Polymarket data becomes a research dataset only after someone decides what each record means, which observations belong in the sample and when the information was available. Those choices can affect a result as much as the model applied afterward. A large archive can still support a misleading conclusion if it silently excludes difficult markets or supplies later information too early.
A useful dataset preserves provenance, schema versions and missing observations alongside prices and outcomes. It should let another researcher reconstruct how a row was produced and identify which claims the available evidence can support. The goal is reproducibility of meaning, not merely the ability to reopen a file.
This guide concerns International Polymarket data. Polymarket US has separate interfaces and venue context. The sample counts, dates, times and calculations below are invented for education. They illustrate dataset design rather than describe an actual market archive or establish the performance of a trading strategy.
Define the Question Before Collecting Polymarket Data
A dataset should be designed around a research question. Studying forecast calibration requires dated probability estimates and outcomes. Studying execution costs requires books for the intended quantities and decision times. Studying account performance requires authorized private records that public market data may not provide.
Write the intended claim in a sentence. For example, a project might ask whether a particular source update improves forecasts made one day before a contract deadline. That question determines the necessary timing, contract definitions and evaluation units. Collecting every accessible field without a question can produce volume without usable evidence.
The older prediction market algorithmic trading guide emphasizes reproducibility. Dataset design is part of that requirement. A later algorithm cannot repair an archive that lacks the observation times or contract mappings needed to test its information claim fairly.
Choose the Unit of Observation
Decide what one row represents: a market snapshot, a trade, a contract-day forecast, an evidence episode or an account fill. These units should not be mixed in one table without a clear relationship. A row count is meaningful only after the unit is defined.
Suppose the research uses one forecast per contract at a fixed horizon. Ten intraday quote snapshots for that contract do not create ten independent forecast outcomes. They may provide useful execution context, but the underlying event result remains shared.
Preserve relationships among events, markets and outcome tokens. Several contracts can refer to thresholds of one observation, and several records can describe one contract over time. A dataset that treats every row as independent can overstate the amount of evidence available for evaluating a model or estimating uncertainty.
Build a Dataset Provenance Record
A dataset provenance record identifies the source, retrieval method, retrieval time, raw identifier and transformation version for each derived record or coherent batch. It should be possible to trace a model input back to the evidence from which it was constructed.
Polymarket's API overview separates discovery, CLOB and data surfaces. A dataset should preserve which surface supplied a record instead of treating all fields as if they came from one uniform source. Similar names across interfaces can have different scopes or update behavior.
Provenance also includes the researcher’s choices. Record filtering rules, normalization methods and manual corrections. A correction should explain the original issue and preserve the original value where appropriate. Silent cleanup may make a file easier to use while removing the evidence needed to understand why a result changed.
Retain Raw Records Alongside Normalized Data
Raw records preserve the source's original representation. Normalized data makes analysis easier by expressing common concepts consistently. Keeping both allows a researcher to investigate whether an unexpected result arose from the market or from a transformation.
The raw archive should be organized enough to locate the relevant record without becoming the only usable dataset. The normalized layer should carry stable links back to it, along with schema and transformation versions. This creates a practical separation between evidence preservation and analytical convenience.
Do not assume normalization is neutral. Converting a missing value to zero, rounding a price or inferring an outcome side can change meaning. Each transformation needs a defined rule and a reason appropriate to the research question. Where the source does not support a reliable interpretation, the normalized record should retain an explicit unresolved value.
Version Contract Meaning Over Time
A research dataset needs more than a market title. Preserve the full contract question, relevant rules, source references, deadlines and outcome mapping with their observation times. If metadata changes, retain the relationship between versions rather than overwrite history.
A later rule clarification may be important for interpreting the final outcome, but a historical decision record should still show what the process knew at that time. These are two useful views: the contemporaneous information set and the eventual governing interpretation. They should not be collapsed without explanation.
The guide to Kalshi market analysis emphasizes exact determination rules in another venue context. A cross-venue research project should preserve each contract's meaning separately. Matching headlines or converting prices to percentages does not establish that the instruments have equivalent payoffs.
Preserve Point-In-Time Availability
Point-in-time availability describes when a particular process could have used a record. Source event time, publication time, local receipt and later storage can differ. A historical simulation should not use a record before the relevant availability time simply because its source timestamp is earlier.
In an invented case, a quote carries a source time of 11:59:58 but reaches the collector at 12:00:03. A decision made by that collector at 12:00:00 could not use the quote unless separate evidence establishes earlier receipt through another path. Sorting only by source time can create look-ahead.
Keep both source and receipt times when available and explain missing fields. Do not replace an unknown source time with receipt time under the same label. The dataset can still support useful research with a narrower timing claim, provided the limitation remains visible and the evaluation respects it.
Define the Eligible Universe Before Outcomes
A Polymarket research dataset should record which contracts were eligible under the study's rules at the relevant time. Selecting only active markets today or only markets with memorable outcomes can introduce survivorship and selection effects. The sample may omit precisely the cases that were difficult to trade or interpret.
Define eligibility using observable criteria such as contract family, rule clarity, horizon and required data coverage. Apply the rule consistently and preserve excluded candidates with reasons. Do not remove a contract because its eventual result makes the proposed strategy look worse.
The guide to a Kalshi strategy shows why a repeatable routine needs a defined contract universe. The same principle applies to dataset construction. The researcher should be able to explain which opportunities the process would have considered prospectively, including those that later disappeared from a convenient discovery list.
Distinguish Four Kinds of Missingness
Missing source evidence, a collection gap, an unresolved outcome and an intentionally excluded contract are different conditions. They should not share one blank field without explanation. Each affects interpretation and evaluation differently.
A collection gap means the process lacks observations for an interval. An unresolved outcome means the target label is not yet available. An excluded contract may be outside the study's scope despite complete data. A missing source can mean the required evidence was unavailable even though market prices were recorded.
Use clear reason codes and human-readable descriptions. A researcher can then evaluate whether missingness clusters around volatile periods, unusual contracts or operational failures. That pattern can be more important than the overall percentage of complete rows, because missing observations may be systematically different from the ones retained.
Quantify the Effect of Missing Outcomes
Consider an invented sample of 120 eligible forecasts. Ninety have observed binary outcomes, including fifty-four Yes results, while thirty outcomes remain missing. The observed Yes frequency among the ninety is 60%. It does not automatically describe the full eligible sample.
If all thirty missing outcomes were No, the full-sample Yes frequency would be 54 divided by 120, or 45%. If all thirty were Yes, it would be 84 divided by 120, or 70%. These are logical bounds under the simple binary assumption, not statistical confidence intervals.
The example shows why reporting only complete cases can hide uncertainty. The researcher should investigate why labels are missing and whether the observed subset differs from the omitted cases. A precise percentage calculated from available rows can be arithmetically correct while supporting a much narrower claim than the report suggests.
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Keep Unresolved and Exceptional Labels Honest
Do not force every contract into Yes or No before its actual resolution supports that label. A market may remain unresolved, undergo a dispute or have an exceptional provision. The dataset should preserve the official state and applicable payout information rather than invent a binary answer for convenience.
Polymarket's resolution documentation describes rule-defined sources and edge cases. A study focused on simple binary outcomes can define an eligibility policy for exceptional cases, but that policy should be established and disclosed rather than applied selectively after seeing results.
Label availability also matters. A model trained at a historical time cannot use a final outcome that had not yet become known. Preserve the time the label became available to the research process and distinguish it from the underlying event's apparent completion time.
Treat Schema Changes as Dataset Events
A schema change can alter field names, precision, envelopes or interpretation. Record the source version and parser version used for each batch. A normalized dataset should identify when a transformation changed and whether old records were reprocessed.
Current documentation should be consulted before assuming that older client examples still describe the active interface. A successful response parse does not prove semantic compatibility. A new field can be present while an old assumption about units or direction remains embedded in the analysis.
The older Polymarket orderbook scraper guide explains reliable collection. Extend that discipline to migrations: test known records, preserve raw inputs and compare derived outputs before replacing a transformation. If the change alters historical values, publish a dataset version and explain the affected analyses.
Preserve Decimal Values Without Premature Rounding
Prices, quantities and calculated values should retain enough precision for the intended research. A screen-friendly rounded price may be acceptable for a chart but unsuitable for reconstructing a narrow cost margin. Keep raw decimal representations and use exact arithmetic where the calculation requires it.
Record the rounding policy separately from the stored value. If an analysis rounds prices to a coarser grid for a deliberate study, that is a transformation to document. It should not be confused with the source's actual valid trading increments at the time.
The same applies to quantities and fees. A model that assumes every observation represents whole shares can distort records containing fractional quantities. A dataset should make the units and precision explicit so later calculations do not inherit a convenient but unsupported simplification.
Store Books According to the Execution Question
A dataset intended to estimate execution costs needs enough order-book information for the proposed quantity. A last-trade series or midpoint chart cannot reconstruct historical depth that was never collected. The study should identify that limitation before simulating fills.
If only top-of-book data is available, the analysis can make a limited claim about that observed level and quantity. It should not grant a large hypothetical order unlimited access to the same price. Likewise, a snapshot taken every minute does not establish what was available between snapshots.
The guide to Kalshi data offers another venue's dataset planning perspective. Cross-venue research should preserve each source's sampling and coverage limits. Identical column names do not make a dense stream and a sparse snapshot archive equally informative about short-lived execution opportunities.
Deduplicate Records Without Deleting Legitimate Events
Repeated transport messages should not create repeated economic observations. Use stable identifiers and documented source semantics to identify duplicates or revisions. Matching only on price, quantity and time can incorrectly collapse separate trades that happen to share those values.
Preserve a record of deduplication decisions. The cleaned dataset should explain how many raw records became one normalized event and why. A later schema change may require revisiting that logic, which is much easier when the raw evidence remains available.
For book data, a repeated state observation can still carry a new receipt time even when the levels are unchanged. Decide whether the research needs both observations and document the choice. Deduplication is a modeling decision about identity and sampling, not merely a file-size reduction exercise.
Construct Features Using Only Prior Information
A feature should be calculated from records available before the decision it is meant to support. A rolling average that includes the current outcome, a revised source value or a later quote can leak future information even if the final dataset is sorted chronologically.
Define the time boundary for every feature and test it with deliberately awkward examples. A record arriving after the decision should not enter the feature merely because its event timestamp is earlier. A later correction should appear only when it became available under the study's chosen information model.
The older prediction market analytics guide explains interpreting measurements. Dataset construction adds the requirement that each measurement be timely. A valid formula applied to an invalid information set can produce an impressive result that no real process could have achieved.
Split Evaluation by Event and Time
Related contracts from one underlying event should remain together when separating development and evaluation samples. Otherwise information about the same event can appear on both sides of the split. The apparent number of independent tests may then be much smaller than the row count suggests.
Use chronological separation when the intended use is forecasting future events. Preserve a later period that was not used to select features, thresholds or exclusions. If the design changes after reviewing that period, it becomes development evidence for the new version rather than a fresh independent test.
Report sample composition as well as size. Event families, horizons, liquidity and missingness can differ across periods. A performance change may reflect that mix rather than a universal change in model quality. The dataset should make those comparisons possible without requiring the researcher to reconstruct eligibility from memory.
Keep Public Research Data Separate From Private Accounts
Public market records can support analysis of prices and activity, but they do not automatically establish an individual user's complete positions or results. Account performance research requires authorized private orders, fills, fees and other relevant records, with their own provenance and access controls.
Do not infer a person's identity or private motive from a public wallet address. The dataset can describe observable activity and uncertainty without attaching unsupported personal claims. Public visibility of a record does not make every interpretation about the participant valid.
If public and private data are joined for an authorized study, preserve the basis for the join and protect sensitive account information. The research output should include only what is needed for its claim. A model evaluation rarely needs reusable credentials or unnecessary personal details in its analytical files.
Publish a Dataset Card for Review
A dataset card is a concise explanation of the question, observation unit, sources, time coverage, eligibility rules, transformations and known limitations. It should identify versions and explain how missing or exceptional cases are represented. This makes the archive usable by someone who did not build it.
Include the claims the dataset cannot support. A snapshot archive may support broad spread analysis while lacking the detail needed for exact queue-position simulation. A complete outcome set may lack contemporaneous forecasts and therefore be unsuitable for evaluating a historical signal without additional evidence.
The card should change when the dataset changes. New sources, reprocessed schemas or corrected labels can affect prior results. Versioning that explanation alongside the data gives the researcher a reliable reference for interpreting differences between analyses rather than treating every regenerated report as directly comparable.
Make Polymarket Data Reproducible in Meaning
A research dataset should preserve the route from raw observation to normalized record, eligible sample, timely feature and verified label. Provenance, schema versions and missingness are part of that route, not optional administrative details added after modeling.
Start with a precise question and retain the information needed to test it honestly. Keep contemporaneous availability separate from later knowledge, preserve contract definitions and avoid claiming historical execution detail that was never observed. Evaluate related records at the appropriate event level.
The strongest dataset is one whose limitations can be stated clearly and whose transformations can be reproduced. It allows a reader to understand why a result appeared and whether the evidence supports the proposed conclusion. That foundation is more durable than an impressive row count built from records whose meaning or timing has been lost.