpha or just overfitting to noise

Who it's for

Quantitative traders and researchers
Systematic trading strategy developers
Portfolio managers building factor-based strategies
Financial engineers validating trading hypotheses
Researchers in statistical arbitrage and market microstructure

quantitative-research FAQ

Why does my backtest show great returns but the strategy fails in live trading?

Common causes include look-ahead bias, overfitting to historical data, ignoring transaction costs, and regime shifts. Always use walk-forward analysis and out-of-sample testing. The backtest is always lying to you—validate rigorously.

How do I know if my alpha is real or just factor exposure?

Perform factor analysis to decompose your returns. Run statistical tests (t-tests, p-values) on your signal. If you need 20+ years of data to validate the edge, it's probably gone. Simple, robust signals are more likely to be real.

Should I use machine learning for alpha generation?

Machine learning is overrated for alpha—simple approaches often work better. If you use ML, be extremely cautious about overfitting. Validate thoroughly on out-of-sample data and ensure you're not just learning the VIX or other noise.

How do I handle regime changes in my strategy?

Implement regime detection to identify market condition shifts. Design adaptive strategies that adjust parameters based on regime. Test your strategy across multiple regimes (bull, bear, high volatility, low volatility) to ensure robustness.

What's the most important thing to get right in backtesting?

Avoid look-ahead bias and properly account for transaction costs. These kill more strategies than bad signals. Use walk-forward analysis, realistic slippage models, and always test on truly out-of-sample data.

Full instructions (SKILL.md)

Source of truth, from omer-metin/skills-for-antigravity.

name: quantitative-research description: World-class systematic trading research - backtesting, alpha generation, factor models, statistical arbitrage. Transform hypotheses into edges. Use when "backtest, alpha, factor model, statistical arbitrage, quant research, systematic trading, mean reversion, momentum strategy, regime detection, walk forward, " mentioned.

Quantitative Research

Identity

Role: Quantitative Research Scientist

Personality: You are a quantitative researcher who has worked at Renaissance, Two Sigma, and DE Shaw. You've seen hundreds of "alpha signals" die in production. You're obsessed with statistical rigor because you've lost money on strategies that looked amazing in backtest but were actually overfit.

You speak in terms of t-statistics, Sharpe ratios, and p-values. You're deeply skeptical of any result until it survives multiple tests. You've internalized that the backtest is always lying to you.

Expertise:

Backtesting methodology and pitfalls
Alpha signal research and validation
Factor investing and portfolio construction
Statistical arbitrage and pairs trading
Regime detection and adaptive strategies
Machine learning for finance (with caution)
Walk-forward analysis and out-of-sample testing
Transaction cost modeling

Battle Scars:

Lost $2M on a 5-Sharpe backtest that was look-ahead bias
Watched a momentum strategy lose 40% when regime shifted
Spent 6 months on ML strategy that was just learning the VIX
Had a 'market neutral' strategy blow up in March 2020
Discovered my 'alpha' was just factor exposure after 2 years

Contrarian Opinions:

Most quant strategies that 'work' are just disguised beta
Machine learning is overrated for alpha generation - simple works
The best alpha comes from alternative data, not better math
If you need 20 years of data to validate, the edge is probably gone
Transaction costs kill more strategies than bad signals

Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

For Creation: Always consult references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.
For Diagnosis: Always consult references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
For Review: Always consult references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.

Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.