Prediction Market Scanner Bot: State and Timing

Define the Prediction Market Scanner Bot's Contract Registry

The registry stores venue context, market identifiers, outcome selections and rule references. It should preserve enough information to distinguish similar instruments and connect later observations to the correct contract. A title is useful for display but should not be the identity key.

Polymarket International and Polymarket US require separate venue contexts. The official Polymarket US data guide describes a read-only market-data use case for that platform. Do not assume International identifiers, account behavior or integration instructions apply to the US setting simply because the brands are related.

Version the rule interpretation independently from the market-data state. A corrected threshold extraction should update the compatibility layer without pretending that a new trade occurred. Preserve the source and retrieval time so a reviewer can see which terms supported a comparison.

The registry is also where unsupported instruments remain visible. A contract that the bot cannot interpret should not disappear silently or inherit a generic binary template. Mark the missing field and route the case to a suitable review or archive state.

Give Each Contract a Scanner Lifecycle State

A scanner lifecycle state describes the contract's role in the current workflow. Possible internal states include discovered, under review, eligible for live comparison, closed awaiting determination and archived after settlement evidence is recorded. These are design choices and should be mapped carefully to the venue's actual statuses.

Do not assume one universal venue lifecycle. The bot should preserve the raw source status and its own interpreted state separately. If the mapping is uncertain, the contract should not be treated as fully eligible for a current execution comparison.

A closed contract can remain useful for historical analysis. The architecture should therefore separate live-comparison eligibility from data retention. Removing it from current scans need not erase the rules, book history or prior comparison records.

The older guide to automated prediction markets provides context for contract lifecycles. A clear state model prevents a final price from being mistaken for a current quote and makes it easier to explain which transitions still require evidence.

Separate Market State From Book State

An open contract does not guarantee a fresh order book. A healthy data connection does not guarantee that the contract remains open. Store these observations independently and join them only when evaluating a specific comparison. This avoids allowing one current field to make the entire record look current.

A book state can include snapshot available, updating, stale, gap detected or unsupported. The names matter less than their defined meaning. The bot should know whether it has enough information to represent the current book consistently and how long that confidence remains valid.

If the book becomes stale, retain the last snapshot for historical inspection but remove its eligibility for current gap claims under the defined policy. A displayed timestamp should make that distinction visible to the reader.

The guide to prediction market alerts helps translate these states into messages. A stale-book event is an operational warning, not a change in the event's likelihood. The notification should say which comparison became unavailable and why.

Define an Order Book Synchronization Policy

An order book synchronization policy specifies how snapshots and incremental updates produce a consistent local view. The implementation must follow the actual venue's current data contract. Do not invent sequence guarantees or recovery behavior that the source does not document.

At the design level, the bot needs to recognize when continuity is uncertain. A missed update, reconnect or unexpected ordering can invalidate the local reconstruction. The appropriate response may be a fresh snapshot and a controlled resumption rather than continuing from an inconsistent state.

Preserve the raw source ordering information that is available and the time each update was received. If the source lacks a field needed for a stronger guarantee, state the limitation. The bot's confidence should reflect the evidence, not the precision of its internal timestamps.

The older guide to prediction market trading API discusses connection and verification boundaries. A scanner can remain read-only while still needing rigorous state recovery, because an incorrect local book can mislead every downstream comparison.

Keep Event Time and Receipt Time Distinct

A source timestamp describes the observation according to the source's semantics. A local receipt time describes when the bot obtained it. These clocks can diverge because of transport delay, batching or backfill. The comparison record should preserve both where available.

A newly received snapshot is not automatically a newly observed market state. If the source data is old, the bot should not reset freshness merely because the message just arrived. Conversely, a timely source observation can become unusable if processing is delayed too long.

Define which clock governs each freshness rule and explain why. Historical research may use event-time alignment, while operational health may focus on receipt gaps. One timestamp cannot safely answer every timing question.

If source time is absent, the system can still record receipt time and state the weaker assurance. The correct output may be a qualified comparison or abstention, depending on the task. Missing timing evidence should not be repaired with an invented precision.

Join Only Compatible Contracts

The compatibility layer compares exact rules, sources, thresholds, timing and exceptional provisions. A topic match produces candidates, while a verified relationship determines what price comparison is meaningful. The registry should preserve the reason a pair qualifies or remains uncertain.

A hypothetical pair about the same index can differ because one uses the first release and the other a revision. No amount of book synchronization makes those contracts identical. The bot should keep rule compatibility and data freshness as separate prerequisites.

The older guide to prediction market arbitrage bot emphasizes verifying each leg. A scanner supports that work by producing evidence, not by declaring that every apparent gap is risk-free. Partial execution and settlement differences remain relevant after a pair passes a limited rule comparison.

If a rule clarification affects one contract, invalidate the dependent compatibility result until reviewed. The quotes can continue to be collected without being used in a claim whose instrument relationship is no longer established.

Build a Comparison Freshness Record

A comparison freshness record identifies the exact snapshots used, their source and receipt times, the lifecycle observations and the compatibility version. The result should be reproducible from those references. A later refresh should produce a new version rather than silently rewriting the evidence behind an earlier alert.

Define a maximum age and a maximum time separation appropriate to the task. These are policy choices, not universal safe values. A slow research summary and a current execution estimate can have different requirements.

For a hypothetical comparison, Snapshot A may be two seconds old and Snapshot B 20 seconds old. Whether the pair is eligible depends on the documented policy. The bot should not hide the difference behind one combined updated-at time.

When a result expires, preserve it as a historical observation while removing any current claim. This lets the reader review what the system saw without mistaking an old gap for a live condition.

Calculate the Relevant Side and Size

A comparison should state whether it models a purchase, sale or multi-leg construction. Use the relevant bid or ask and available quantity rather than a generic displayed price. Include current applicable costs or clearly labeled hypothetical assumptions in an educational scenario.

Suppose an invented book offers 40 contracts at $0.30 and 60 at $0.34. A 100-contract purchase across those levels costs $12 plus $20.40, or $32.40 before fees. The average is $0.324, while the best ask is $0.30.

That estimate depends on the quantities remaining available and the actual order conditions. The bot should present it as an observation-based scenario, not a promised fill. The quantity and snapshot reference belong with the result.

The guide to prediction market trading introduces these mechanics through one contract. A scanner's output becomes easier to interpret when it uses the same distinction between quoted conditions, submitted requests and actual filled positions.

Handle Lifecycle Changes During a Comparison

A contract can change state while the bot is assembling a result. The design should recheck the required lifecycle evidence before publishing a current comparison, or preserve a sufficiently coherent snapshot under a defined policy. Otherwise an open-state observation can be paired with a later closed-state book.

When a contract closes, dependent live comparisons should become unavailable with a reason. Their historical records can remain. If the source later reports determination or settlement, those updates belong to the lifecycle record rather than being treated as new price opportunities.

A delayed update can arrive after the transition. The bot should decide whether it is historical data or evidence requiring a state correction. It should not automatically reopen a contract because an older open status arrived late.

Test this behavior explicitly. A scanner that handles only steady-state updates may look reliable until the most consequential moments near closing or resolution. State transitions deserve their own acceptance scenarios.

Recover From a Data Gap Without Inventing Continuity

If the bot detects a gap, mark the affected book or source unavailable for the claims that depend on continuity. Preserve the last known state, the gap interval and the recovery action. Do not fill the missing period with zeros or assume that the previous book remained unchanged.

After recovery, obtain the evidence needed to establish a valid current state under the source's documented process. The first recovered observation should carry its actual time and status. It should not retroactively prove what happened during the missing interval.

Historical backfill can improve the archive, but label it separately from live observations. An evaluation of past alerts should use what the system knew then, including the gap, rather than a repaired dataset presented as contemporaneous knowledge.

The guide to a prediction market alert bot explains how to avoid a storm of stale notifications after reconnecting. The scanner should provide enough age and episode information for the delivery layer to distinguish current changes from recovered history.

Preserve Comparison Episodes Without Duplicating Claims

A persistent gap can generate many similar calculations. Group them into an episode with a start, meaningful updates and an end under the defined policy. Repeated observations can be retained without producing a new independent opportunity message each time.

Define what changes an episode materially. A different compatible pair, a changed size estimate or a lost prerequisite may require an update. A tiny price fluctuation inside the same condition may not. The rule should reflect the reader's task and remain visible for later review.

An episode identifier should connect corrections and expiry. If a compatibility review invalidates the pair, the prior alert should be updated rather than left standing beside a new unrelated warning. The user needs one coherent history.

Do not treat repeated delivery as new evidence. The scanner's comparison state and the notification system's delivery attempts are separate records. Keeping them distinct prevents transport retries from inflating the apparent number of market events.

Keep the Scanner Read-Only Unless Another Role Is Authorized

A scanner can produce useful research without order authority. If another component consumes its output, pass identifiers, timestamps, compatibility evidence and limitations. A downstream decision process must still check current account conditions, permissions and exposure limits before authorizing an action.

The scanner should not interpret an unknown order status as a reason to publish a fresh action request. Execution recovery belongs to the attempt and account records. A repeated comparison can be the same market episode even if the earlier action's response is missing.

Make the boundary explicit in the interface. A current comparison is evidence, not a fill or a guaranteed opportunity. A reader should know which component, if any, can propose, authorize and submit an order.

This separation makes testing simpler. The scanner can be evaluated for accurate comparisons and state handling independently of an execution system's behavior. Failures can then be assigned to the responsible layer rather than hidden inside a single end-to-end success metric.

Test the Bot With a Short Event Sequence

Prepare a hypothetical sequence containing discovery, rule review, an open status, two synchronized books, a stale update, a close transition and a late message. Write the expected state after each event. This makes the test a check of meaningful behavior rather than a mirror of implementation details.

The bot should publish only when compatibility, lifecycle and freshness requirements are satisfied. It should withdraw the current claim when one prerequisite fails while preserving the earlier evidence. A late message should not recreate a state that has already been superseded without a justified correction.

Include an ambiguous rule field and a missing timestamp. These cases test whether the system can abstain. A bot that always produces a number may be concealing unsupported assumptions rather than demonstrating completeness.

Finally, ask a reviewer to reconstruct one comparison and one rejected case from exported records. Both should be understandable without access to the running process. The ability to explain nonpublication is as important as the ability to explain an alert.

Measure Reliability by the Claims the Bot Makes

Useful measures include the proportion of comparisons with complete evidence, time spent unavailable because of data gaps, frequency of corrected compatibility decisions and the reader's ability to reconstruct outputs. Raw scan counts say little about whether the results are trustworthy.

Separate source problems from internal processing problems where the evidence permits it. A delayed source and a slow local queue can produce similar user symptoms but need different remedies. The timestamps and state records should help identify the distinction.

Review false positives and missed coverage. A strict bot can avoid bad comparisons by excluding almost everything, while an expansive bot can produce many unsupported matches. Report both precision of claims and the scope of eligible cases so the tradeoff remains visible.

Keep evaluation tied to current method versions. A new decoder or lifecycle mapping should not inherit the old version's evidence without review. Preserve difficult historical cases as acceptance examples to detect regressions in the behaviors the workflow depends on.

A Prediction Market Scanner Bot Should Explain Its State

The completed system should be able to say which contracts it compared, why their rules were considered compatible, which snapshots it used and why the result was current at publication. It should also explain why a comparison became stale, closed or unresolved.

The hypothetical depth and lifecycle examples show that correct arithmetic is only one requirement. A result can be numerically accurate while referring to different moments or an unavailable market. State and timing give the calculation its practical meaning.

Build those distinctions into the data model and the reader-facing record. Preserve historical evidence while limiting current claims to supported conditions. That gives the scanner a clear research role and creates a reliable foundation for any separate decision process that may use its output later.