quantitative-researcher

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Quantitative Researcher

When to Use

适用场景

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

在接触数据前，先构建研究问题、原假设和可证伪的结论
获取、核查授权并分析市场或宏观数据集的特征（缺失值、时效性、公司行为）
运行描述性和推断性统计，并记录假设前提
在工作流层面应用时间序列或面板方法（平稳性、自相关性、固定效应——按需使用）
结合清晰的经济直觉和可测试的预测，开展因子、信号或alpha研究
构建包含真实交易成本、时点化投资组合和偏差检查清单的回测框架
计算并解读风险指标（波动率、回撤、尾部风险）和压力市场状态
生成包含局限性、敏感性分析和不确定性区间的可复现Notebook或研究备忘录
说明哪些因素会改变结论——而非将点预测作为建议传达

When NOT to Use

不适用场景

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)

生产级ML流水线、特征存储、模型部署或MLOps →
```
data-scientist
```
、
```
ml-research-engineer-safeguards
```
、
```
ml-ops-engineer
```
高管BI仪表盘、KPI定义或数据仓库指标层 →
```
data-analyst
```
（若已安装）、
```
bi-analyst
```
、
```
analytics-data-engineer
```
股票首次覆盖、盈利叙事或卖方风格研究报告 →
```
equity-research
```
、
```
initiating-coverage
```
、
```
earnings-analysis
```
（若已安装）
SOX合规结账、日记账分录或GAAP财务报表 →
```
financial-statements
```
、
```
compute-accounting-manager
```
合法投资建议、适用性评估或监管申报 →
```
commercial-counsel
```
、合规类技能
交易执行、OMS或低延迟生产系统 →
```
senior-software-engineer
```
（若已安装）
产品A/B测试和实验平台设计 →
```
ab-testing-engineer
```
未结合量化因子/回测框架的文本情绪预测 →
```
sentiment-forecasting-engineer
```
、
```
sentiment-analysis-engineer
```
警报阈值和误报决策策略 →
```
anti-false-positive-decision-making
```
债券相对价值、曲线交易或发行人信用叙事 →
```
bond-relative-value
```
（若已安装）
未结合研究设计的比率分析和企业财务叙事 →
```
financial-analyst
```
（若已安装）

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)

需求	技能
经典机器学习、因果推断、生产模型评估	`data-scientist`
SQL探索、仪表盘、业务报告	`data-analyst` （若已安装）
财务比率、估值框架、投资者指标	`financial-analyst` （若已安装）
债券估值高低、利差分解、曲线背景分析	`bond-relative-value` （若已安装）
文本衍生情绪特征和预测流水线	`sentiment-forecasting-engineer`
实验设计、功效分析、随机化、结果解读	`ab-testing-engineer`
基于弱信号行动前的证据验证	`anti-false-positive-decision-making`
宏观压力测试和场景沟通（非交易类）	`scenario-war-room` （若已安装）
DCF/可比公司法股票工作底稿	`dcf-model` 、 `comps-analysis` （若已安装）

Core Workflows

核心工作流

1. Frame the research question

1. 构建研究问题

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

See
references/research_framing_and_data_quality.md
.

明确决策或学习目标（不能仅为“寻找alpha”却无机制说明）
定义研究群体、时间范围和频率（日线数据与日内数据的方法差异）
预先登记主要统计量或指标；列出次要和稳健性检验项
记录原假设和备择假设；明确哪些证据会推翻假设
提前列出数据需求和已知局限性

参考
references/research_framing_and_data_quality.md
。

2. Acquire and validate data

2. 获取并验证数据

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

See
references/research_framing_and_data_quality.md
.

记录供应商、版本、时点规则和调整策略（拆股、分红、总回报）
分析数据集特征：覆盖范围、缺口、重复项、时区对齐、投资组合文件中的生存偏差
尽可能与第二个数据源进行对账抽查
在分析前冻结研究快照（哈希值、日期范围、投资组合版本）

参考
references/research_framing_and_data_quality.md
。

3. Explore and model (descriptive → inferential)

3. 探索与建模（描述性→推断性）

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

See
references/statistics_time_series_and_panels.md
.

从描述性统计和可视化诊断（分布、异常值、断点）入手
根据依赖结构选择匹配方法（独立同分布 vs 时间序列 vs 面板数据）
报告效应量、置信区间和假设检验结果——而非仅提供p值
针对窗口大小、缩尾处理和样本排除进行敏感性分析

参考
references/statistics_time_series_and_panels.md
。

4. Factors, signals, and backtests

4. 因子、信号与回测

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

See
references/factors_signals_and_backtesting.md
.

将每个信号与经济逻辑和持有周期关联
仅使用时点化输入构建信号；记录滞后和发布延迟
结合交易成本、容量分析和换手率报告进行回测
核查前瞻偏差、生存偏差、选择偏差和过拟合（多重检验）

参考
references/factors_signals_and_backtesting.md
。

5. Risk, robustness, and regimes

5. 风险、稳健性与市场状态

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

See
references/risk_metrics_and_robustness.md
.

报告波动率、回撤和尾部风险指标，并明确窗口定义
结合已知局限性（非正态分布、样本量小）解读Sharpe及相关比率
按市场状态（波动率、利率、流动性）划分区间，并开展压力场景测试
适用时对比样本内与样本外结果，进行滚动向前测试

参考
references/risk_metrics_and_robustness.md
。

6. Deliver and document

6. 交付与文档

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

See
references/research_deliverables_and_ethics.md
.

交付可复现的成果（Notebook + 固定依赖版本 + 数据清单）
包含适合利益相关者阅读的局限性、假设前提和不确定性说明
将研究发现与落地实施或执行建议分开呈现
归档参数、随机种子和版本元数据

参考
references/research_deliverables_and_ethics.md
。

When to load references

何时加载参考文档

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`

主题	参考文档
角色边界与交付物	`references/quantitative_researcher_scope.md`
问题构建与数据质量	`references/research_framing_and_data_quality.md`
统计、时间序列、面板数据	`references/statistics_time_series_and_panels.md`
因子、信号与回测	`references/factors_signals_and_backtesting.md`
风险指标与稳健性	`references/risk_metrics_and_robustness.md`
交付物、可复现性、伦理	`references/research_deliverables_and_ethics.md`

quantitative-researcher

Original

Translation

Quantitative Researcher

Quantitative Researcher

When to Use

适用场景

When NOT to Use

不适用场景

Related skills

相关技能

Core Workflows

核心工作流

1. Frame the research question

1. 构建研究问题

2. Acquire and validate data

2. 获取并验证数据

3. Explore and model (descriptive → inferential)

3. 探索与建模（描述性→推断性）

4. Factors, signals, and backtests

4. 因子、信号与回测

5. Risk, robustness, and regimes

5. 风险、稳健性与市场状态

6. Deliver and document

6. 交付与文档

When to load references

何时加载参考文档