CoinQuant Introduces Trading Infrastructure for the Agent Economy

TheNewsCryptoPublicado em 2026-05-26Última atualização em 2026-05-26

Resumo

CoinQuant, an AI-powered no-code trading platform with over 15,000 users, is expanding its infrastructure to serve the emerging "agent economy." The company is introducing a unified trading intelligence architecture designed for both human traders and autonomous AI agents. This system acts as a trust layer, ensuring all trading strategies—whether human-created or AI-generated—undergo rigorous validation, backtesting, and risk analysis before live deployment. The core of the platform is an intelligence engine that combines institutional-grade backtesting, structured market data, AI optimization, and a proprietary Domain Expert system. Human traders interact via a natural language interface, while AI agents connect through APIs. Every validated strategy contributes to an anonymized aggregate intelligence layer. CoinQuant plans to launch an automated execution layer on HyperLiquid and is currently raising a $3 million Seed round to scale its product and infrastructure, including the development of HYDRA, a hierarchical multi-agent architecture. The company aims to become the foundational intelligence backbone for algorithmic trading in the agent-driven financial era.

The agent economy is reshaping financial markets. Open-source agent frameworks are accelerating autonomous financial activity, with AI agents increasingly executing trades, managing portfolios, and interacting directly with exchanges. Yet the financial infrastructure supporting this shift has not evolved at the same pace.

CoinQuant, the AI-powered no-code trading platform that has attracted over 15,000 users since launch, today announces its expansion into a unified trading intelligence architecture built for both human traders and autonomous AI agents.

“Autonomous trading is no longer theoretical. It is already happening. The next phase requires structured validation, disciplined risk management, and intelligence infrastructure. That is what CoinQuant delivers,” said Maan Ftouni, Founder and CEO of CoinQuant.

The trust layer for autonomous AI agents

As AI agents increasingly connect directly to exchanges and wallets, many rely on raw APIs without structured backtesting, risk analysis, or validated data pipelines. CoinQuant introduces a structured intelligence layer between trading intent and live capital deployment.

No strategy goes live unvalidated, whether built by a human or generated autonomously. Backtesting, risk metrics, and parameter optimization are embedded directly into the workflow, ensuring capital is deployed only after systematic evaluation.

From no-code platform to trading intelligence architecture

CoinQuant’s expansion reflects the evolution of its core engine. At the center of the platform is a unified intelligence system combining institutional-grade backtesting, structured market data from providers including Kaiko and Financial Modeling Prep, AI-powered optimization, and CoinQuant’s proprietary Domain Expert system.

Human traders interact through a natural language interface that allows them to describe, test, optimize, and deploy strategies without writing code. AI agents connect programmatically through API and MCP integrations to validate strategies and access structured data at scale.

The interface is only the surface. The intelligence engine beneath it is the product.

One engine, two growth vectors

This expansion represents a natural extension of CoinQuant’s business model. The platform’s growing base of over 15,000 traders validates product-market fit and generates structured strategy intelligence. The agent interface multiplies that value through high-volume programmatic validation and automation workflows.

Every strategy built, tested, and deployed contributes to an anonymized aggregated intelligence layer, creating a proprietary dataset mapping trading intent to logic, validation metrics, and performance outcomes across market conditions.

“The same engine that powers a trader’s first backtest can validate hundreds of strategies for autonomous systems in parallel. We are building one intelligence foundation for both humans and AI agents,” Ftouni added.

Automation layer launching next

CoinQuant is preparing to launch its automated strategy execution layer on HyperLiquid as its second major revenue stream.

The automation layer will enable validated strategies to transition seamlessly from backtest to live deployment within the same intelligence framework.

Raising $3 million to scale

CoinQuant is currently raising a $3 million Seed round to support product development, infrastructure scaling, and global expansion. The company is also developing HYDRA, a hierarchical multi-agent architecture designed for advanced research, risk modeling, and strategy optimization.

With over 15,000 users validating demand for structured trading intelligence, CoinQuant aims to become the intelligence backbone of algorithmic trading in the agent-driven financial era.

About CoinQuant

CoinQuant is an AI trading platform that enables traders and AI agents to build, validate, optimize, and automate trading strategies using natural language. Headquartered in Dubai, CoinQuant integrates with major exchanges and institutional data providers to deliver professional-grade trading infrastructure to a global community.

  • Websitehttps://coinquant.ai
  • Xhttps://x.com/CoinQuantX
  • Discordhttps://discord.gg/StNxg33z
  • Instagramhttps://www.instagram.com/coinquant.ai/
  • TikTokhttps://www.tiktok.com/@coinquant.ai
  • LinkedInhttps://www.linkedin.com/company/coinquant

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Perguntas relacionadas

QWhat is the core challenge that CoinQuant's new trading infrastructure aims to address for the agent economy?

AIt addresses the lag in financial infrastructure evolution, which has not kept pace with the rapid growth of autonomous AI agents in trading. It aims to provide structured validation, disciplined risk management, and intelligence infrastructure that are currently missing when AI agents connect directly to exchanges.

QWhat are the two primary user groups or growth vectors for CoinQuant's unified trading intelligence architecture?

AThe two primary user groups are human traders and autonomous AI agents. Human traders use the natural language interface, while AI agents connect programmatically via API and MCP integrations. Both groups utilize the same underlying intelligence engine.

QAccording to the article, what is the specific function of the intelligence layer CoinQuant introduces for autonomous AI agents?

AThe intelligence layer acts as a structured 'trust layer' between trading intent and live capital deployment. It enforces mandatory backtesting, risk analysis, and parameter optimization for all strategies—human or AI-generated—before they can go live, ensuring systematic evaluation.

QWhat are the two main components CoinQuant is developing or launching with the $3 million Seed funding, as mentioned in the article?

AThe two main components are: 1) The automated strategy execution layer launching next on HyperLiquid as a new revenue stream. 2) HYDRA, a hierarchical multi-agent architecture designed for advanced research, risk modeling, and strategy optimization.

QHow does CoinQuant's platform benefit from the strategies created by its user base?

AEvery strategy built, tested, and deployed contributes to an anonymized, aggregated intelligence layer. This creates a proprietary dataset that maps trading intent to logic, validation metrics, and performance outcomes across market conditions, enhancing the platform's core intelligence foundation.

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