CoinQuant Expands AI Trading Platform with New Intelligence Architecture

The agent economy is transforming financial markets, with open-source frameworks accelerating autonomous financial activities. AI agents are increasingly executing trades, managing portfolios, and interacting directly with exchanges. However, the financial infrastructure supporting this shift has not evolved at the same pace. According to Cointelegraph, CoinQuant, an AI-powered no-code trading platform with over 15,000 users, has announced its expansion into a unified trading intelligence architecture designed for both human traders and autonomous AI agents. Maan Ftouni, Founder and CEO of CoinQuant, emphasized that autonomous trading is no longer theoretical but a reality. The next phase requires structured validation, disciplined risk management, and intelligence infrastructure, which CoinQuant aims to deliver. As AI agents 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, ensuring no strategy goes live unvalidated. Backtesting, risk metrics, and parameter optimization are embedded directly into the workflow, ensuring capital is deployed only after systematic evaluation. CoinQuant’s expansion reflects the evolution of its core engine into a unified intelligence system. This system combines institutional-grade backtesting, structured market data from providers like Kaiko and Financial Modeling Prep, AI-powered optimization, and CoinQuant’s proprietary Domain Expert system. Human traders can interact through a natural language interface 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. 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. CoinQuant is preparing to launch its automated strategy execution layer on HyperLiquid as its second major revenue stream. This automation layer will enable validated strategies to transition seamlessly from backtest to live deployment within the same intelligence framework. CoinQuant is also raising a $3 million Seed round to support product development, infrastructure scaling, and global expansion. The company is 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.