Building a Polymarket Bot? Validate Resolution Rules Before Trading

A Polymarket bot can read market prices correctly, calculate probabilities and submit orders—and still trade the wrong market for its strategy.

One reason is that a market's title is not its complete specification. The resolution rules determine what outcome counts as correct, and those rules need to be treated as part of the bot's input data.

Polymarket's "official resolution guide" (https://help.polymarket.com/en/articles/13364518-how-are-prediction-markets-resolved) explains that markets are resolved according to predefined rules. For developers, the practical challenge is turning those rules into a reliable market-validation process.

1. Separate discovery from validation

A discovery function might find markets that match a keyword, category or strategy.

That is only the first step. Before a market becomes eligible for automated trading, the system should verify that its metadata and resolution criteria are suitable for the strategy.

For example, a strategy built for short-duration crypto price markets should not automatically accept every market containing the word “Bitcoin.” Different markets can use different time windows, reference sources and resolution conditions.

A simple architecture could look like this:

def market_is_eligible(market, strategy):
if not market.get("question"):
return False

if not market.get("rules"):
    return False

if not strategy.supports(market):
    return False

if strategy.requires_manual_review(market):
    return False

return True

This is illustrative pseudocode. It assumes the market object has already been retrieved and normalized, and that "strategy" implements the required validation methods.

The important principle is that discovery should not automatically grant trading permission.

1. Make validation specific to the strategy

A generic check for a non-empty question and rules is not enough for production.

The validation layer should encode the requirements of the strategy. Depending on the market type, those may include:

• The event or asset being measured.
• The exact resolution source.
• The relevant deadline or measurement window.
• The condition that determines YES versus NO.
• Any ambiguity or exception that the bot cannot handle safely.

Not every field will apply to every market. The validation logic should reflect the actual strategy rather than assume all markets share one resolution model.

If a required condition cannot be verified, the safest default is to reject the market or send it for human review.

1. Treat market rules as versioned input

Market data can be refreshed. A bot should not assume that a market's previously retrieved metadata is always the version it should act on.

A practical system can store the market identifier, the time it retrieved the rules, the normalized fields used for validation and the validation result. If relevant rules or metadata change, the bot can re-run its checks before allowing new orders.

For example:

from datetime import datetime, timezone

def build_validation_record(market, eligible):
return {
"market_id": market["id"],
"validated_at": datetime.now(timezone.utc).isoformat(),
"eligible": eligible,
"question": market.get("question"),
"rules": market.get("rules"),
}

This example records a snapshot for auditing; it does not detect changes automatically or guarantee that the market is safe to trade. A production implementation should also handle missing fields, normalize data consistently and compare newly retrieved information against the previous snapshot.

1. Test rejection decisions, not just profitable trades

A trading strategy is usually evaluated by the opportunities it captures. Market validation also needs to be evaluated by the opportunities it correctly rejects.

Build test cases for markets with missing rules, unsupported resolution sources, unclear deadlines and conditions outside the strategy's supported scope.

Then verify that the system rejects those markets instead of silently treating them as valid.

Useful measurements include the number of markets rejected, the reasons for rejection, how often human review is required and how often validation catches a changed or unsupported condition.

These measurements help developers understand whether the validation layer is doing useful work—not merely allowing more markets through.

Final thought

Automated trading starts before the order is submitted. It starts when the system decides what it is trading and whether it understands the conditions attached to that market.

Separating market discovery, rule validation and order execution makes that decision easier to inspect, test and improve.

At "Dexoryn Labs" (https://dexorynlabs.com), we build Polymarket trading bots and custom automation for specific trading requirements. If you're developing a bot and need help with market logic or implementation, learn more on our website.

The goal isn't to trade every market your bot can find. It's to trade only the markets your strategy can actually handle.