> For the complete documentation index, see [llms.txt](https://quandora.gitbook.io/quandora-docs/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/quandora-docs/getting-started/quandora-101-for-non-quants.md).

# Quandora 101 (For Non-Quants)

Quandora is an open infrastructure designed to make quant-style trading research more accessible, transparent, and agent-native. For decades, the serious trading research workflow lived inside hedge funds, prop shops, expensive platforms, and private notebooks. Market data, backtesting, overfit checks, cost modeling, signal memory, and research reports were treated as gated infrastructure. Quandora opens that workflow up.

Quandora makes systematic trading research more inspectable and repeatable. The product is not the “magic alpha.” The product is the validation layer: the process that shows whether an idea worked historically, whether it survived costs, whether it was overfit, whether it duplicated something already tested, and what should be improved next.

Quandora modernizes quant trading through:

**Transparency:** Users can see the research process behind an idea: the assumptions, metrics, caveats, and verdict. The goal is to replace black-box signals with visible evidence.

**Open access:** The agent workflow — plugins, skills, and task templates — is open source under Apache 2.0, so serious traders, coders, and AI-agent users can inspect, run, and modify how research is generated. Market-data binding and backtest evaluation run on Quandora's hosted servers, which keeps the changing market data controlled and makes results comparable across everyone's runs.

**Agent-native research:** AI agents are good at generating ideas and code. Quandora gives them the missing finance infrastructure: data, backtests, validation, memory, error feedback, and result cards.

**Risk awareness:** A backtest is only useful if it survives real-world questions. Quandora checks for costs, drawdowns, turnover, liquidity, duplicated ideas, and overfitting risk.

For non-systematic traders, Quandora is a bridge from intuition to discipline. You do not need to stop having market instincts. The point is to test them. If you believe a breakout pattern works, a funding signal matters, or a market setup repeats, Quandora helps you ask the serious question: did this actually work before, and is it still worth testing?

Quandora’s larger vision is to open up the gated infrastructure behind systematic trading research. Backtesting, overfit checks, liquidity awareness, cost realism, repeatable workflows, and research reports should not only belong to closed institutions. Quandora makes that validation layer open, transparent, and accessible to the next generation of AI-powered market participants.

{% hint style="info" %}
Quandora is finance-agent infrastructure. It is not a guaranteed-profit tool or copy-trading product, and it never trades on its own judgment — execution follows the strategy you define, under your risk limits and kill switch.
{% endhint %}


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# 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 current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://quandora.gitbook.io/quandora-docs/getting-started/quandora-101-for-non-quants.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.