> For the complete documentation index, see [llms.txt](https://quandora.gitbook.io/en/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://quandora.gitbook.io/en/start-here/readme.md).
# Introduction
Quandora is finance-agent infrastructure for AI-native quant trading workflows.
## About Quandora
Quandora is a finance-agent infrastructure for AI-native quant trading workflows.
The public plugin provides five focused skills: Factor Mining, Factor Analysis, Strategy Building, Strategy Analysis, and Paper Trading. Together they let an agent create research artifacts, read server-persisted evidence, backtest a cross-sectional Strategy, and monitor simulated execution with explicit user confirmation between stages.
[Create or sign in to your Quandora account](https://app.quandora.ai/auth/signin), then [choose the path](/en/start-here/choose-your-path.md) that matches your experience and goal.
## Thesis
As trading becomes more agentic, it converges toward quant trading because serious agents need data, validation, feedback loops, risk controls, and evidence rather than vibes.
Finance does not need another chatbot. It needs:
* signal validation
* overfit detection
* microstructure / liquidity analysis
* cost and turnover realism
* backtesting and walk-forward checks
* reviewed factor memory
* reporting and audit trails
* security-aware workflows
The scarce layer is not another dashboard or raw data feed. The scarce layer is the evidence system that records whether an idea passed its current checks, became too expensive to trade, duplicated prior attempts, or needs deeper review—without turning analysis into an automatic submission.
{% hint style="info" %}
Quandora is finance-agent infrastructure. Factor research and analysis, Strategy backtesting and analysis, and simulated Paper Trading are available to public users. Live trading is not part of the public product. Quandora is not a guaranteed-profit tool or copy-trading product. Crypto markets first.
{% endhint %}
---
# 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 dynamically by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://quandora.gitbook.io/en/start-here/readme.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 `build a script that syncs our docs to a CMS` 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.
