> For the complete documentation index, see [llms.txt](https://docs.hyprearn.com/HnETD1HQdaN5QhTwmV4l/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hyprearn.com/HnETD1HQdaN5QhTwmV4l/quickstart.md). # Welcome To HyprEarn
> **The AI trading desk for perps.** \ > HyprEarn brings AI-curated trade discovery, risk-controlled execution and automated vault strategies into a single, non-custodial interface that runs across multiple DEXs - Hyperliquid, Pacifica, Nado, Avantis and Paradex.
It was built to solve the two biggest bottlenecks in the onchain perp market: \ **discovery** and **risk-controlled execution**. {% hint style="warning" %} **The problem: a fragmented workflow** Trading perps onchain usually means juggling a dozen tabs. You chart on one, check funding rates on another, manually size positions on a third, then hop between venues to find the best liquidity. This fragmentation breeds hesitation, slippage and missed opportunities. For most traders, the friction isn't the trade itself. It's everything around it. {% endhint %} *** #### **What HyprEarn does** HyprEarn replaces that chaos with a unified intelligence layer. Instead of sourcing and sizing trades by hand, you get real-time AI-curated setups with precise entries, dynamic exits and risk parameters baked in, all executable in one click across the venues HyprEarn connects to. {% tabs %} {% tab title="High-signal discovery" %} AI-curated setups based on market structure, funding rates and volatility; so you start from an actionable idea, not a blank chart. {% endtab %} {% tab title="Risk-controlled execution" %} Suggested entry, stop-loss and take-profit on every setup, with live risk-to-reward simulations so you never enter blind. {% endtab %} {% tab title="One-click simplicity" %} HyprEarn handles routing across DEXs to fill your order at favorable pricing, with your protection levels in place from the start. {% endtab %} {% tab title="Multi-DEX by design" %} Liquidity and funding are sourced across Hyperliquid, Pacifica, Nado, Avantis and Paradex, rather than a single venue. {% endtab %} {% tab title="Automated strategies" %} Vaults let you allocate into non-custodial, algorithmic strategies that trade 24/7, for traders who prefer a hands-off approach. {% endtab %} {% endtabs %} #### **Who it's for** {% hint style="info" %} HyprEarn is built for the "pro-tail" trader, meaning serious active participants who want institutional-grade execution and risk tooling without the institutional overhead. {% endhint %} We don't replace a trader's intuition. We amplify it by removing the grunt work of research and execution. Every feature, from the **PnL Calendar** to dynamic **R:R simulations**, is designed to give traders more conviction in their positions. {% embed url="" %} > You stay in control while HyprEarn makes acting on your edge faster, safer and simpler. *** #### About the team HyprEarn is built by a small, senior team working from first principles in decentralized infrastructure, AI and large-scale data systems. The founders have spent over a decade building together and the people designing the execution engine trade these markets themselves. The team's background spans the full stack that HyprEarn depends on. Its leadership has shipped decentralized infrastructure and AI-native data platforms at scale, taken consumer products past 10M users and $100M+ raised and contributed to core protocols and major Web3 indexing networks. Its product direction draws on a decade across AI, data infrastructure and product-led growth, including large-scale systems work at a global investment bank and go-to-market across more than twenty Web3 networks. Its engineering is owned by builders who run production-grade infrastructure securing over $10M in staked assets and drive multi-chain validation across more than ten AI- and DeFi-focused networks. > Together this is more than thirty years of combined experience and ten years building as a team - applied to a single problem: making onchain perpetual trading faster, safer and simpler. #### Explore the features {% content-ref url="/pages/ogtM28ga8wbFwajkLu2G" %} [AI Copilot](/HnETD1HQdaN5QhTwmV4l/features/ai-copilot.md) {% endcontent-ref %} {% content-ref url="/pages/8gjcW4augukvYLkVpbbZ" %} [Vaults](/HnETD1HQdaN5QhTwmV4l/features/vaults.md) {% endcontent-ref %} {% content-ref url="/pages/ULmoXhthX9AEP6aH6ZSe" %} [Program & Rewards](/HnETD1HQdaN5QhTwmV4l/features/interactive-blocks.md) {% endcontent-ref %} --- # 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: ``` GET https://docs.hyprearn.com/HnETD1HQdaN5QhTwmV4l/quickstart.md?ask= ``` 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.