Polymarket Trading Bots: How to Build the Decision Engine

Market data is readily available, but determining how to act upon it poses a significant challenge. A Polymarket trading bot can access the API, stream market data, read order books, calculate prices, and even employ AI models to analyze events. However, the crucial aspect lies in the decision engine, which converts all the system's knowledge about a market into a decision. This engine needs to consider several factors before making a move.

First, the bot should have a normalized market state. It should not be burdened with understanding raw API responses. Instead, it should receive a clean representation of the current market, such as:

```

market_state = {

market_id: ...

token_id: ...

mid_price: 0.61,

best_bid: 0.60,

best_ask: 0.62,

spread: 0.02,

depth: 1250,

volume: 84000,

timestamp: 1760000000

}

```

Without normalization, strategy code becomes tightly coupled to individual API responses, making the system difficult to test and modify. The bot needs its own estimate of the outcome. Let's call this P(model) and the market-implied probability P(market). The bot then compares these probabilities and assesses whether the difference is significant enough to consider a trade.

However, simply comparing two numbers isn't enough. The bot must also account for the uncertainty in its probability estimate. A model may estimate, for example, 68%, but it doesn't mean the true probability is exactly 68%. The decision engine should evaluate how confident it is in this estimate.

A more reliable approach is to calculate expected value, considering factors such as payout, cost, and execution. For instance, if a YES contract costs $0.61 and the bot estimates a 68% probability of YES, the expected value calculation becomes:

EV = 0.68 × $1.00 − $0.61 = $0.07

However, this calculation doesn't account for execution. If the bot can't enter the position at the displayed price due to insufficient order book size, the effective entry price may change, affecting the expected value. Therefore, the decision engine should not solely rely on "Model probability − displayed price" as its trading signal. It should consider the executable price and other execution effects.

A proper decision engine requires a minimum estimated edge threshold, which is determined through historical testing. If the estimated edge falls below this threshold, the system should return NO_TRADE. Additionally, the engine should incorporate a confidence layer, taking into account the model's calibration and historical performance. It should also consider execution quality, risk constraints, and ensure that no single signal controls the bot.

Finally, it's essential to separate the signal engine from the execution engine. The signal engine generates a structured signal, while the decision engine evaluates this signal. Only if the decision engine approves the trade should the execution system come into play. This separation simplifies testing and enhances the overall effectiveness of the trading system.