> For the complete documentation index, see [llms.txt](https://docs.billybets.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.billybets.ai/autonomous-betting/betting-engine.md). # Betting Engine The agent will place daily bets autonomously utilizing the betting engine. Billy Bets will utilize a combination of strategies and integrations across betting markets to place directional and delta-neutral bets on sports markets, starting with the NBA, with the objective of maximizing bankroll growth, whilst minimizing risk. The following strategies will form the core of the bets made by the agent: 1. **Sports Intelligence**: Leverage proprietary odds from Bittensor's SportsTensor subnet 2. **Arbitrage**: Offset trades across exchanges 3. **Copy Trading**: Following top sports bettors on & off-chain 4. **Market Making**: Leverage off-chain betting odds to provide liquidity on-chain Moreover, the agent will utilize a combinations of risk mitigation strategies to maximize returns whilst limiting downside risk. These include Kelly criterion sizing optimizations, unit sizing models and other gold standard betting strategies used by the leading bettors. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.billybets.ai/autonomous-betting/betting-engine.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.