The Tool Desk
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Define what the feed must answer
Before choosing a vendor or writing an adapter, write down the decisions researchers need to make from the data. A feed for comparing reported revenue across issuers has different source, timing, and storage needs from a feed for studying intraday execution or reconstructing exchange order books.
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- Instruments and identifiers: Specify companies, securities, asset classes, and the identifiers needed to join records over time.
- Geography and coverage: Set the markets, exchanges, issuers, and corporate entities in scope.
- Fields: Separate filings and structured financial facts from quotes, trades, auction data, and order-book events.
- Cadence and latency: Decide whether end-of-day, delayed, event-driven, or real-time updates are actually necessary.
- History and corrections: State the lookback period and how amendments, restatements, cancellations, symbol changes, and corporate actions must be represented.
- Consumers and use: Identify whether the feed serves private analysis, an internal shared service, a displayed product, or redistribution.
Do not buy low-latency or full-depth data by default. First determine whether the research question can be answered with filings, structured fundamentals, delayed data, or end-of-day observations. The extra frequency and depth create engineering work as well as licensing obligations.
Choose the source family that matches the question
| Source family | Best suited to | Typical engineering concerns | Rights to confirm |
|---|---|---|---|
| Issuer filings and structured fundamentals | What an issuer disclosed, and when it became available | Documents, XBRL contexts, amendments, identifier mapping, and point-in-time availability | Confirm source terms and intended reuse; public access does not itself settle every downstream use. |
| Consolidated market data | Disseminated trades and best bid/offer prices and sizes | Timestamps, symbology, corrections, and trading-session rules | Confirm display, non-display, redistribution, and retention terms for the intended use. |
| Proprietary exchange data | Exchange-specific depth, trades, auction imbalances, or other exchange events | High-volume event processing, sequencing, and—in depth research—order-book reconstruction | Confirm exchange-specific user, derived-data, display, redistribution, and retention terms. |
These are not interchangeable versions of the same feed. A filing answers a disclosure question; a consolidated quote feed describes a limited view of market activity; exchange feeds can expose different event types and levels of depth. Match each dataset to a research requirement rather than treating “market data” as one product category.
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Start with U.S. issuer disclosures when the question is about reported facts
The U.S. Securities and Exchange Commission’s official Developer Resources page, dated June 25, 2024 and last reviewed March 10, 2025, describes REST APIs on data.sec.gov that provide company submissions and extracted XBRL in JSON. EDGAR indexes, archives, and RSS can also support discovery and historical backfills. The SEC Open Data portal links to public machine-readable datasets, inventories, technical specifications, and developer resources.
The SEC developer page states a maximum of 10 requests per second per user and advises efficient, moderated requests. Identify your client, retrieve only what you need, cache appropriately, and avoid uncontrolled crawls. Treat this as an operational constraint to build into concurrency limits and retry behavior, not a target request rate.
Use the appropriate market-data product for trading questions
NYSE distinguishes real-time products—including depth, top of book, trades, and auction imbalances—from historical TAQ products for post-trade analysis and backtests, reference data for instrument and company details, and corporate-action updates. These solve different jobs: historical data is not a substitute for a live stream, and reference or corporate-action data may be necessary to interpret a price history correctly.
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NYSE’s catalog includes products and distributors such as FactSet, LSEG, TradingView, and Databento; that listing is not an endorsement or evidence that a particular agreement is available to you. Compare coverage, history, timestamps, latency, corrections, delivery format, support, licensing, and total cost against your written requirements. NYSE also describes Cloud Streaming as real-time exchange data delivered through AWS in Kafka format using Redpanda. This is one delivery path, not a general requirement to adopt Kafka.
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Understand what market-data depth does—and does not—show
A consolidated top-of-book feed is not a complete market reconstruction. The SEC’s MIDAS page, dated June 14, 2024 and last reviewed June 28, 2024, says consolidated tape for listed equities generally includes trades of 100 shares or more and reports best bid/offer prices and sizes, but does not show orders at prices beyond the best quotes. Research that depends on deeper displayed liquidity or exchange-level order events needs a different data scope.
MIDAS combines consolidated tapes with proprietary exchange feeds. The SEC says it gathers about 1 billion records each day from proprietary feeds of 13 national equity exchanges, timestamped to the microsecond. It can analyze thousands of stocks over periods of six months or a year, with analyses involving 100 billion records at a time. The SEC characterizes this data as extremely voluminous and challenging to process correctly. Those figures describe MIDAS and the scale of the analyses it supports; they are not a minimum requirement for ordinary investment research.
For fundamental analysis, do not prescribe full-depth feeds or specialized reconstruction infrastructure unless the hypothesis requires that granularity. For order-book research, plan explicitly for high message volume, event sequencing, corrections, and reconstruction expertise rather than assuming best quotes are enough.
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A practical architecture separates provider-specific behavior from the records researchers consume:
- Source adapters: Isolate SEC endpoints, vendor APIs, file deliveries, and streaming connections so source-specific schema changes do not silently alter research logic.
- Immutable raw landing: Retain the original response, message, or a durable pointer to it before parsing or transformation.
- Validation and quarantine: Check records on arrival and route malformed or suspicious data to a visible quarantine path with reasons.
- Normalized canonical records: Map identifiers, units, fields, and timestamps into documented internal schemas while preserving links to source values.
- Storage and delivery: Store data in a form suited to its access pattern—such as analytical or time-series storage—and expose it through a query layer or API with data-quality status.
This architecture is an engineering recommendation based on the variety of SEC interfaces and exchange-feed families; it is not a design mandated by the SEC or NYSE.
Record provenance and time explicitly
For each record, preserve the source and native identifier, event or effective timestamp, filing or publication timestamp when supplied, retrieval timestamp, raw payload or durable pointer, parser or schema version, and transformation lineage. Keep event time distinct from ingestion time. Normalize time zones and trading calendars deliberately; do not assume that a timestamp without an explicit zone is UTC or that all sources define a date boundary the same way.
Make changes and historical corrections first-class
Design for amendments and restatements in issuer data, and for cancellations, corrections, symbol changes, and corporate actions in market data. Retain the ability to represent what was first received as well as what is currently considered correct when point-in-time research requires it. Make backfills repeatable and transformations idempotent so rerunning a period does not create duplicates or silently overwrite the history needed to explain an earlier result.
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Validate required fields, identifier mappings, uniqueness, chronology, missing intervals, impossible values, and expected market-session coverage. Monitor source freshness, API errors, ingestion lag, volume, schema drift, missing partitions, and replay or backfill completion. Publish quality status alongside the feed so an analyst can distinguish a source fact from a pipeline gap or uncertainty.
Plan licensing and operating cost before launch
Public availability is not a blanket permission for every downstream use. The SEC materials describe public filing-data access and a fair-access policy; NYSE describes proprietary data products. The product overviews do not establish the exact rights that apply to a particular reader or deployment. Before distributing feed-derived content or building a shared service, obtain the applicable exchange or authorized-vendor agreement and confirm display, non-display, redistribution, derived-data, user-count, and retention terms.
Cost is not only the subscription price. Include storage and retention, compute for parsing or reconstruction, network and delivery, historical backfills, operational support, and licensing scope. Low-latency and depth requirements can materially increase both data volume and processing complexity. Estimate those needs from a representative sample and the actual research horizon before committing to a production design.
Check specifications and versions during integration
NYSE’s technical-document index lists feed specifications and versions, including dated specifications. Pin the version your adapter implements, record it with the deployment, and check for announced changes before production integration. Apply the same discipline to any provider’s schemas and API behavior: test changes against representative raw data before they reach normalized research tables.
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Common build problems and fixes
| Symptom | Likely cause | Practical fix |
|---|---|---|
| SEC requests are throttled or responses become unreliable during a crawl | Concurrency or request volume ignores the SEC’s stated fair-access constraint. | Enforce a shared per-user rate limiter, identify the client, cache responses, use backoff, and scope retrieval to required records. |
| A historical fundamental query returns the latest figure instead of the figure available at the time | The pipeline flattened amendments or later filings over earlier disclosure state. | Retain filing/publication timing and amendment lineage; query against the relevant point-in-time cutoff. |
| Intraday analysis disagrees with an order-book hypothesis | The selected consolidated feed shows best quotes and qualifying tape trades, not orders away from the best prices. | Reassess whether exchange-level depth is necessary, then confirm its data scope, reconstruction needs, and licensing before acquiring it. |
| Backfills create duplicates or change prior output unpredictably | Replays are not idempotent or raw inputs and transformation versions are not preserved. | Keep immutable raw inputs, version parsers and schemas, define stable keys, and test replay behavior before routine recovery. |
| Valid records appear out of order or fall into the wrong trading session | Event time, ingestion time, time zone, or market calendar was handled implicitly. | Store timestamps separately, normalize zones explicitly, apply the relevant calendar, and validate chronology at session boundaries. |
| A feed works in development but breaks after a provider update | The integration relies on an unpinned or changed specification. | Record the specification version, watch provider change notices, and validate schema changes with fixtures and quarantining. |
| A dashboard or shared feed is blocked after launch | The contract did not cover the actual display, non-display, redistribution, derived-data, or retention use. | Obtain the applicable rights before launch and align access controls and product behavior to that agreement. |
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ScreenshotNeo is a website screenshot API and MCP server, not an investment-data provider. It can be used as an auxiliary way to capture a public webpage for visual review; do not substitute screenshots for structured filings, licensed market feeds, or auditable source records. For example, this cURL request captures a page as an image:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://www.sec.gov -o shot.webpSee the ScreenshotNeo API documentation for parameters. Before capture, it can accept cookie or consent banners and remove more than 60 known consent platforms, newsletter popups, and chat widgets; each cleanup step can be turned off. Bot checks and CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers indicate the page verdict and billing status. Its MCP server gives AI agents tools for screenshots, page information, and PDF capture. The free plan includes 1,000 screenshots per month with no card; paid plans start at $5 for 3,000 screenshots.
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Frequently Asked Questions
Can I use one canonical schema for filings and market events?
Use a common envelope for provenance, identifiers, and timestamps if useful, but preserve domain-specific fields and semantics in separate record types. Filing facts and exchange events do not share the same meaning or correction model.
Should a small research team build its own order-book reconstruction system?
Only if the research depends on the relevant depth and the team can support the sequencing, volume, validation, and licensing requirements. Otherwise, narrower market data or issuer disclosures may answer the question with substantially less operational burden.
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