How to Build a Trade-Rejection Layer for a Polymarket Bot in Python
A Polymarket trading bot needs more than a strategy that identifies potential opportunities. It needs a reliable way to reject trades that fail its execution or risk requirements. Without this layer, a bot may submit orders using stale market data, accept an unattractive price, exceed its exposure limits, or act on a signal that no longer makes sense. In this tutorial, we'll build a small Python…
Building a trade-rejection layer for a Polymarket bot in Python involves creating a separate validation stage between signal generation and execution. This layer ensures that trades meet specific execution and risk requirements before they are submitted through the trading client. By separating the process into distinct stages, the bot can more easily identify and reject trades that fail these requirements, such as stale market data, unattractive prices, or exceeding exposure limits.
To implement this, start by representing a proposed trade with structured data using a Python dataclass. Each field in the TradeProposal class serves a specific purpose, such as identifying the token, side of the trade (buy or sell), estimated probability, execution price, expected cost per share, data age, proposed exposure, and current exposure. This simplified data model can be expanded in a production system to include more precise exposure units and market metadata validation.
Next, create a structured result for trade validation by defining a ValidationResult dataclass with boolean and reason fields. This will provide more meaningful feedback on why a trade was rejected, allowing for better logging, testing, and performance analysis.
The validation function checks various conditions, including the token ID, side of the trade, probability, execution price, data age, and exposure limits. If any of these conditions are not met, the function returns a rejected ValidationResult with a specific reason. For example, if the data age is too old, the function returns a rejection reason of "STALE_DATA."
Real calculations for determining the expected edge must account for fees, price constraints, execution uncertainty, and the specific strategy being evaluated. Selling positions require different logic, and the maximum price and minimum edge are separate controls that should come from a documented strategy configuration and testing process.
Before connecting the validation layer to a live execution client, it should be thoroughly tested with both accepted and rejected trades. This ensures that the validation logic works as intended and does not inadvertently allow trades that should be rejected.
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