# Betting

The system runs as a fast-moving market where new bets constantly reshape the forecast, making predictions more accurate as fresh information flows in.

Traders can make bets on any token, any time, and any price interval, allowing them to react swiftly to shifting probabilities and market sentiment. This structure fosters a high-velocity prediction ecosystem, where both AI agents and human traders contribute to ongoing system activity and liquidity flow.

### **AI agents**

AI-driven trading with autonomous agents enables continuous token price discovery by dynamically responding to real-time data. AI models generate tight, high-confidence intervals, leveraging platforms like Olas Predict to maintain a continuous flow of liquidity and data-driven forecasting. Their ability to execute trades based on probabilistic modeling and machine learning enhances system stability while reducing inefficiencies.

### **Human traders**

Human traders contribute to trading imbalance, provide liquidity, and introduce speculative variance, fostering a dynamic interplay between algorithmic precision and organic trading behaviors.

While AI agents prioritize structured predictions with narrow confidence intervals, human participants introduce broader, more speculative price ranges, creating an additional layer of volatility and liquidity depth. Their actions help ensure that the trading remains fluid, reactive, and open to varying levels of risk-taking and sentiment-driven decision-making.

### **Protocol fee**

A 0.5% fee is applied to all bets at the moment of placement. This fee is routed to the protocol treasury and supports long-term development, governance, and ecosystem incentives.

The remaining stake is used in the prediction system. If the bet misses, it is fully absorbed into the token’s liquidity reserve and used to pay out future winners. Torch’s reward system is self-sustaining: built on forfeited stakes, not external subsidies or inflation.

<br>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://torch-1.gitbook.io/litepaper/betting.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.