AI Stock Research Assistant for Agents面向 Agent 的 AI 股票研究助手
Build an AI stock research assistant that starts with industry valuation, reads market context, creates a screener candidate pool, and reviews ROE, PE, and share float before generating a research memo.
构建一个 AI 股票研究助手：先看行业估值和市场环境，再用 screener 形成候选池，随后检查 ROE、PE 和流通股结构，而不是直接给出买卖结论。
Why an AI Stock Research Assistant Needs a Workflow
为什么 AI 股票研究助手需要工作流
The original QVeris blog shows a practical lesson: a stock research AI should not jump from a ticker to an opinion. A useful assistant first frames the industry, checks sector behavior, reviews market movers, builds a candidate pool, and then asks whether the fundamentals and share structure support further review.
QVeris 原始博客展示了一个很实际的经验：股票研究 AI 不应该从一个 ticker 直接跳到观点。真正有用的助手应该先建立行业背景、检查板块表现、观察市场异动、形成候选池，再判断基本面和股本结构是否值得继续研究。
The AI Stock Research Assistant Workflow
AI 股票研究助手的工作流
Compare industry valuation before reading company-level moves.
先比较行业估值，再看个股变化。
Read sector performance, gainers, losers, and most active stocks.
读取板块表现、涨幅榜、跌幅榜和活跃股。
Build a candidate pool using market cap, volume, sector, and trading status.
用市值、成交量、行业和交易状态构建候选池。
Review ROE, earnings yield, and implied PE for selected companies.
复核 ROE、盈利收益率和隐含 PE。
Check share float so liquidity-driven moves are not misread.
检查流通股，避免把流动性异动误读成基本面机会。
What QVeris Adds to Stock Research AI
QVeris 能为 Stock Research AI 增加什么
An agent can search for industry valuation, screener, market mover, key metric, or share float capabilities before calling anything.
Agent 可以先发现行业估值、screener、市场异动、关键指标和 share float 等能力，再决定调用什么。
The workflow avoids pretending a screener supports ROE or PE directly when the schema does not provide those filters.
如果 screener 的 schema 不支持直接按 ROE 或 PE 筛选，工作流不会假装它可以做到。
The output is a research memo, candidate list, valuation context, and next questions, not investment advice.
输出应该是研究备忘录、候选列表、估值背景和下一步问题，而不是投资建议。
AI Stock Research Assistant vs Stock Picker
AI 股票研究助手 vs 荐股工具
| Dimension | Stock Picker | QVeris-style Research Assistant |
|---|---|---|
| Goal | Rank or recommend stocks quickly | Organize verifiable data before a human decision |
| Data flow | Often starts with a ticker or signal | Starts with industry valuation, market context, screener, fundamentals, and float |
| Best output | Buy/sell style conclusion | Candidate pool, research memo, evidence, and follow-up questions |
| Risk | Can over-explain market heat as company quality | Separates story, valuation, liquidity, and fundamental quality |
Useful Data Sources for This Workflow
这个工作流适合连接的数据源
A reliable AI investment research assistant should cite and compare multiple sources. For filings and float context, teams often reference SEC EDGAR. For financial metrics and screeners, developer teams may compare vendors such as Financial Modeling Prep, then use QVeris to route calls through a unified capability workflow.
可靠的 AI 投资研究助手应该能引用和比较多个来源。涉及文件和股本结构时，团队通常会参考 SEC EDGAR。涉及财务指标和筛选器时，开发者也会比较 Financial Modeling Prep 等数据源，再通过 QVeris 用统一能力工作流完成调用。
How to Evaluate an AI Stock Research Assistant
如何评估 AI 股票研究助手
A strong AI stock research assistant should make its reasoning path visible. Before it summarizes a company, the agent should show which data it used, whether the quote was delayed or real time, whether the financial metrics came from a statement or a model, and whether the result includes source URLs. This matters because research workflows are judged by evidence quality, not only by how fluent the final memo sounds.
好的 AI 股票研究助手应该让研究路径可见。在总结公司之前，Agent 应该说明使用了哪些数据、行情是延迟还是实时、财务指标来自报表还是模型、结果是否带有来源 URL。研究工作流的质量取决于证据质量，而不只是最终备忘录写得是否流畅。
For production teams, the key question is not “can the model write a stock summary?” The better question is “can the agent reliably discover the right market data, filings, news, fundamentals, and valuation capabilities for this exact task, inspect the schema, and call them without silently changing the user’s intent?”
对生产团队来说，关键问题不是“模型能不能写股票摘要”，而是“Agent 能不能为当前任务可靠发现合适的市场数据、文件、新闻、基本面和估值能力，先检查 schema，再在不悄悄改变用户意图的情况下完成调用”。
Related Guides for Stock Research Agents
股票研究 Agent 相关指南
If your workflow starts with price data, compare free and paid data sources first. If your workflow needs live monitoring, review real-time stock price APIs. If the assistant must explain market movement, add financial news and market data capabilities before asking the model to write commentary.
如果你的工作流从价格数据开始，应先比较免费和付费数据源。如果需要实时监控，应重点评估实时股价 API。如果助手要解释市场波动，则应在生成评论前加入金融新闻和市场数据能力。
Data Checklist for an AI Stock Research Assistant
AI 股票研究助手的数据检查清单
| Data area数据区域 | Why it matters为什么重要 | Agent behaviorAgent 行为 |
|---|---|---|
| Price and volume价格与成交量 | Shows whether the market reaction is unusual or just normal noise.判断市场反应是异常还是普通波动。 | Compare current price, range, volume, and sector context.比较当前价格、区间、成交量和板块背景。 |
| Fundamentals基本面 | Prevents the assistant from ranking companies on headlines alone.避免助手只根据新闻标题排序公司。 | Inspect ROE, margins, debt, growth, and valuation before summary.总结前检查 ROE、利润率、债务、增长和估值。 |
| Filings and events文件与事件 | Explains what changed and gives the memo a verifiable source trail.解释发生了什么，并为备忘录提供可验证来源。 | Cite filings, earnings dates, guidance, and corporate actions.引用文件、财报日期、指引和公司行动。 |
Production Risks for Stock Research Agents
股票研究 Agent 的生产风险
Stock research agents can sound confident even when the data path is weak. The most common risk is mixing delayed quotes with real-time language, old filings with current news, or peer comparisons from companies that are not actually comparable. A production assistant should display source age, data provider, and the assumptions behind every comparison.
股票研究 Agent 即使数据路径很弱，也可能表现得很自信。最常见风险是把延迟行情写成实时语气、把旧文件和当前新闻混用，或者用并不真正可比的公司做同行对比。生产级助手应展示来源时间、数据供应商和每个对比背后的假设。
This is where QVeris-style capability routing helps. The agent can discover the needed capability, inspect the input contract, call the tool, and then validate whether the returned fields are enough for the memo. If the answer is not strong enough, the agent should ask for clarification or add another source instead of inventing a conclusion.
这正是 QVeris 式能力路由的价值：Agent 可以先发现所需能力，检查输入契约，再调用工具，并验证返回字段是否足够支撑备忘录。如果答案不够可靠，Agent 应该请求澄清或增加来源，而不是编造结论。