Some weeks are defined by broad market direction. Others are defined by the handful of stocks that reveal where capital is moving and why. This week's screen surfaced five names whose outsized gains and losses reflected shifting sentiment across AI infrastructure, electric vehicles, storage, and semiconductor hardware, offering a more useful snapshot of market positioning than headline index performance alone.
This analysis is built using FMP's EOD Bulk API, which makes it possible to rank weekly winners and losers across an entire market universe from just two end-of-day pricing snapshots. Beyond examining what this week's biggest movers may be signaling, we'll also walk through how the API can be used to build a structured weekly movers screen that can be adapted to different investment universes and research workflows.
Key Takeaways
- This week's biggest movers reflected selective repricing rather than broad market rotation, with AI, EV, semiconductor, and storage names reacting to company-specific developments instead of a single sector-wide trend.
- Looking beyond weekly returns provides a more useful research signal. Combining price action with financial statements, cash flow trends, and analyst expectations helps distinguish changing fundamentals from short-term market positioning.
- The FMP EOD Bulk API enables a repeatable screening process by ranking weekly gainers and losers across an entire market universe using just two end-of-day pricing snapshots before deeper fundamental analysis begins.
Five Names That Defined This Week's Tape
Sandisk Corporation (SNDK)
Weekly Performance: -16.53%
SanDisk posted the steepest decline among this week's featured names, falling 16.53% over the observation window. The move stood out not simply because of its magnitude, but because it occurred during a broader pullback across parts of the semiconductor and storage ecosystem, where investors appeared to rotate away from several hardware names after an extended period of strong performance. Recent market commentary also highlighted SanDisk as one of the largest drags on semiconductor indexes during the week, underscoring that the weakness was not isolated to a single company.
From a research perspective, sharp declines in memory and storage companies often warrant looking beyond price action. Investors typically examine whether expectations for pricing, enterprise demand, or capital spending have changed materially, or whether the move primarily reflects positioning after a strong run. To separate those possibilities, quarterly income statement trends alongside analyst estimate revisions provide useful context, helping determine whether earnings expectations are evolving in line with the stock's repricing or whether sentiment has simply reset faster than fundamentals.
Rivian Automotive, Inc. (RIVN)
Weekly Performance: +19.19%
Rivian was this week's strongest performer, gaining 19.19% as investors responded positively to operational developments rather than broad enthusiasm toward the EV sector. During the week, the company reported deliveries that exceeded analyst expectations while also raising its delivery outlook, reinforcing the market's focus on execution after several quarters in which production consistency remained under close scrutiny.
For companies at Rivian's stage, delivery numbers often carry more informational value than daily price fluctuations because they provide a direct measure of manufacturing execution and customer demand. The next layer of analysis typically extends beyond vehicle counts into cash flow statements, balance sheet liquidity, and vehicle delivery datasets to evaluate whether production improvements are translating into stronger financial resilience. Monitoring those metrics alongside future production updates offers a more complete framework than relying on share performance alone.
Palantir Technologies Inc. (PLTR)
Weekly Performance: +14.49%
Palantir advanced 14.49% during the week, continuing to attract attention as enterprise AI and government technology remained prominent themes across the software sector. Recent developments included announcements surrounding expanded AI initiatives and partnerships, while several analysts pointed to Palantir's ability to integrate multiple AI models within enterprise environments as an important competitive differentiator.
What makes Palantir notable from a market-structure perspective is that its valuation increasingly reflects expectations around sustained commercial adoption alongside its established government business. As a result, investors often evaluate not only headline contract announcements but also whether commercial customer growth, revenue mix, and operating leverage continue to support those expectations. Reviewing segment revenue, government versus commercial revenue breakdowns, and contract-related disclosures provides a more grounded way to interpret the company's momentum than focusing on individual news headlines in isolation.
Teradyne, Inc. (TER)
Weekly Performance: -15.51%
Teradyne declined 15.51% during the week, making it one of the largest laggards in this screen. Unlike semiconductor manufacturers themselves, Teradyne occupies a different position in the technology supply chain through automated test equipment, meaning its performance is often interpreted as an indirect read on future semiconductor manufacturing activity rather than current chip demand alone.
That distinction makes Teradyne particularly useful as a signal stock. When test-equipment providers experience outsized moves, market participants frequently reassess assumptions around customer capital expenditure, production timing, and equipment orders throughout the semiconductor cycle. To evaluate whether those concerns are supported by underlying business conditions, order backlog data, income statement trends, and management guidance history generally provide stronger evidence than short-term price movements. Comparing those datasets across reporting periods can help determine whether changing expectations are being reflected operationally or primarily through market sentiment.
Micron Technology, Inc. (MU)
Weekly Performance: -13.84%
Micron finished the week down 13.84%, extending weakness across parts of the memory segment despite remaining one of the companies most closely associated with AI-driven infrastructure spending. Market reports suggested that investors rotated out of several semiconductor leaders during the period following an extended rally, illustrating how positioning can temporarily outweigh company-specific developments.
For memory manufacturers, weekly price movements are often interpreted alongside broader indicators such as DRAM and NAND pricing, inventory normalization, and hyperscale capital spending. Rather than viewing the decline in isolation, analysts typically compare it with changes in gross margin trends, inventory levels, capital expenditure guidance, and analyst earnings revisions to understand whether market expectations are shifting alongside operating fundamentals. Those datasets provide a more durable framework for evaluating changes in sentiment than price action alone.
Reading the Broader Signal Behind the Weekly Moves
Viewed together, this week's five biggest movers tell a more nuanced story than a simple leaderboard of winners and losers. The strongest advances were concentrated in companies where investors reacted to evidence of operational execution or continued AI-related demand, while the sharpest declines came from businesses tied to semiconductor hardware and memory, where expectations had already become elevated. That combination points less to a broad shift in market direction and more to selective repricing within technology and adjacent sectors, as capital differentiated between individual business developments rather than moving uniformly across an industry.
This is where a weekly movers screen becomes more valuable as a research tool than as a performance ranking. A large percentage move is only the starting point. The more meaningful question is whether the market is responding to a measurable change in fundamentals, a revision in expectations, or simply a repositioning after an extended run. Looking at price alone rarely provides that answer.
A more complete workflow connects price action with the financial data that helps explain it. The EOD Bulk API identifies where the largest weekly moves occurred, but the interpretation becomes stronger when those results are evaluated alongside income statements, cash flow trends, and evolving analyst expectations. Financial Modeling Prep's platform makes it possible to combine these datasets into a single research process, allowing analysts to compare market reactions against underlying business performance instead of relying solely on weekly returns. Viewed together, those data points shift the focus from identifying volatility to understanding what may have driven it.
For research teams, that distinction matters. The objective is not to explain every large move after the fact, but to develop a repeatable framework for identifying which moves deserve further investigation. Some will ultimately reflect durable changes in business performance, while others may prove to be short-lived reactions to news flow or positioning. A standardized process that combines price action with financial statements, analyst expectations, and other fundamental datasets provides a more disciplined foundation for separating meaningful signals from market noise.
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:
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https://financialmodelingprep.com/stable/eod-bulk?date=2024-10-22&apikey=YOUR_API_KEY |
Sample Response:
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[ { "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 This Week's Tape Ultimately Revealed
Weekly movers rarely tell the entire market story, but they often reveal where expectations changed the most. Combined with FMP's EOD Bulk API, this type of analysis provides a structured way to separate headline price moves from the underlying business developments and market themes that deserve closer attention.
If you found this useful, you might also like: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (June 22-26)
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.
