> For the complete documentation index, see [llms.txt](https://docs.singularityfinance.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.singularityfinance.ai/sfi-value-proposition/core-pillars-of-the-sfi-l2/sfi-vaults/features/execution-engine.md).

# Execution Engine

The Execution Engine acts as the operational backbone of SFI Vaults, facilitating the seamless execution of asset management strategies. It connects offchain logic with onchain execution, ensuring precision and reliability in trade execution. Trades are initiated offchain by either a human digital asset manager (DAM) or trading algorithm signals, which are activated by events and conditions derived from technical analysis tools or machine learning signals.

All current Vaults trades are handled by the execution server. Both DAM and trading algorithms can create custom trade orders, such as 'Spot' or 'Market If Touch' (MIT) orders, which the execution server validates and executes onchain. The execution server utilises onchain oracles to ensure accurate token pricing and verifies order matches with each new block.

Trading on Vaults occurs exclusively through the execution server.&#x20;

The read-only endpoints of the execution server incorporate authentication layers using traditional methods. To modify any state on the server, such as creating or cancelling orders, an additional layer of authentication is required, utilising web3 signature authentication with only select authorised wallets.

<br>


---

# 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.singularityfinance.ai/sfi-value-proposition/core-pillars-of-the-sfi-l2/sfi-vaults/features/execution-engine.md?ask=<question>&goal=<user_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.
