CoinQuant Surpasses 15,000 Users as No-Code AI Trading Opens Quant Investing Across 16,000+ Assets

CoinQuant recently announced that it has surpassed 15,000 users within just 90 days of launch, a milestone that was subsequently featured across multiple media outlets, including NewsBreak. This article builds on that announcement by exploring why the milestone matters, what it says about the evolution of quantitative investing, and how AI is making professional-grade trading tools more accessible than ever.

CoinQuant has surpassed 15,000 users, all within the first 90 days following its launch in January 2026. The growth reflects rising demand for accessible, AI-powered investment tools and signals a clear shift: the technical wall that once kept quantitative trading out of reach for most investors is coming down.

CoinQuant, an AI trading platform, is helping democratize quantitative investing by enabling anyone to build, test, and automate trading strategies using plain English or voice commands, without coding skills or a finance background. With strategy creation now spanning more than 16,000 assets across multiple asset classes, users get a single environment to research and automate far beyond a single market.

Why 15,000 Users in 90 Days Is the Story

Crossing 15,000 users this quickly is more than a vanity number. It is evidence that a large group of investors has long had market insight but lacked the technical means to act on it systematically.

Traditional quantitative trading platforms often require extensive programming knowledge, technical expertise, and complex workflows that can discourage newcomers. CoinQuant aims to eliminate these barriers by turning strategy development into a simple conversational experience. The early adoption suggests that when the barrier disappears, the appetite for disciplined, rules-based investing is far larger than the market assumed.

What Quant Investing Actually Means (And Why It Matters Here)

To understand the milestone, it helps to understand what these 15,000 users are now able to do.

Quantitative investing means trading by defined rules rather than gut feel. Instead of "I think this will go up," you specify entry conditions, exit conditions, position sizing, and risk limits, then test those rules against historical data before committing capital. The discipline removes emotion and makes every decision measurable.

The concept was never the obstacle. The implementation was. That is precisely the gap CoinQuant's growth is closing.

From Plain English to an Executable Strategy

The core unlock behind the adoption is natural language. Users describe a trading idea, whether based on price action, technical indicators, market conditions, or risk management rules, and CoinQuant's AI generates a structured, executable trading system from that idea, scores its quality, and synthesizes a full backtest with risk evaluation, handling the technical implementation end to end.

Rather than learning programming languages or building algorithms from scratch, users focus on their investment ideas while the AI does the engineering. For newcomers, this also creates a powerful way to learn by doing: articulate a hypothesis, watch it become a real system, and study why it works or fails.

A Confidence Framework Before You Commit Capital

A major reason new users stay is that CoinQuant builds in a check most beginners skip.

CoinQuant's proprietary Strategy Quality Score (SQS) evaluates every strategy on a scale from 0 to 100 before deployment. The score gives traders an objective benchmark for robustness, consistency, and risk-adjusted reliability across market regimes. By providing a standardized confidence framework, it gives users insight into a strategy's strengths and weaknesses before they put money behind it, and builds the habit of evaluating a strategy rather than chasing a single lucky result.

Institutional-Grade Backtesting in Seconds

Backtesting is where many of those 15,000 users first see the value.

The platform delivers backtesting powered by institutional-grade, tick-level historical market data. Within seconds, users receive detailed performance reports that include fee-adjusted analytics, drawdown evaluations, risk assessments, and other key metrics.

The educational point worth knowing: backtest quality depends entirely on data resolution. Tick-level data captures every trade and is far more precise than daily candles, and fee-adjusted results reflect reality rather than fantasy returns. The high-resolution data lets investors validate ideas with a level of precision typically reserved for professional trading desks, without spending hours configuring complex testing environments.

Learning From a Community

Beyond strategy development and testing, CoinQuant is building a collaborative ecosystem for traders. Its community marketplace lets users discover, clone, customize, and share automated trading strategies.

For beginners, this is one of the fastest ways to learn. Studying how an experienced trader structured their rules teaches more than any tutorial, and it helps traders accelerate research and explore new methodologies without starting from scratch.

Built for the Coming Wave of AI Agents

CoinQuant is also built to serve the emerging wave of autonomous AI agents. Through its Model Context Protocol (MCP) interface, agents such as OpenClaw and Hermes can connect directly to the platform to research, build, and run trading strategies programmatically, a glimpse of where systematic investing is heading as the line between research and execution disappears.

In the Founder's Words

"Many investors have strong market insights but lack the technical skills needed to turn those ideas into executable systems," said Maan Ftouni, Founder and CEO of CoinQuant. "Our goal is to remove the complexity traditionally associated with algorithmic trading and give every investor access to professional-grade tools through a simple, conversational interface."

What Comes Next

Looking ahead, CoinQuant is extending its automation infrastructure so users can move seamlessly from research and testing to live execution. The company believes automation represents the final step in making advanced investing tools truly accessible, allowing users to deploy strategies with greater efficiency and confidence.

As AI adoption accelerates across financial services, investors increasingly seek platforms that combine intelligence, automation, and usability. By integrating strategy creation, backtesting, risk evaluation, community collaboration, and execution into a single environment, CoinQuant is positioning itself as a next-generation solution for modern traders. The 15,000-user milestone is an early signal that this approach is resonating.

About CoinQuant

CoinQuant is an AI trading platform that enables traders and AI agents to create, test, and automate trading strategies using natural language and voice commands. The platform combines conversational AI, tick-level backtesting technology, proprietary strategy scoring, and collaborative strategy sharing to make professional-grade trading tools accessible to investors of all experience levels.

Ready to turn your market ideas into tested strategies? Start free at https://app.coinquant.ai/?ref=NSfBKvpv