Quantitative Research
Stress-test systematic trading ideas with rigorous backtests and walk-forward validation before risking capital or shipping a quant stack.
Overview
Quantitative Research is an agent skill most often used in Validate (also Build, Operate) that guides systematic trading research—from backtesting and alpha validation through factor models and walk-forward testing—with
Install
npx skills add https://github.com/omer-metin/skills-for-antigravity --skill quantitative-researchWhat is this skill?
- Persona-driven quant research workflow: backtesting pitfalls, alpha validation, factor models, and stat arb framing
- Emphasis on statistical rigor: t-stats, Sharpe, p-values, walk-forward and out-of-sample discipline
- Covers regime detection, transaction costs, and cautious ML-for-finance guidance
- Explicit skepticism of backtest overfitting and look-ahead bias (production-minded defaults)
- Triggers on backtest, alpha, factor model, statistical arbitrage, mean reversion, momentum, walk forward, regime detecti
Adoption & trust: 1.8k installs on skills.sh; 89 GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have a trading idea that looks profitable in a quick backtest but you cannot trust the numbers or know if it will survive costs, regime change, and out-of-sample reality.
Who is it for?
Indie quant builders, systematic traders, and agent-assisted researchers who want backtest discipline before live capital or automated execution.
Skip if: Casual stock tips, discretionary day-trading chat, or teams that only need a one-line indicator script with no validation workflow.
When should I use this skill?
User mentions backtest, alpha, factor model, statistical arbitrage, quant research, systematic trading, mean reversion, momentum strategy, regime detection, or walk forward.
What do I get? / Deliverables
You leave with a statistically framed research plan—clear hypotheses, validation steps, and overfit checks—so you can either discard the signal or move into implementation and monitoring with explicit assumptions.
- Structured research plan with validation and OOS steps
- Bias and overfit checklist applied to the proposed strategy
- Interpretation framework (signals, costs, regime risks)
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Validate because the skill’s core job is turning a trading hypothesis into evidence (alpha, factors, regime tests) before you commit to build or live trade. Scope fits strategy definition, assumption checks, and out-of-sample gates—not one-off chart tinkering in isolation.
Where it fits
Define mean-reversion vs momentum hypotheses and required OOS tests before writing execution code.
Shape a backtest and transaction-cost model spec the agent will implement in your research repo.
Re-evaluate a live strategy after a regime shift using the same skepticism about in-sample Sharpe.
How it compares
Use instead of asking an agent for a “profitable strategy” without walk-forward, cost, and bias checks—this is a research methodology skill, not a signal feed or broker MCP.
Common Questions / FAQ
Who is quantitative-research for?
Solo and indie builders working on systematic trading, factor models, or stat-arb who want agent-guided rigor around backtests, alpha validation, and regime-aware skepticism—not casual investing advice.
When should I use quantitative-research?
In Validate when scoping whether an edge is real; in Build when designing backtest and factor pipelines; in Operate when revisiting strategies after drawdowns or regime shifts—especially when triggers mention backtest, alpha, walk forward, or regime detection.
Is quantitative-research safe to install?
Review the Security Audits panel on this Prism page and inspect the skill source in your repo before granting network or secrets access to any trading or data tools the agent suggests alongside it.
SKILL.md
READMESKILL.md - Quantitative Research
# 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. # Quantitative Research - Sharp Edges ## Look-Ahead Bias Is Almost Always Present ### **Id** look-ahead-bias-hidden ### **Severity** CRITICAL ### **Description** If your backtest looks too good, you're probably using future data ### **Symptoms** - Sharpe ratio above 3 - Perfect entry/exit timing - Backtest crushes reality ### **Detection Pattern** backtest.*sharpe.*[3-9]|perfect.*timing|too.*good ### **Solution** Common Sources of Look-Ahead Bias: 1. Index Membership - Testing "S&P 500 stocks" but using TODAY's membership - Stocks were added BECAUSE they performed well - Fix: Use point-in-time constituents 2. Earnings Dates - Using reported earnings date, not announcement date - Fix: Add 1-day lag minimum to all fundamental data 3. Adjusted Prices - Some splits/dividends adjusted using future info - Fix: Use unadjusted prices + adjust yourself 4. Data Alignment - Daily close vs intraday signal generation - Fix: If signal uses close, trade next open 5. ETF Creation - Testing ETF since "inception" but it tracks older index - Fix: U