Stock & Investment Analyzer

Analyze stocks, companies, and investment opportunities using financial market data. Provide company profiles, technical analysis, fundamental analysis, and portfolio insights.

When to Use

• User wants to analyze a specific stock or company
• User asks about financial metrics, earnings, or valuations
• User wants to compare investment options
• User needs portfolio analysis or allocation advice
• User asks about market trends or sector performance

When NOT to Use

• Tax-specific questions (use tax-reviewer skill)
• Personal budgeting (use budget-planner skill)
• Insurance coverage (use insurance-optimizer skill)

Data Sources (Use These — Don't Guess)

Python libs (run directly, no API key):

import yfinance as yf
t = yf.Ticker("AAPL")
t.info              # P/E, market cap, beta, 52w range, margins
t.financials        # income statement (4yr)
t.balance_sheet     # debt, cash, equity
t.cashflow          # FCF, capex
t.history(period="1y")  # OHLCV for technicals
t.institutional_holders # 13F ownership

Screening:

finvizfinance

lib — filter S&P 500 by sector/valuation/signals. Finviz.com directly for heatmaps and insider tables.

Primary filings: Start from the EDGAR filing index:

webFetch("https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK={ticker}&type=10-K")

. This returns a list of filings — find the most recent 10-K and

webFetch

its "Documents" link to reach the actual filing. Read Item 1A (Risk Factors) and Item 7 (MD&A) — this is where management admits problems.

Insider activity:

webFetch("http://openinsider.com/screener?s={ticker}")

— look for cluster buys (multiple execs buying same week) and P-code open-market purchases (insider paid cash at market price — strongest signal). Ignore option exercises (M-code) and 10b5-1 scheduled sales.

Short interest: webSearch

"{ticker} short interest fintel"

— >20% of float = crowded short, squeeze risk either direction.

Methodology

Step 1: Pull the Data (Python)

Run

yfinance

to get fundamentals + 1yr price history. Compute 50/200 SMA, RSI(14), and current price vs 52w high. Takes 10 lines of pandas.

Step 2: Fundamental Analysis

Valuation (compare to sector median, not S&P):

• P/E — meaningless alone; flag if >2× sector median
• PEG — <1.0 = growth at reasonable price; >2.0 = priced for perfection
• EV/EBITDA — better than P/E for capital-intensive or leveraged cos
• P/S — only metric for unprofitable growth; >20× = needs hypergrowth to justify
• FCF yield (FCF/market cap) — >5% = genuinely cheap; negative = burning cash

Quality red lines (practitioner heuristics):

• Revenue growing but FCF shrinking → earnings quality problem, dig into receivables
• Debt/EBITDA >4× → one bad year from covenant breach
• Gross margin compressing 3+ quarters → losing pricing power
• Stock-based comp >15% of revenue → dilution machine (common in SaaS)
• Goodwill >50% of assets → acquisition-heavy, writedown risk

Step 3: Technical Context (Not Prediction)

Compute in pandas — don't just describe:

• Price vs 50/200 SMA: below both = downtrend, don't catch knives
• Golden cross (50 crosses above 200) = trend confirmation, not entry signal
• RSI(14): >70 overbought / <30 oversold — only useful at extremes + divergence
• Volume: moves on 2×+ avg volume are real; low-volume moves fade
• % off 52w high: >30% drawdown in an uptrending market = something broke

Step 4: The Retail Edge — Signals Institutions Ignore

• Insider cluster buys (OpenInsider): 3+ insiders open-market buying within 2 weeks is the single highest-conviction public signal. Research shows insider buys outperform; sells mean nothing (taxes/divorces/yachts).
• Buying the dip: insider P-code purchase after >10% drop = management disagrees with the market
• Short squeeze setup: short interest >20% + days-to-cover >5 + any positive catalyst
• Unusual options: webSearch

  "{ticker} unusual options activity"

  — large OTM call sweeps before earnings sometimes leak info

Step 5: Comparative Table

Build a pandas DataFrame with peers side-by-side: P/E, PEG, rev growth, gross margin, FCF yield, debt/EBITDA. The outlier in either direction is your thesis.

Step 6: Web Research — Find Existing Analyst Reports and News

Use web search aggressively. Before writing the report, gather real external research to cite:

webSearch("[ticker] analyst report 2026")
webSearch("[ticker] earnings analysis site:seekingalpha.com")
webSearch("[ticker] bull case bear case site:seekingalpha.com OR site:fool.com")
webSearch("[company] investor presentation 2026 filetype:pdf")
webSearch("[ticker] price target consensus")
webSearch("[ticker] industry outlook [sector]")
webSearch("[company] competitive landscape")
webSearch("[ticker] short interest thesis")

Source hierarchy (cite all of these in the report):

Source	What you get	How to cite
SEC EDGAR (10-K, 10-Q, 8-K)	Primary financials, risk factors, MD&A	"Source: [Company] 10-K FY2025, Item 7"
Earnings call transcripts	Management commentary, guidance	"Source: Q4 2025 Earnings Call, CEO remarks"
Sell-side research (via SeekingAlpha, TipRanks)	Price targets, consensus estimates	"Source: TipRanks consensus, 12 analysts"
Industry reports	TAM, growth rates, competitive dynamics	"Source: [Firm] [Industry] Report, [Date]"
Company investor presentations	Management's own bull case, KPIs	"Source: [Company] Investor Day 2025"
News (Reuters, Bloomberg, CNBC)	Catalysts, M&A, regulatory	"Source: Reuters, [Date]"

Use

webFetch

to pull actual content from SeekingAlpha articles, earnings transcripts, and investor presentations. Extract specific data points, quotes, and estimates to cite in the report.

Excel Output — Financial Models & Data Tables

For any financial models, comp tables, DCF spreadsheets, or data-heavy outputs, load the

excel-generator

skill to get professional Excel formatting with formulas, charts, conditional formatting, and data validation. The excel-generator skill has detailed guidance on openpyxl and xlsxwriter usage, number formatting, chart creation, and best practices for building polished .xlsx files.

Output — Professional Research Report as PDF

Do not output a markdown summary. Build a polished, professional equity research report as a React web artifact (like the resume/invoice skills) and export to PDF via Puppeteer. The report should look like a sell-side initiation note from Goldman, Morgan Stanley, or JP Morgan.

Report Structure (Sell-Side Format)

Follow the standard institutional research report format:

Page 1 — Cover / Executive Summary:

• Company name, ticker, exchange, current price, market cap
• Rating: Buy / Hold / Sell with price target and upside/downside %
• Investment thesis in 3-4 bullet points (the "elevator pitch")
• Key metrics snapshot: P/E, EV/EBITDA, revenue growth, FCF yield
• A 1-year price chart (generated via matplotlib/plotly, embedded as image)

Pages 2-3 — Investment Thesis:

• Bull case (with probability weighting if possible)
• Bear case (required — what kills this trade?)
• Key catalysts with expected timeline
• Competitive positioning / moat analysis

Pages 3-4 — Financial Analysis:

• Revenue breakdown by segment (with a stacked bar chart)
• Margin trends over 4+ quarters (with a line chart)
• FCF bridge / waterfall
• Balance sheet health (debt maturity, liquidity)
• Peer comparison table (pulled from yfinance for 3-5 peers)

Page 5 — Valuation:

• DCF model summary (show assumptions: WACC, terminal growth, revenue CAGR)
• Comparable company analysis table
• Historical valuation range (P/E or EV/EBITDA band chart)
• Price target derivation

Page 6 — Technical Analysis:

• Price chart with 50/200 SMA overlay (generated via matplotlib)
• Volume analysis
• Key support/resistance levels
• RSI chart

Page 7 — Risks:

• Ranked by probability × impact
• Regulatory, competitive, execution, macro risks
• Specific to this company, not generic boilerplate

Final Page — Sources:

• Full citation list with dates for every external source referenced
• Disclaimer / not financial advice

Charts and Visualizations

Generate charts using matplotlib or plotly in Python, save as PNG, and embed in the React report:

import matplotlib.pyplot as plt
import yfinance as yf

# Price chart with SMAs
df = yf.Ticker("AAPL").history(period="1y")
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(df.index, df['Close'], label='Price', color='#1a1a2e')
ax.plot(df.index, df['Close'].rolling(50).mean(), label='50 SMA', color='#e94560', linestyle='--')
ax.plot(df.index, df['Close'].rolling(200).mean(), label='200 SMA', color='#0f3460', linestyle='--')
ax.set_title("AAPL — 1 Year Price History")
ax.legend()
ax.grid(alpha=0.3)
fig.savefig("price_chart.png", dpi=150, bbox_inches='tight')

# Revenue by segment bar chart
# Margin trend line chart
# Valuation band chart
# RSI chart

Generate at least 4 charts for the report: price history with SMAs, revenue/margin trends, peer valuation comparison, and one more relevant to the thesis.

PDF Generation

Use jsPDF (same approach as the resume skill) to generate the PDF with explicit point-based layout:

• new jsPDF({ unit: "pt", format: "letter" })

  — US Letter: 612×792pt
• Use 36pt margins (0.5in). Content area: 540w × 720h points.
• Track Y position as you render each element. When the next element would exceed

  PAGE_H - MARGIN

  , call

  doc.addPage()

  and reset Y to the top margin. Never let content silently overflow — always check before rendering.
• Embed chart PNGs via

  doc.addImage()

  — scale each chart to fit the content width while respecting remaining page height. If a chart won't fit on the current page, start a new page.
• Add a header (company name, ticker, page number) and footer on each page by hooking into page creation.
• Use a clean sans-serif font, navy/dark blue headers, and consistent spacing.
• The React web artifact is the preview — it reads the same report data and renders an HTML version. The PDF is the primary deliverable.

Styling Guidelines

• Header bar on each page with company name, ticker, and page number
• Data tables with alternating row shading, right-aligned numbers
• Charts at full column width with clear titles and axis labels
• Callout boxes for key insights ("Management guided 15% revenue growth in Q4 call")
• Source citations as footnotes or inline parenthetical references
• Professional typography: 11pt body, 14pt section headers, consistent spacing

Best Practices

1. Timestamp everything — state data pull date; yfinance prices are ~15min delayed
2. Sector-relative only — a 30 P/E is cheap in software, expensive in utilities
3. Label facts vs thesis — "FCF yield is 6%" (fact) vs "undervalued" (opinion)
4. Bear case required — every analysis must include: what kills this trade?
5. Position sizing reality — no single stock >5% for most retail portfolios; if conviction demands 20%, the conviction is the problem

Limitations & Disclaimer

• This is NOT financial advice. Informational analysis only. User is responsible for all investment decisions. Recommend consulting a licensed financial advisor before acting.
• yfinance scrapes Yahoo — occasionally breaks, data may lag filings
• Cannot access Bloomberg/FactSet/real-time Level 2
• Cannot execute trades or provide personalized portfolio allocation
• Past performance does not indicate future results; all equities can go to zero