Deep Stock Research Engine
Deep research: 7D, sourced, bull/bear
Deep Stock Research Engine
Deep research: 7D, sourced, bull/bear
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Description
Not a news summary, but an individual stock research report with a methodological framework. Enter a company (A-share/H-share/US stock name or code), and it will perform 10-15 bilingual web searches to produce a report based on a seven-dimension framework: ① Business Model – where money comes from and goes, with a "translated into plain language" version ② Revenue Structure – breakdown by business, growth engines and drags ③ Moat – mandatory answer on widening or narrowing, using market share, gross margin, and pricing power ④ Financial Quality – cash flow vs profit alignment, changes in receivables, inventory, and goodwill ⑤ Competitive Landscape – competitor comparison table and concentration direction ⑥ Valuation Framework – only data and calculation framework, no judgment of over/undervaluation ⑦ Risk List – sorted by impact, each with verifiable trigger signals Each report includes: a one-page bull/bear summary (with substantial evidence on both sides), a next-quarter tracking list, and data sources for each point. Three iron rules: · Each key number is traceable to official filings or authoritative media; if not found, labeled 'insufficient information', never fabricated · Facts, calculations, and inferences are labeled separately · No stock price predictions, no target prices, no buy/sell advice – even for 'should I buy', only bull/bear evidence is given Supports three perspectives: Understand the Business (default), Financial Checkup, and Company Comparison. Suitable for investment researchers, financial content creators, and serious investors who want to systematically understand a company.
Related Skills
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Stock Report Analyzer
Financial reports are a listed company's "physical exam report", but most investors feel lost when faced with pages of dense numbers: • 📝 Can't read the three financial statements — What do the balance sheet, income statement, and cash flow statement tell you? Which numbers matter most? • 🤔 Don't know what to look at — With dozens of pages, which key metrics should you focus on? • 📊 Lack an analysis framework — Even when you find the data, how do you judge good from bad and cross-check it? • 🏭 Industry standards differ — What counts as "good" varies completely by industry; manufacturing and internet companies can't be measured with the same yardstick. • ⏰ Time cost is too high — Manually looking up data, comparing peers, and calculating growth rates can take hours for one report. This skill was built to solve exactly these problems. It automates a professional financial analyst's framework, so you just need to enter a stock code to get a structured, logical, and beginner-friendly financial report interpretation. The tool's core value lies in its rigorous analysis framework, covering multiple dimensions from assessing growth quality to analyzing business models. It not only compares core metrics like revenue, gross margin, net margin, and cash flow, but also runs a quick Q&A on the seven key financial questions investors care about most. It clearly shows whether profits are turning into real cash, and whether receivables and inventory carry potential risks. By benchmarking against industry leaders, it helps you identify a company's true position in its industry chain. When generating the report, it also evaluates valuation levels, core strengths, and potential risks, and offers data-driven recommendations on what to watch. All analysis is presented in intuitive tables and plain, easy-to-understand language, turning complex accounting terms into concrete investment logic. Whether for daily review or deep research, it helps you quickly zero in on the core issue amid massive amounts of data, improving the efficiency and accuracy of your investment decisions.
Report Anomaly Graphic
Not a financial report rehash, but identifying where things really changed. Enter the name, ticker, report link, or PDF of an A-share, Hong Kong, or US-listed company. The tool automatically cross-checks official filings, compares key metrics on a like-for-like basis, and identifies growth accelerations or decelerations, margin shifts, cash flow anomalies, one-time items, and changes in management guidance. Each delivery includes: ① Source cards and key metrics table ② The most important report anomalies ③ Supporting evidence and risk checklist ④ Tracking indicators for the next quarter ⑤ A 3:5 or 9:16 investor long graphic ⑥ A ready-to-post short summary You can use it to: • Analyze Tencent's latest report, focusing on gaming, advertising, and capital expenditure • Compare NVIDIA's last two quarters to find accelerating or decelerating metrics • Check Kweichow Moutai's cash flow, contract liabilities, and channel inventory • Verify numbers and conclusions in a stock analysis article, then compile into a graphic All numbers must be traceable to original sources: company announcements, exchange filings, SEC, or company IR. Facts, calculations, and inferences are labelled separately. No fabricated missing data. No buy or sell recommendations. Ideal for investors, financial content creators, researchers, and corporate content teams.
ResearchMulti-Agent: A-Share Pick & IC
It's not an AI assistant, but a virtual investment research team. Common AI stock-picking tools suffer from three problems: fabricating financial figures and target prices, giving vague "bullish/bearish" remarks, and offering "buy" recommendations without clear reasoning. The Multi-Agent Investment Research Team tackles these with a three-pronged approach: 6 parallel roles, cross-validation, and mandatory source attribution. It convenes researchers, fundamental analysts, technical analysts, sentiment analysts, risk officers, and investment managers to work in parallel, deliberating like a real investment committee. What you get is not fuzzy opinions, but a professional research document with facts, signals, disagreements, risks, and every number traceable to its source. Two modes covering "researching a single stock" and "screening a batch of stocks" Mode A: Single-Stock Committee Deep Analysis — Just provide a stock (e.g., "Analyze BYD 002594"), and the skill automatically convenes a full investment committee: the researcher aggregates market data, financial reports, research reports, and industry chain positioning, presenting only objective facts; the fundamental analyst issues a financial health scorecard, key changes in the three financial statements, and PEG valuation; the technical analyst evaluates trends, moving averages, MACD, support and resistance levels, and provides a five-point buy signal hit table; the sentiment analyst scans institutional divergence, retail investor sentiment, and potential misinterpretations; the risk officer digs up counter-evidence, systematically refuting optimistic conclusions from other roles; finally, the investment manager, without adding new data, integrates everything to produce committee minutes and a one-page summary. Mode B: Multi-Condition Stock Screening — From a specified universe (e.g., CSI 300, a sector/theme basket, or your own stock pool), apply a three-layer funnel: L1 financial hard screen (three consecutive quarters of growth, ample cash flow, PEG<1 or huge increase in contract liabilities), L2 technical timing (base breakout, moving average golden cross, volume breakout, strong pullback on low volume, MACD crossing above zero line), L3 information validation (research report ratings and industry chain logic, eliminating "pure technical without fundamental basis" picks). After obtaining a candidate list, the top N stocks can automatically proceed to Mode A for deep analysis. What you will get Mode A delivers a fixed "five-piece set": ① Full analysis report integrating all six roles; ② Data source and evidence table, with each key conclusion mapped to "data → source → date"; ③ Meeting-style committee minutes (agenda → each role's view → disagreements → consensus → variables to track); ④ Risk list sorted by high/medium/low severity; ⑤ One-page investment manager summary condensing core logic, key variables, verification points, and confidence level. Mode B delivers: Candidate stock list table (ticker | name | triggered conditions | key data | source | trigger date) plus screening criteria and methodology description, optionally with the full five-piece set for top candidates. All outputs are saved as files with ticker and date in the filename for easy reuse and archiving.
Information
- Version
- v2
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
