Rank the week by percentage move and the top three slots go to the same industry. SanDisk, Super Micro Computer and Seagate all posted double-digit gains between the August 7 and August 14 closes, and all three sit on the storage side of the artificial intelligence build-out. That kind of clustering is rare enough to be worth separating from the two declines, which had nothing in common with each other beyond being company-specific.
The rankings here come from the FMP EOD Bulk API. This article walks the five names and then sets out how the endpoint anchors a movers screen you can run the same way every week.
Key Takeaways
- All three top gainers came from storage, which makes this week's upside a sector event rather than five independent company stories.
- SanDisk's 35.4% gain is the largest single move in the combined large and mid-cap universe, and the pricing component of storage economics is doing much of the work behind it.
- Tapestry fell 20.6% in a week where the results themselves were well received, which is the clearest illustration that the direction of a move and the quality of a print are separate variables.
- AECOM's decline traces to a single project charge, a reminder that percentage rankings treat one-off items and structural deterioration identically.
Three Up, Two Down: The Week's Outliers
SanDisk Corporation (NASDAQ: SNDK)
Weekly Performance: +35.38%
SanDisk closed the week at $1,641.11 against $1,212.21 the previous Friday, the largest move in the screened universe by a comfortable margin. The fiscal fourth quarter landed in early August, and the storage cycle it reported into is one where memory pricing has been the dominant variable rather than unit demand. That distinction is the whole analytical question here: a substantial share of recent quarterly improvement has come from price realisation rather than volume growth, which is a very different earnings composition from one built on capacity additions.
Pricing-led cycles in memory have a well-documented shape, and the useful posture is to watch the composition of revenue growth rather than its rate. The demand backdrop from artificial intelligence workloads is real and appears durable in its direction, but the pass-through to reported results currently runs heavily through average selling prices, which historically respond faster in both directions than the underlying capacity picture does.
Trailing margin data from FMP's Financial Ratios TTM API makes this visible, because gross margin against its own historical range is the cleanest single read on how much of the improvement is price. Margin expanding well beyond the historical band while unit growth stays moderate describes a pricing environment rather than a structural change in the business, and those two conditions have different persistence characteristics.
Super Micro Computer (NASDAQ: SMCI)
Weekly Performance: +27.98%
The second-largest advance of the week took Super Micro from $31.13 to $39.84, following a fourth-quarter report and a forward outlook that landed well ahead of where expectations sat. The move came in two parts: a preliminary update that lifted the shares before the print, and the guidance itself, which extended the reaction rather than reversing it.
What separates this from the other two gainers is that the operative variable is the forward number, not the reported quarter. Super Micro sits at the assembly and integration layer of the artificial intelligence infrastructure stack, a position with genuine revenue leverage to accelerating deployment and correspondingly thin margins, which means the gap between a guided revenue figure and the profit it eventually produces is wider here than in most of the supply chain. A guidance-led re-rating therefore rests on execution assumptions extending well past the quarter just reported.
The relevant follow-up is language rather than arithmetic. FMP's Earnings Transcript API supplies management's own framing of the guidance, and in a business where the constraint has periodically been supply, component availability or working capital rather than demand, the qualifications attached to a forward number carry as much information as the number itself. Reading those against prior quarters is what separates a raised outlook from a repeated one.
Seagate Technology Holdings (NASDAQ: STX)
Weekly Performance: +19.77%
Fiscal-year results that beat expectations carried Seagate from $812.76 to $973.44 and prompted a wave of price target increases, several of them above the $1,000 mark. The company's heat-assisted magnetic recording transition has moved from a technology roadmap item into a reported contributor, which is what changes the character of the story.
The mass-capacity drive market has consolidated to the point where supply discipline is a structural feature rather than a cyclical accident, and the areal density gains from the technology transition affect cost per terabyte rather than simply unit shipments. That is a margin mechanism rather than a volume one, and it is more durable than pricing strength alone, though it remains dependent on the ramp progressing broadly as planned.
The immediate question after a move of this size is whether the analyst community has already extended past the fundamentals. Running FMP's Price Target Consensus API answers that directly, and dispersion is the more informative statistic than the mean: a tight cluster of raised targets indicates agreement on the mechanism driving the revision, while a wide spread around a similar average suggests the desks are modelling the ramp very differently from one another.
Tapestry, Inc. (NYSE: TPR)
Weekly Performance: -20.56%
Tapestry fell from $162.36 to $128.98, the sharpest decline of the week, and the circumstances make it the most instructive name on the list. The quarter itself was well received. Coach continued to perform strongly. The problem was elsewhere: the Kate Spade repositioning is taking longer than previously indicated, and the forward framing that accompanied the results did not support the multiple the shares had reached.
This is a brand-portfolio problem showing up as a company-level move, and consolidated reporting obscures exactly that. A group in which one brand is compounding and another is in an extended repair phase will show a blended figure that describes neither, and the market response indicates the blend is being taken apart rather than accepted. The variable that matters is the timeline on the smaller brand, not the trajectory of the larger one.
Brand-level contribution from FMP's Revenue Product Segmentation API separates the two, and tracking each across several quarters is what establishes whether the repositioning is progressing slowly or stalling. A screen that ranks Tapestry on a 20.6% weekly decline records the magnitude accurately and the cause not at all.
AECOM (NYSE: ACM)
Weekly Performance: -16.79%
A fifty-two-week low arrived for AECOM at $63.11, down from $75.84, after third-quarter fiscal 2026 results in which a project charge produced a quarterly loss on revenue of roughly $3.59 billion, accompanied by reduced full-year guidance. Both elements matter, and they are not the same kind of information.
A single project charge in an engineering and infrastructure services business is a known hazard of fixed-price contracting, and in isolation says little about the earnings power of the wider portfolio. The guidance reduction is the more consequential item, because it indicates the effect extends beyond the quarter in which the charge was recognised. The distinction to establish is whether this is one contract behaving badly or evidence of a pattern in how a class of work has been priced, and that difference is not visible in the headline decline.
The reference that matters is FMP's As Reported Income Statements API, since it preserves the charge as filed rather than as adjusted, and setting that against the adjusted presentation over several periods shows whether one-off items are genuinely one-off or recurring under different labels. In project-based businesses, the frequency of the exception is often the more informative statistic than the size of any individual one.
Storage Ran the Tape This Week
Three gainers, one industry. That is the finding, and it is worth more than the individual moves. SanDisk, Super Micro and Seagate occupy different layers of the same value chain, respond to overlapping demand drivers, and moved 35%, 28% and 20% in the same five sessions. When correlation clusters that tightly inside a single week, the screen has caught capital rotating into a theme rather than five separate research conclusions being reached simultaneously.
The two declines make the opposite point. Tapestry fell on a brand-level timeline inside an otherwise functioning portfolio; AECOM fell on a contract charge and the guidance revision that followed it. Neither has anything to do with the other, and neither reflects a sector move. That asymmetry is itself the week's pattern: concentrated, thematic strength on the upside, and dispersed, idiosyncratic weakness on the downside. Weeks with that shape tend to say more about where positioning is flowing than about how the underlying businesses are performing.
Confirming that requires stepping outside the movers list. Within the FMP data environment, the Market Sector Performance Snapshot API establishes whether the technology complex outperformed broadly or whether the strength was confined to the storage names specifically, which is the difference between a sector rotation and a narrow theme trade. Adding the Historical Market Cap API shows how much index-level weight actually moved, since three mid-sized advances and three mega-cap advances of identical percentage produce very different flow implications.
The last layer separates positioning from revision. The Financial Estimates API shows whether forward consensus is moving with the prices, and in a pricing-led cycle the risk is precisely that share prices re-rate ahead of the estimate base rather than alongside it. Pairing that with the Stock Grades API captures whether the analyst community is following the moves or leading them. Where estimates and ratings move together with price, the week's rotation has an underlying revision behind it. Where price has moved alone, the screen has surfaced flow, which is useful to know for a different reason.
Building a Repeatable Weekly Movers Framework with FMP Data
A reliable weekly movers screen starts with bulk pricing data tied to fixed dates rather than pulling symbols one at a time. Using two end-of-day snapshots — one at the beginning of the observation window and one at the end — gives you enough information to calculate weekly performance across an entire market universe in a relatively clean workflow. Once the raw data is collected, the process becomes less about retrieval and more about refinement: narrowing the universe, removing lower-quality signals, and ranking the moves that actually matter.
That is where the FMP EOD Bulk API becomes useful operationally. Instead of stitching together hundreds of individual requests, the endpoint returns daily pricing data for all listed symbols in a single response for a chosen date. Before running the workflow, make sure your API key is active.
1. Pull Bulk EOD Prices for Both Anchor Dates
Begin by hitting the EOD Bulk endpoint twice — once for the close at the start of the window, once for the close at the end:
|
https://financialmodelingprep.com/stable/eod-bulk?date=2024-10-22&apikey=YOUR_API_KEY |
Sample Response:
|
[ { "symbol": "EGS745W1C011.CA", "date": "2024-10-22", "open": "2.67", "low": "2.7", "high": "2.9", "close": "2.93", "adjClose": "2.93", "volume": "920904" } ] |
The response includes standard OHLC pricing, adjusted close, and trading volume for every symbol available on the requested date. For pure weekly return calculations, the symbol and close fields are generally sufficient — or adjClose if you want performance adjusted for splits and dividends. The remaining fields become more useful later when introducing liquidity screens, minimum-price filters, or volatility checks.
2. Filter Down to Your Defined Universe
The raw dataset is intentionally broad. Running a movers screen directly against the entire feed usually produces noisy results dominated by illiquid names rather than meaningful institutional activity. A cleaner approach is to overlay a defined universe filter. In this example, the screen uses the S&P 500 constituent list:
For that filter, call the S&P 500 Index API endpoint:
https://financialmodelingprep.com/stable/sp500-constituent?apikey=YOUR_API_KEY
After pulling the constituent data, keep only the symbols that appear in both bulk EOD snapshots and the S&P 500 list. The same framework can easily be adapted elsewhere depending on the use case. A small-cap workflow might substitute the Russell 2000, a sector analyst could isolate semiconductor or energy names, and a global strategy desk could combine multiple regional indexes. The mechanics stay consistent — only the universe definition changes.
3. Join, Compute, and Rank
Once both filtered snapshots are prepared, the remaining work is mostly calculation and sorting. Join the datasets on symbol, then compute percentage performance across the window:
Weekly Performance % = (End Close − Start Close) / Start Close × 100
From there, rank the results separately by strongest gainers and largest decliners. Most workflows also apply additional cleanup filters before interpreting the output — minimum trading volume thresholds, exclusion of newly listed stocks, market-cap requirements, or other liquidity constraints intended to reduce statistical noise. The exact thresholds vary by strategy, but the objective is consistent: isolate moves that likely reflect meaningful positioning activity rather than unstable price behavior in thinly traded names.
At that stage, the screen becomes more than a leaderboard of weekly winners and losers. The combination of bulk pricing data, liquidity filtering, and universe control turns the output into a more structured view of where capital rotated during the week — highlighting names where both the magnitude of the move and the underlying trading quality justify deeper research attention.
From Daily Screening to Institutional Research Process
What begins as a useful desk-level screening process often becomes more valuable once it is standardized across a broader research organization. Weekly movers analysis is rarely just about identifying outperformers and underperformers in isolation; in institutional settings, the larger objective is building repeatable frameworks that multiple analysts, strategists, and portfolio teams can reference consistently across coverage areas.
That shift matters because fragmented workflows create interpretation drift. One analyst may screen using raw percentage moves, another may apply liquidity thresholds differently, while a third may exclude event-driven distortions altogether. Over time, inconsistent methodologies make cross-team comparisons less reliable and reduce confidence in how signals are being interpreted internally. Standardizing the workflow — from universe construction to filtering logic and ranking methodology — creates a cleaner foundation for collaborative research and internal decision-making.
This is where centralized data infrastructure becomes operationally important rather than merely convenient. Bulk APIs, standardized constituent datasets, and structured financial statement feeds allow research teams to work from the same underlying inputs instead of maintaining disconnected spreadsheets or manually assembled watchlists. Once workflows become shared internally, they are easier to audit, easier to reproduce historically, and easier to adapt across sectors, regions, or strategy groups without rebuilding the process from scratch each time.
In practice, that often leads to broader integration into internal dashboards, systematic monitoring tools, and recurring research pipelines. A desk tracking AI infrastructure momentum may layer movers data against earnings revisions and backlog growth, while another team focused on macro-sensitive sectors may connect the same framework to balance-sheet leverage or insider transaction activity. The underlying process stays consistent even as the analytical overlays evolve by mandate.
For firms moving beyond isolated analyst workflows, Financial Modeling Prep's Enterprise Plan becomes relevant less as a standalone product decision and more as infrastructure that supports shared research environments, larger-scale data pulls, governance controls, and cross-team consistency. At that point, the value is no longer simply speed of access — it is reducing workflow fragmentation while making internally distributed research easier to validate, compare, and operationalize across the organization.
What the Leaderboard Leaves Out
A ranking tells you the size of a move and nothing about its durability, which is why a 20% decline on a well-received quarter and a 20% decline on a guidance cut sit side by side without distinction. Run weekly against a fixed universe, the FMP EOD Bulk API is most useful as the point where the questions start, particularly in a week where three of the five answers turned out to be the same one.
If you found this useful, you might also like: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Aug 3-7)
Disclosure: Signals Desk content is provided for informational and analytical purposes only and does not constitute investment advice or trade recommendations. The analysis reflects interpretation of market data and publicly disclosed or third-party information, including data accessed via Financial Modeling Prep APIs, at the time of publication. Signals discussed are probabilistic, can be wrong, and may change as market conditions and consensus data evolve. This content should be considered alongside broader research, individual objectives, and risk assessment.
