Obesity Drug Stocks Dashboard Excel: GLP-1 Leader and Pipeline Tracker (2026)

Obesity drug stocks dashboard excel - if that brought you here, the goal is clear. You want one workbook that holds the GLP-1 leaders, the late-stage pipeline, the binary early-stage names, and the adjacent telehealth plays in one screener, with KPI tiles up top, scenario analysis on TAM and market share, and position sizing tied to your portfolio. This guide walks through a premium, dashboard-style template built for exactly that job, with live MarketXLS formulas powering every price, ratio, and return.

The obesity drug theme is the largest active pharmaceutical narrative of the decade. Eli Lilly's Zepbound and Novo Nordisk's Wegovy created a multi-billion dollar weekly-injection market essentially out of nothing in three years. The pipeline behind them, from oral GLP-1 small molecules to dual and triple agonists, is where the next leg of share dispersion will be decided. A static screener cannot keep up. A premium dashboard with scenario inputs, conditional formatting, and live formulas can.

Quick reference: obesity drug stocks at a glance

The table above is the screener inside the template. The dashboard adds price, market cap, returns, margins, share assumptions, scenario sensitivity, and a composite score, all formatted with color scales, data bars, and icon sets so the standout names jump off the page.

Why obesity drug stocks need a premium dashboard

The GLP-1 obesity universe is unusual in three ways that make a regular screener inadequate.

First, the cap range is enormous. LLY and NVO together are worth more than a trillion dollars at recent prices. Late-stage pipeline names like AMGN and PFE are mega caps. Pure-play names like VKTX, ALT, and TERN are small caps, with market caps in single-digit billions or under one billion. Generic plays like HKMPY are smaller still. A flat screener that does not bucket these by category is hard to interpret because share assumptions, conviction sizing, and trade structure differ across the buckets.

Second, the valuation framework is forward-looking. The interesting question is not what each company earned last quarter. It is what slice of the 2030 GLP-1 obesity market each company captures, multiplied by the implied gross margin, multiplied by the multiple investors are willing to pay. That calculation needs a scenario engine, not a static cell. The premium template builds three scenarios (Conservative, Base, Aggressive) that vary 2030 TAM from $85 billion to $180 billion and reallocate share across the categories. The pipeline names are worth more in the Aggressive scenario, but so are the leaders. The shape of the curve matters.

Third, position sizing has to handle binary catalysts. PIPELINE-E names like VKTX, ALT, and STRC are essentially defined-risk speculations against a Phase 2 readout. They can double on a positive readout or lose half their value on a negative one. The conviction multipliers in the template push allocation higher for the leaders (1.40x) and lower for the binary names (0.60x), with everything bounded by the max-per-name cap on the Inputs sheet.

Put those three points together and the result is a workbook that has to do market sizing, scenario sensitivity, screener mechanics, conviction-weighted position sizing, and correlation analysis. That is exactly what the premium template was built for.

What is inside the obesity drug stocks dashboard template

The template has ten sheets, each with a single job. The design is consistent across all of them, with a navy and blue color palette, gold accents on input cells, frozen panes, and conditional formatting wherever a ranking is at stake.

1. Cover

The first sheet a user opens. Large navy banner with the workbook title, the edition (Static Sample or Live MarketXLS Formula), the data-as-of date, the universe description, and a table of contents that links every other sheet. Branded with the MarketXLS website and the demo booking URL. Gridlines hidden so the page reads as a presentation cover, not a worksheet.

2. How To Use

A step-by-step tutorial covering inputs, the dashboard, the scenario engine, the strategy playbook, and the correlation matrix. Includes a formula notes block listing every MarketXLS function used in the workbook, with the exact syntax and a one-line description. This is the page to send to a colleague who needs to onboard in five minutes.

3. Dashboard

The headline sheet. KPI tile row across the top with five tiles: Approved Leaders, Late Pipeline Count, Early Pipeline Count, Leader Share Percent, and Average Year-to-Date Return. Below the tiles, the obesity drug screener: 18 names across 17 columns covering ticker, name, category, price, market cap, lead program, phase, current and 2030 share assumption, returns, margins, ROE, TAM exposure rating, and a composite score. Conditional formatting on five columns: data bars on returns, color scale on 2030 share and operating margin, 5-arrow icon set on TAM exposure, and a 3-color scale on the composite score. Two charts below the screener: a bar chart of stocks by category and a pie chart of illustrative 2030 market share.

4. Inputs & Controls

The yellow-cell control sheet. Portfolio size, obesity sleeve weight, risk tolerance (1 to 5, data-validation dropdown), max position per name, scenario toggle (Conservative / Base / Aggressive, dropdown), 2030 TAM assumption, leader-duo share, late pipeline share, slippage assumption, and three conviction multipliers (Leader, Pipeline-L, Pipeline-E). Every other sheet reads these cells. Below the inputs, a watchlist table with a Yes / No dropdown on each ticker so users can include or exclude names from their personal universe.

5. Market Share Scenarios

The scenario engine. Three rows for Conservative, Base, and Aggressive. Each row holds the 2026 TAM, the 2030 TAM, the implied CAGR, and the share splits across Leader, Pipeline-L, Pipeline-E, Adjacent, and Generic. Two implied revenue columns at the right show what LLY and NVO 2030 obesity revenue would look like under each scenario, assuming a 69 / 31 split of the leader pool. Below the scenarios, a per-name implied 2030 revenue table for the entire universe, with a revenue multiple cross check that flags which late pipeline names are already priced for success. Data bars on the implied revenue column for visual scan.

6. Strategy Playbook

Per-category trade structure. Five direction modes: CORE LONG for leaders, SWING LONG for late pipeline, WATCH-ENTRY for early pipeline, TACTICAL LONG for adjacent, and CORE LONG for generic. Each row holds entry price, target, stop, break-even, hold days, risk per share, reward per share, and R:R. Color scale on R:R highlights the cleanest setups. Six playbook notes below the table explain how to think about each category's catalysts: earnings and prescription trends for leaders, Phase 2 and Phase 3 readouts for late pipeline, binary readouts for early pipeline, subscriber growth for telehealth, and patent expiry dynamics for generics.

7. Portfolio Allocation

Position-sizing calculator that reads everything from the Inputs sheet. Equal weight column, conviction weight column (different multiplier per category), dollar allocation column capped by the max-per-name limit, share count, direction, pre-slippage and post-slippage dollar exposure. Totals row at the bottom shows total sleeve dollars deployed, total conviction weight (should be near 100 percent if multipliers balance), and total post-slippage exposure. The conviction multipliers are read live from the Inputs sheet, so changing them on one screen recalculates the entire allocation table.

8. Correlation Matrix

Each obesity drug stock against seven reference ETFs: XLV (Health Care Select Sector), IBB (Nasdaq Biotech), XBI (SPDR Biotech), XPH (SPDR Pharmaceuticals), SPY, QQQ, and IWM. The matrix is color-coded with a 3-color scale (red for negative, amber for neutral, green for high positive). In the live template, each cell pulls from StockReturnCorrelation with a 30-day window; in the sample, illustrative values are pre-filled to show how the matrix reads. The early pipeline names correlate most tightly with XBI, the leaders track XLV and XPH, and the adjacent telehealth plays correlate with IWM (small-cap factor) rather than the pharma ETFs.

9. Methodology

A one-page explainer of GLP-1 mechanism, dual and triple agonist platforms, oral GLP-1 catalysts, TAM framing, market share assumptions, pipeline phase classification, risk factors, position sizing, the catalyst calendar, and limitations. Designed to be read by someone who has never analyzed a biotech name before.

10. Glossary & Disclaimer

Definitions for every term in the workbook (GLP-1, GIP, semaglutide, tirzepatide, dual agonist, oral GLP-1, amylin, MASH, MC4R, TAM, formulary access, Phase 1 / 2 / 3, CVOT, PDUFA date, XLV / IBB / XBI / XPH, Conviction Weight, R:R), followed by an educational-only disclaimer that flags drug names as trademarks of their respective owners and reminds users that drug development outcomes are uncertain.

How the GLP-1 market sizing scenarios work

The scenario engine is the single most useful part of the template. It forces a user to be explicit about three assumptions: how large the 2030 TAM is, how much share the duopoly holds, and how much share leaks to the late and early pipeline.

The Conservative scenario assumes the TAM only grows from $48 billion in 2026 to $85 billion in 2030 (roughly 15 percent CAGR), with LLY and NVO together holding 80 percent of the market. This is a world where oral GLP-1 entrants struggle with tolerability or efficacy, formulary access remains restrictive, and the duopoly converts existing weekly-injection patients into multi-year refills. The implied 2030 obesity revenue for LLY is $46.9 billion and for NVO is $20.9 billion under this scenario.

The Base scenario takes TAM to $130 billion (28 percent CAGR), with the duopoly retreating to 65 percent and late-stage pipeline names taking 22 percent. This is the world where MariTide, danuglipron, VK2735, pemvidutide, and CT-388 all read out positively and capture commercial share, with no single late-stage entrant approaching the leaders. LLY implied 2030 revenue is $58.3 billion, NVO is $25.9 billion.

The Aggressive scenario takes TAM to $180 billion (39 percent CAGR), with the duopoly falling to 52 percent and the late pipeline expanding to 28 percent. This is the disruptive case, where oral GLP-1 entrants materially expand the patient population by removing the injection barrier, and where dual and triple agonist platforms compete head-to-head with tirzepatide on efficacy. LLY implied 2030 revenue is $64.6 billion, NVO is $28.7 billion. Note that both leaders still grow under the Aggressive case because TAM expansion outpaces the share loss.

The scenario engine highlights an under-appreciated point: the leaders are not strictly losers if pipeline names succeed, provided TAM expands enough. That is what makes the obesity drug theme defensible as a multi-name basket rather than a binary leader-versus-pipeline bet.

The MarketXLS formula stack behind the dashboard

Every data cell in the live template is a MarketXLS formula. Here are the building blocks, all verified against the MarketXLS Function Reference.

Price, market cap, classification

=QM_Last("LLY")
=MarketCapitalization("LLY")
=Sector("LLY")
=Industry("LLY")
=Name("LLY")

These four formulas populate the ticker, name, sector, industry, current price, and market cap columns on the dashboard. MarketCapitalization returns dollars; the workbook divides by 1,000,000,000 to display billions.

Returns

=StockReturnOneYear("LLY")
=StockReturnSixMonths("LLY")
=StockReturnThreeMonths("LLY")
=FiftyTwoWeekHigh("LLY")
=FiftyTwoWeekLow("LLY")

The Dashboard sheet uses one-year and six-month returns by default. The strategy playbook also references the 52-week high as a soft upside reference for swing trades around readouts.

Margins, profitability, valuation

=GrossMargin("LLY")
=OperatingMargin("LLY")
=ReturnOnEquity("LLY")
=PERatio("LLY")
=Revenue("LLY")
=Beta("LLY")

Margins matter because the obesity drug class is unusually high gross margin (north of 80 percent for the leaders). The Score column on the dashboard rewards operating margin alongside 2030 share assumption and TAM exposure.

Correlation

=StockReturnCorrelation("LLY","XLV",30)
=StockReturnCorrelation("LLY","XBI",30)
=StockReturnCorrelation("LLY","XPH",30)
=StockReturnCorrelation("LLY","SPY",30)

These power the correlation matrix. The 30-day rolling window keeps the correlation responsive to changes in the theme's macro driver, which can shift quickly around major medical conferences (ADA, EASD, ObesityWeek).

Historical prices

=QM_GetHistory("LLY")

The strategy playbook references historical prices for backtesting how the stock has reacted to prior readouts. This is a research input, not part of the dashboard front-end.

Every formula above is verified against the MarketXLS Function Reference. The template never invents a function name. Users are encouraged to cross-check via the in-platform docs before extending the workbook.

How to use the obesity drug stocks dashboard step by step

The fastest path through the workbook is the path the How To Use sheet maps out. In practice:

Step 1. Open Inputs & Controls. Set the portfolio size to your actual portfolio. Set the obesity sleeve weight to the fraction you want in this theme (10 percent is the default). Choose your scenario from the dropdown. Set the conviction multipliers if you disagree with the defaults.

Step 2. Switch to the Dashboard sheet. Read the KPI tiles for the 30-second view of the universe: how many approved leaders, how many late-stage pipeline candidates, average return year-to-date, and the leader duopoly's current share. Then scan the screener. The Score column and 2030 Share column are the two highest signal-to-noise columns; the data bars on YTD show momentum.

Step 3. Open Market Share Scenarios. Read the three scenarios. Identify which one matches your view, and look at the implied 2030 revenue for LLY and NVO under that scenario. Then look at the per-name implied revenue table to see which pipeline names are priced for success and which still have room.

Step 4. Switch to Strategy Playbook. For each name you care about, the playbook gives you entry, target, stop, break-even, and R:R. The R:R color scale will flag the cleanest setups. PIPELINE-E names will tend to have wider R:R because the targets and stops are wider, reflecting the binary catalyst.

Step 5. Open Portfolio Allocation. Read the dollar allocation column. If the totals row shows more conviction weight than 100 percent, the leader and pipeline-L multipliers are pushing the allocation above the equal-weight baseline; tune them down. If the totals show less than 100 percent, tune the multipliers up.

Step 6. Open Correlation Matrix. Look at the row for each name. If you already own XLV, names with 0.80+ correlation to XLV add limited diversification. Names with low correlation to XLV but high correlation to XBI are the pure pipeline plays.

Step 7. Read the Methodology and Glossary tabs once before using the workbook in anger.

That is the loop. Re-run it every time a major catalyst hits.

The state of obesity drug stocks as of June 2026

Year-to-date, the obesity drug names have diverged. LLY is up modestly into the high single digits, while NVO has had a difficult first half (down low-double digits) as the pipeline narrative has pulled some of the leader premium to the late-stage names. Pure-play pipeline names like VKTX are higher than the start of the year on positive Phase 2 oral readouts. Earlier-stage names like ALT, TERN, and STRC are down materially because Phase 2 timelines slipped and capital raises diluted the float.

Underneath the price action, the fundamental picture is straightforward. The TAM is growing, and the duopoly is still growing inside that TAM. But the marginal investor dollar is shifting from "buy the leaders" to "buy the leaders plus a basket of pipeline." That is exactly the framing the template is built for: not a single-stock pick, but a portfolio of category-bucketed exposures with conviction-weighted sizing.

The next twelve months hold meaningful catalysts. Amgen's Phase 3 MariTide readout. Pfizer's danuglipron Phase 2 data. Viking's VK2735 oral readout. Roche's CT-388 Phase 2 update. Each one will move both the specific name and the leader prices. The dashboard's strategy playbook is built to absorb those moves: the leader plays are core long positions held through readout volatility, while the late-pipeline plays are swing longs sized around the readout window.

FAQ: obesity drug stocks dashboard excel

What does an obesity drug stocks dashboard excel template include?

An obesity drug stocks dashboard excel template includes a screener of GLP-1 leaders and pipeline candidates, KPI tiles for the universe count and average return, scenario analysis on 2030 TAM and market share, conviction-weighted position sizing, a strategy playbook with entry and exit prices, and a correlation matrix versus healthcare and biotech ETFs. The MarketXLS version powers every price and ratio with live formulas like QM_Last, MarketCapitalization, GrossMargin, and StockReturnOneYear.

Which stocks are in the obesity drug stocks dashboard?

The dashboard tracks 18 names: two approved leaders (LLY, NVO), seven late-stage pipeline candidates (PFE, AMGN, VKTX, ALT, ZLDPF, AZN, RHHBY), five early-stage pipeline names (TERN, RYTM, STRC, MRK, NVS), two adjacent telehealth plays (HIMS, WW), and two generic exposures (TEVA, HKMPY). Each is bucketed by category and stage, with the lead program, mechanism, and phase listed in the screener.

How does the GLP-1 market share scenario work?

The scenario engine reads three explicit assumptions: 2030 GLP-1 obesity TAM in billions, the leader-duo combined market share, and the late-stage pipeline share. Three pre-built scenarios (Conservative $85B TAM at 80 percent leader share; Base $130B at 65 percent; Aggressive $180B at 52 percent) reallocate share across categories and compute implied 2030 obesity revenue for each name. Users can override the assumptions on the Inputs sheet and the whole workbook recalculates.

What MarketXLS formulas power the obesity drug dashboard?

The core formulas are QM_Last for live price, MarketCapitalization for market cap, Sector and Industry for classification, Beta for risk, GrossMargin and OperatingMargin and ReturnOnEquity for profitability, StockReturnOneYear and StockReturnSixMonths for performance, FiftyTwoWeekHigh and FiftyTwoWeekLow for range, and StockReturnCorrelation for the matrix. QM_GetHistory powers historical backtests of readout reactions. Every formula is verified against the MarketXLS Function Reference.

Is the obesity drug stocks dashboard for trading or long-term holding?

It is built for both. The Strategy Playbook categorizes positions as CORE LONG (leaders and generics, multi-quarter holds), SWING LONG (late pipeline, traded around readouts), WATCH-ENTRY (early pipeline, sized small as defined-risk speculations), and TACTICAL LONG (adjacent telehealth, sized around subscriber growth). The conviction multipliers on the Inputs sheet let users tilt the allocation toward longer-hold leaders or higher-turnover pipeline trades.

How do oral GLP-1 drugs change the obesity drug investment thesis?

Oral GLP-1 small molecules (Pfizer danuglipron, Lilly orforglipron, Roche CT-996, Structure GSBR-209, AstraZeneca AZD5004) compete on convenience versus weekly injections. The investment thesis is that oral entrants expand the TAM by lowering the access barrier, but also reallocate share away from the injectable duopoly. The dashboard's Aggressive scenario models this dynamic: TAM grows from $48B to $180B by 2030, but leader share falls from 85 percent to 52 percent. Both leaders still grow in absolute dollars under this scenario because TAM expansion outpaces the share loss.

Download the template

Download the templates:

• - Pre-filled with illustrative values, every data cell has a comment showing the MarketXLS formula that produces it.
• - Live-updating dashboard with QM_Last, MarketCapitalization, GrossMargin, StockReturnOneYear, and StockReturnCorrelation feeding every cell.

Both files are free. The sample version is the right starting point to see how the workbook reads; the premium template version is the right one for ongoing use as your personal obesity drug dashboard.

The bottom line

A premium obesity drug stocks dashboard excel template is the right way to track a theme that spans mega-cap leaders, mid-cap late pipeline, small-cap binary catalyst names, telehealth, and generics in one place. The KPI tiles give the 30-second read. The scenario engine forces explicit assumptions about TAM and share. The strategy playbook structures the trade per category. The portfolio allocator sizes positions with conviction multipliers. And the correlation matrix flags which names actually diversify a healthcare book.

The two downloadable files do all of this. The static sample lets you see the layout; the live MarketXLS version updates every price, ratio, and return on the next refresh. Build the dashboard once, set your scenario assumptions and conviction multipliers, and the workbook becomes your living view of the obesity drug theme.

To explore the broader MarketXLS platform, visit marketxls.com or book a demo to see the full feature set including live streaming data, options analytics, portfolio tracking, and the function library that powers this template.