Build an industry comp sheet Excel model with deep operational KPIs
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Rapid first-read earnings flash for a given company
Pre-earnings preparation report for the night before a company reports
Full earnings analysis with guidance tracking for a given company
Walk through initial setup and authentication for this Daloopa starter kit
| name | bull-bear |
| description | Bull/bear/base case scenario framework for a given company |
| argument-hint | TICKER |
Build a bull/bear/base case scenario framework for the company specified by the user: $ARGUMENTS
Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.
Follow these steps:
Look up the company by ticker using discover_companies. Capture:
company_idlatest_calendar_quarter — anchor for all period calculations below (see ../data-access.md Section 1.5)latest_fiscal_quarter../data-access.md Section 4.5Get the current stock price using get_stock_prices (see ../data-access.md Section 1.7). Pass company_id and dates for the 3 most recent calendar days — use the most recent returned close price. This is the anchor for scenario comparison: each scenario's implied value will be compared against this price to show upside/downside.
Calculate 8 quarters backward from latest_calendar_quarter. Pull:
Compute trailing 4-quarter totals for revenue, EBITDA, net income, EPS, and FCF — these are the baseline the scenarios build from.
Flag any one-time items that distort quarters.
First, think about what the most important KPIs are for THIS specific company based on its business model and what drives its valuation. For example:
Search for those specific KPIs by name and pull them. These are the building blocks for bottoms-up scenario math.
Also pull capital allocation data: share count, buyback amounts, dividends.
Search SEC filings/documents across multiple queries. If any search returns empty, try alternative keywords before giving up.
If consensus estimates are available (see ../data-access.md Section 3), note:
If consensus data is not available, skip this section.
For each scenario, build a bottoms-up revenue model showing key segment or product-level assumptions (e.g., units x ASP, subscribers x ARPU, segment growth rates). Don't just state a revenue range — show the math that gets there.
Don't default to 25/50/25. Assign probabilities informed by the most recent data points:
Be honest about which scenario is most likely. Don't default to a bullish framing or split the difference to seem balanced. If the data suggests the bear case is more probable, say so clearly. If the bull case requires multiple things to go right simultaneously, acknowledge that compounds the risk. The reader needs your honest assessment, not diplomatic equivocation.
Save to reports/{TICKER}_bull_bear.html using the HTML report template from ../design-system.md. Write the full analysis as styled HTML with the design system CSS inlined. This is the final deliverable — no intermediate markdown step needed.
The report should include:
All financial figures must use Daloopa citation format: <a href="https://daloopa.com/src/{fundamental_id}">$X.XX million</a>
Tell the user where the HTML report was saved.
Highlight: which scenario you believe is most likely and why, the key swing factors between bull and bear cases, and where you think the market is currently positioned (closer to bull, base, or bear). If the current stock price implies an overly optimistic or pessimistic scenario, flag it.