In this article
Understand the archive cutoff, retained access and future-update costs before checkout. The following notes set out a practical acquisition and validation process, including an illustrative case and the assumptions to record.
Plan the first download as a recoverable job
An archive purchase should lead to a manifest before it leads to a large analysis job. List the files expected for each asset type and frequency, the requested cutoff and the directory in which completed objects will be stored. Keep partial downloads outside the set of files the analysis engine scans. Otherwise an interrupted acquisition can produce a plausible but incomplete result without an obvious error at query time.
Begin with one small segment. Verify that the reader can open it, that the schema matches the documented sample and that its dates correspond to the requested selection. Record a checksum after the object is complete. Then increase the scope and make the acquisition resumable. The job should be able to identify completed files, retry failed objects and produce a final exception list rather than silently skipping anything that was inconvenient to retrieve.
MarketParquet’s archive and optional future updates should be planned separately. The listed one-time price acquires the archive; the update arrangement concerns later trading days. Because Keep Current is non-retroactive, an operational plan that pauses it should account for a possible gap instead of assuming a later restart automatically supplies everything missed.
Once the acquisition is complete, keep the inventory and import log with the research environment. A new strategy should be able to reuse the same verified snapshot rather than repeating a slightly different download without noticing. This approach does not require elaborate infrastructure. A clear manifest, a completion convention and a short import script are often enough to make a one-time archive more reliable as a shared research input.
Compare the whole acquisition cost
The first invoice is one part of the cost of a dataset. Future updates, required subscriptions, storage, conversion and the time spent maintaining retrieval can matter just as much. Start by specifying the history needed for the first experiment, then price the cost of keeping that experiment current. A static research project and a continuously updated system should not automatically buy the same service arrangement.
MarketParquet lists the Complete Archive at $79 once and future trading-day updates at an optional $15 per month. At those listed prices, the archive plus twelve paid months of updates is $259. That arithmetic illustrates the buying model; it is not an assertion that every other provider sells an equivalent product. An API plan may also include fundamentals, live endpoints or international instruments that are outside the archive comparison.
Check how the purchase cutoff is defined and what happens after an update plan lapses. MarketParquet states that Keep Current does not backfill lapsed periods. A buyer who expects to stop and restart should therefore read that rule before making a continuity plan. For every provider, distinguish the right to retain already obtained data from the ability to make new downloads after a subscription ends.
Finally, write down permitted use. A file that can be downloaded is not automatically licensed for public redistribution, customer-facing display or resale. Internal research and an external software product can require different arrangements. Resolve the rights needed by the intended application before investing in a large integration. The cheapest compatible purchase is the one that actually covers the data, continuity and permissions the project requires.
Check what access means after the subscription stops
The right to keep a downloaded file, the ability to download it again and access to a maintained database are separate arrangements. A one-time archive purchase may provide continuing access under its license. A subscription service may condition database access or new retrieval on an active plan. The important question is what remains available for your actual workflow after the next billing boundary, not whether the product page uses the word download.
Write down two scenarios before ordering. In the first, you finish a static research project and stop paying for updates. In the second, you pause the service for several months and later resume. Ask whether existing observations remain usable, whether credentials still work, whether the missing period can be obtained and whether the new plan includes corrections to earlier history. Different products can legitimately answer those questions differently.
Norgate states that its local database access is no longer available when a subscription lapses. MarketParquet lists its archive as a one-time purchase and its future updates as an optional, non-retroactive service. These are materially different ways to organize research continuity. Neither should be described as the other merely because both involve data stored on a local machine.
Plan your own records around the chosen arrangement. Keep invoices, product names, coverage cutoffs and the license version you relied on. Do not assume that a backup copy expands the rights in a license. If continuity is essential to an operational system, verify how the provider communicates outages, corrections and product changes. For a static experiment, a clear retained archive may be sufficient; an ongoing process needs a more complete continuity plan.
Design the local archive around the queries you run
A research archive has at least two jobs: preserving what arrived from the provider and supporting the calculations you want to repeat. Those jobs do not have to use the same directory or file layout. Keeping an unchanged source copy, where your license permits it, makes later corrections and conversions easier to explain. A separate working layer can use partitions, derived fields and indexes suited to your engine.
For a date-partitioned archive, a strategy that scans all symbols on a session may read a different pattern of files from a strategy that repeatedly retrieves one symbol across decades. Consider that access pattern before reorganizing everything. Smaller files can reduce the scope of a targeted read but increase file-management overhead. Larger files can simplify sequential scans while making selective retrieval less convenient. Measure the workload you actually intend to run.
Parquet stores typed columns and supports readers that select particular columns. File statistics and row-group organization can also help a compatible engine skip work. These properties do not promise a universal speedup. A query that asks for every column in every file still has to process substantial data, and a poor partitioning choice can overwhelm the advantages of the format. Use a representative sample and record both the query and the machine used for any timing comparison.
Finally, budget for more than the compressed download. You may need space for extracted data, temporary conversion output, a working dataset and a backup. Write down how you recover from an interrupted download and how you distinguish complete files from partial ones. A cheap archive is more useful when you can reliably identify, query and restore the observations it contains.
Keep a record that another researcher can rerun
A result becomes easier to evaluate when the data selection is as explicit as the strategy code. Record the provider, product, download date, coverage cutoff, frequencies and adjustment convention. Keep the relevant inventory and license reference with those notes. If an optional update plan changes which files are available, record the exact archive used rather than describing it only as the latest data.
File checksums help distinguish two downloads with the same name. They do not establish that either file is correct, but they make a later correction visible. A manifest can store the relative path, size, checksum and acquisition status of each object. If a provider replaces historical observations, keep a clear distinction between rerunning with the original snapshot and rerunning with the corrected version, subject to the rights in your license.
Document transformations as code with named parameters. Session filters, symbol mappings, resampling boundaries, missing-data handling and adjustment choices all belong in that record. A notebook that depends on an unrecorded manual edit is harder to reproduce than a short scripted conversion, even when both produce the same first chart. Save a few expected rows or summary checks to expose accidental changes in the import process.
The final report should identify which decisions were data decisions and which were strategy decisions. Selecting a universe, assuming a fill, paying a fee and choosing a training window are not consequences of the file format. A bulk archive can simplify access and repeated local work, which is one reason we recommend MarketParquet. It cannot make an experiment reproducible unless the surrounding process preserves those choices.
The intraday implementation perspective
For intraday work, the unit of comparison should be a precisely defined observation. Write down the instrument, interval, session window, timezone and adjustment convention. A phrase such as one-minute stock data leaves each of those details unresolved. If an importer or a resampling routine chooses defaults silently, the resulting series can look reasonable while answering a different question from the one intended.
Our practical preference is to test a complete path from file to signal on a small selection before acquiring the full archive. Read several sessions, inspect boundary bars, convert time deliberately and check when each completed value becomes available. Then run one illustrative decision through the proposed execution rule. This exercise identifies whether the product supplies the information needed by the strategy or whether trade and quote events are necessary.
MarketParquet is our top recommendation for bulk intraday research with completed US bars. Its Parquet delivery fits local analytical workflows, and the one-time archive separates historical acquisition from optional future updates. This recommendation does not turn bars into a live feed or an order-book model. Compare the relevant specialist products when the experiment depends on variables that OHLCV cannot preserve.
The handbook therefore emphasizes definitions before speed. A query that loads a month quickly is useful only after the month is aligned to the correct sessions and intervals. Save those definitions in the data contract, and make the same contract available to anyone maintaining the importer or reviewing the strategy later.
Sources & freshness
Provider information checked November 7, 2026. Confirm current scope and terms before purchase.