quantitative-researcher

name

quantitative-researcher

description

Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).

Quantitative Researcher

When to Use

• Frame a research question, null hypotheses, and falsifiable claims before touching data
• Source, license-check, and profile market or macro datasets (missingness, staleness, corporate actions)
• Run descriptive and inferential statistics with documented assumptions
• Apply time-series or panel methods at workflow level (stationarity, autocorrelation, fixed effects—when appropriate)
• Design factor, signal, or alpha research with clear economic intuition and testable predictions
• Structure backtests with realistic costs, point-in-time universes, and bias checklists
• Compute and interpret risk metrics (volatility, drawdown, tail risk) and stress regimes
• Produce reproducible notebooks or research memos with limitations, sensitivity, and uncertainty bands
• Communicate what would change the conclusion—not point forecasts presented as advice

When NOT to Use

• Production ML pipelines, feature stores, model serving, or MLOps → data-scientist, ml-research-engineer-safeguards, ml-ops-engineer
• Executive BI dashboards, KPI definitions, or warehouse metric layers → data-analyst (if installed), bi-analyst, analytics-data-engineer
• Equity initiation, earnings narrative, or sell-side style research reports → equity-research, initiating-coverage, earnings-analysis (if installed)
• SOX close, journal entries, or GAAP financial statements → financial-statements, compute-accounting-manager
• Legal investment advice, suitability, or regulatory filings → commercial-counsel, compliance skills
• Trading execution, OMS, or low-latency production systems → senior-software-engineer (if installed)
• Product A/B tests and experiment platform design → ab-testing-engineer
• Text sentiment forecasting without quant factor/backtest framing → sentiment-forecasting-engineer, sentiment-analysis-engineer
• Alert threshold and false-positive decision policy → anti-false-positive-decision-making
• Bond RV, curve trades, or issuer credit narrative → bond-relative-value (if installed)
• Ratio analysis and corporate finance storytelling without research design → financial-analyst (if installed)

Related skills

Need	Skill
Classical ML, causal inference, production model eval	data-scientist
SQL exploration, dashboards, business reporting	data-analyst (if installed)
Financial ratios, valuation framing, investor metrics	financial-analyst (if installed)
Bond richness/cheapness, spread decomposition, curve context	bond-relative-value (if installed)
Text-derived sentiment features and forecast pipelines	sentiment-forecasting-engineer
Experiment design, power, randomization, readouts	ab-testing-engineer
Evidence bars before acting on weak signals	anti-false-positive-decision-making
Macro stress and scenario communication (non-trading)	scenario-war-room (if installed)
DCF / comps equity workpapers	dcf-model, comps-analysis (if installed)

Core Workflows

1. Frame the research question

1. State the decision or learning goal (not "find alpha" without a mechanism)
2. Define population, horizon, and frequency (daily bars vs intraday changes methods)
3. Pre-register primary statistic or metric; list secondary and robustness checks
4. Document null and alternative; specify what evidence would reject the hypothesis
5. List data requirements and known limitations upfront

See references/research_framing_and_data_quality.md.

2. Acquire and validate data

1. Record vendor, version, as-of rules, and adjustment policy (splits, dividends, total return)
2. Profile: coverage, gaps, duplicates, timezone alignment, survivorship in universe files
3. Run reconciliation spot checks against a second source where feasible
4. Freeze a research snapshot (hash, date range, universe version) before analysis

See references/research_framing_and_data_quality.md.

3. Explore and model (descriptive → inferential)

1. Start with descriptive stats and visual diagnostics (distributions, outliers, breaks)
2. Choose methods matched to dependence structure (i.i.d. vs time series vs panels)
3. Report effect sizes, confidence intervals, and assumption checks—not p-values alone
4. Run sensitivity to window, winsorization, and sample exclusions

See references/statistics_time_series_and_panels.md.

4. Factors, signals, and backtests

1. Tie each signal to an economic story and holding period
2. Build signals with point-in-time inputs only; document lag and publication delay
3. Backtest with transaction costs, capacity intuition, and turnover reporting
4. Audit lookahead, survivorship, selection, and overfitting (multiple testing)

See references/factors_signals_and_backtesting.md.

5. Risk, robustness, and regimes

1. Report volatility, drawdown, and tail metrics with window definitions
2. Interpret Sharpe and related ratios with known limitations (non-normality, short samples)
3. Segment by regime (vol, rates, liquidity) and run stress scenarios
4. Compare in-sample vs out-of-sample and walk-forward where applicable

See references/risk_metrics_and_robustness.md.

6. Deliver and document

1. Ship a reproducible artifact (notebook + pinned deps + data manifest)
2. Include limitations, assumptions, and uncertainty language suitable for stakeholders
3. Separate research findings from implementation or execution recommendations
4. Archive parameters, random seeds, and version metadata

See references/research_deliverables_and_ethics.md.

When to load references

Topic	Reference
Role boundaries and deliverables	references/quantitative_researcher_scope.md
Question framing and data quality	references/research_framing_and_data_quality.md
Statistics, time series, panels	references/statistics_time_series_and_panels.md
Factors, signals, backtesting	references/factors_signals_and_backtesting.md
Risk metrics and robustness	references/risk_metrics_and_robustness.md
Deliverables, reproducibility, ethics	references/research_deliverables_and_ethics.md