QUANTITATIVE INVESTING GUIDE 量化投资指南
Quant Stock Screener: Rank Factors with Auditable Evidence Quant Stock Screener: Auditable Factors 量化选股器：用可审计证据进行因子排序 量化选股器：可审计因子排序
Build a transparent screen across quality, momentum, valuation, liquidity, volatility, and news risk. 围绕质量、动量、估值、流动性、波动率与新闻风险构建透明的选股流程。
What Is a Quant Stock Screener?
A quant stock screener ranks stocks using measurable factor signals. Investors use quality, momentum, valuation, liquidity, volatility, and news risk to build candidate universes and compare stocks with a repeatable framework.
This QVeris scenario uses quant stock screener as the search entry point, then connects it to the QVeris Quant Factor Screen skill. The workflow helps agents screen many stocks, explain factor-driven rankings, and return source-backed analyst memos.
什么是量化选股器
量化选股器使用可度量的因子信号对股票进行排序。投资者利用质量、动量、估值、流动性、波动率和新闻风险构建候选股票池，并通过可重复的框架比较股票。
本 QVeris 场景以“量化选股器”为搜索入口，并连接 QVeris 量化因子筛选能力。该工作流帮助 Agent 批量筛选股票、解释因子驱动的排名，并返回带有来源依据的分析备忘录。
What a Factor Screener Should Rank
A useful quant screen combines factor scores with source context, exclusions, and explainable ranking logic.
Quality Factors
Rank profitability, balance-sheet strength, earnings quality, margins, return on capital, and operating consistency.
Momentum Factors
Track price momentum, earnings momentum, estimate revisions, relative strength, and trend persistence.
Valuation Factors
Compare multiples, free cash flow yield, earnings yield, revenue multiples, and valuation dispersion.
Liquidity Factors
Screen trading volume, spread risk, float, market cap, turnover, and position sizing constraints.
Volatility Factors
Measure drawdown risk, realized volatility, beta, gap risk, and factor instability across regimes.
News Risk
Flag earnings events, filings, analyst changes, product news, regulatory risk, and unusual catalyst context.
因子选股器应该对哪些指标排序
实用的量化筛选需要结合因子得分、来源背景、排除条件和可解释的排序逻辑。
质量因子
评估盈利能力、资产负债表稳健性、盈利质量、利润率、资本回报率和经营稳定性。
动量因子
跟踪价格动量、盈利动量、预期修正、相对强弱和趋势持续性。
估值因子
比较估值倍数、自由现金流收益率、盈利收益率、营收倍数和估值离散程度。
流动性因子
筛选成交量、价差风险、流通股本、市值、换手率和仓位规模限制。
波动率因子
衡量回撤风险、已实现波动率、贝塔、跳空风险和不同市场阶段的因子稳定性。
新闻风险
标记财报事件、监管文件、分析师调整、产品新闻、监管风险和异常催化因素。
How QVeris Builds a Transparent Factor Table
QVeris keeps the workflow agent-native: discover data capabilities, inspect factor schemas, call the skill, then return structured ranking output.
// Example quant stock screener workflow goal: "Rank an investment universe by factor signals" discover: fundamentals, prices, liquidity, volatility, filings, news inspect: factor definitions, universe filters, output fields, evidence notes call: "QVeris Quant Factor Screen skill" output: ranked table, factor scores, exclusions, source notes, audit appendix
QVeris 如何构建透明的因子表
QVeris 保持 Agent 原生工作流：发现数据能力、检查因子 Schema、调用能力，然后返回结构化排序结果。
// 量化选股工作流示例 目标: "按因子信号对投资股票池排序" 发现: 基本面、价格、流动性、波动率、监管文件、新闻 检查: 因子定义、股票池过滤条件、输出字段、证据说明 调用: "QVeris 量化因子筛选能力" 输出: 排名表、因子得分、排除项、来源说明、审计附录
Where Quant Screening Fits
The same workflow can support equity research, portfolio idea generation, factor investing, and risk-aware stock ranking.
Candidate Universe Building
Screen many stocks into a smaller research list using quality, momentum, valuation, liquidity, and risk controls.
Factor Investing Research
Compare value, quality, momentum, low volatility, and liquidity signals before portfolio construction.
Stock Ranking Reviews
Return a transparent table showing factor scores, source notes, missing data, and why a stock moved up or down.
QVeris Quant Factor Screen Skill
Use the actual QVeris skill to screen stocks by quality, momentum, valuation, liquidity, volatility, and news risk.
量化筛选适合哪些场景
同一套工作流可用于股票研究、投资组合创意生成、因子投资和兼顾风险的股票排序。
构建候选股票池
利用质量、动量、估值、流动性和风险控制，将大量股票筛选为更小的研究清单。
因子投资研究
在构建投资组合前，比较价值、质量、动量、低波动和流动性信号。
股票排序复核
返回透明表格，展示因子得分、来源说明、缺失数据以及股票排名升降的原因。
QVeris 量化因子筛选能力
使用 QVeris 的实际能力，按质量、动量、估值、流动性、波动率和新闻风险筛选股票。
Static Stock Screener vs QVeris Factor Workflow
| Need | Static stock screener | QVeris factor workflow |
|---|---|---|
| Build a candidate list | Sort by fixed columns and manual filters | Ranks stocks with explainable factor logic and source notes |
| Explain rankings | Analyst manually interprets why a stock scored well | Returns factor contribution, evidence strength, and missing data |
| Blend signals | Fundamentals, price action, liquidity, and news are separated | Combines factors into a reusable workflow and analyst memo |
| Repeat at scale | Manual refresh slows down across universes | Reusable AI agent workflow for recurring factor screening |
静态股票筛选器与 QVeris 因子工作流对比
| 需求 | 静态股票筛选器 | QVeris 因子工作流 |
|---|---|---|
| 建立候选清单 | 依靠固定列和手动筛选条件排序 | 使用可解释的因子逻辑和来源说明对股票排序 |
| 解释排名 | 由分析师手动判断股票得分较高的原因 | 返回因子贡献、证据强度和缺失数据 |
| 融合信号 | 基本面、价格走势、流动性和新闻彼此分离 | 把多类因子组合为可复用的工作流和分析备忘录 |
| 规模化重复执行 | 跨股票池的手动刷新效率低 | 使用可复用的 AI Agent 定期执行因子筛选 |
Useful Factor Investing References
External references help readers understand factor investing, stock screening, and quantitative equity research.
Investopedia Factor Investing
Introductory reference explaining factor investing and common equity factors.
MSCI Factor Investing
Reference for factor indexes, factor definitions, and portfolio construction context.
Fama-French Data Library
Academic data library for factor research and equity factor analysis.
实用的因子投资参考资料
这些外部资料可帮助读者理解因子投资、股票筛选和量化股票研究。
Quant Stock Screener FAQ
What is a quant stock screener?
What is factor investing?
Why use AI agents for factor screening?
Which QVeris skill is connected to this page?
量化选股器常见问题
什么是量化选股器？
什么是因子投资？
为什么使用 AI Agent 进行因子筛选？
本页连接了哪项 QVeris 能力？
Build a Quant Stock Screener Agent
Use QVeris to connect factor investing, stock ranking, quality, momentum, valuation, liquidity, volatility, news risk, source notes, and audit-ready briefs in one workflow.
Open the Quant Factor Screen skill构建量化选股 Agent
使用 QVeris 在一个工作流中连接因子投资、股票排序、质量、动量、估值、流动性、波动率、新闻风险、来源说明和可审计简报。
打开量化因子筛选能力How to Evaluate Quant Screener Results
A quant stock screener should not be judged only by how many tickers it returns. For an AI investment research workflow, teams should inspect whether the screen explains the factor definition, reporting period, data freshness, ranking method, and exclusion rules. A transparent workflow also separates exploratory factor discovery from production portfolio decisions, so the agent can present candidates without overstating certainty.
QVeris-style capability routing helps because the agent can discover factor, fundamentals, price, filings, and news capabilities separately, inspect their schemas, and then combine them into a traceable screening workflow. That makes the screener more useful for research teams that need evidence and repeatability, not just a static watchlist.
如何评估量化筛选结果
评估量化选股器不能只看它返回了多少只股票。对于 AI 投资研究工作流，团队还应检查筛选结果是否说明因子定义、报告期、数据新鲜度、排序方法和排除规则。透明的工作流还应把探索性因子发现与生产投资组合决策分开，使 Agent 能够给出候选股票而不夸大确定性。
QVeris 式能力路由可以分别发现因子、基本面、价格、监管文件和新闻能力，检查各自的 Schema，再把它们组合成可追踪的筛选工作流。对于需要证据和可重复性的研究团队，这比静态观察列表更有价值。
Continue with Verified QVeris Destinations 继续使用经过验证的 QVeris 入口
Inspect the concrete Open Smart Stock Screener and its actual provider profile. Continue with QVeris CLI, Free 10-K API, or Free Crypto Price API when those workflows match the next task. 先检查具体的 打开智能选股 Tool及其实际 Provider 详情。后续任务匹配时，可继续阅读 QVeris CLI、免费 10-K API或免费加密货币价格 API。
Move from Research to a Verified Call 从研究进入经过验证的调用
Inspect the exact capability and provider contract before connecting it to a production workflow. 连接生产工作流之前，先检查准确的能力与 Provider 契约。
Open Smart Stock Screener 打开智能选股工具