Most traders have ideas. The difficult part has always been proving whether an idea is worth acting on.
A trader may notice a pattern in Bitcoin, believe a market signal works, or want to build a portfolio with specific risk limits. Yet testing those ideas properly has traditionally required coding skills, expensive software, or access to a quantitative research team.
CoinQuant was built to open new possibilities for traders, offering a clearer way to test ideas, make informed decisions, and explore strategies with greater confidence.
Founded by Maan Ftouni and headquartered in Dubai, CoinQuant was shaped by the resourcefulness and resilience required to build in Beirut. BDD connected the company with a community of founders and operators who share that practical, fast-moving approach.
Since launch, more than 20,000 users have signed up and completed over 100,000 backtests. We spoke with Maan about the problem CoinQuant is solving, how the platform works today, and what comes next.
BDD: More than 20,000 users have signed up and completed over 100,000 backtests. What do those numbers tell you?
The backtests matter more to me than the signups. Signups tell you that people are interested. One hundred thousand backtests tell you what they were looking for once they arrived: answers.
Most people already have market ideas. The problem is that they have not had an accessible way to test them before risking real money. For years, that kind of research required coding skills, professional software, or access to a quantitative team.
Almost everyone I knew had a market hypothesis, and almost none could test it. Getting an answer depends on whether you can code; that barrier decides who gets to test their ideas and who has to guess.
BDD: What does CoinQuant allow a user to do in practice?
A user can type a simple instruction, such as: “Buy the golden cross on Bitcoin. Sell at ten percent profit or after ten days, whichever comes first.”
CoinQuant turns that instruction into a structured strategy and tests it against years of historical market data. The user can then see key results, including historical performance, win rate, drawdowns, and the effect of real fees and slippage.
AI helps users formulate, explore, and refine an idea. But the results come from a deterministic testing engine using tick-level market data, real fees, and real slippage. As we say internally, “The AI debates, but the numbers are real.”
That distinction matters because a strategy can appear more successful than it truly is if a backtest overlooks trading costs or is repeatedly adjusted until historical data looks unusually favorable. We are building CoinQuant to give people an honest view of what an idea could have done under real market conditions.
BDD: CoinQuant is built around Build, Analyze, and Automate. What does that mean for the product’s future?
Build begins with an idea. A user describes what they want to test, and CoinQuant translates that into a structured strategy.
Analyze is about testing the strategy against real market data. The next stage of the Analyze roadmap will help users understand the market conditions under which a strategy stopped working in historical testing and alert them when similar conditions recur.
Automate closes the gap between research and live trading. The strategy that goes live will run through the same engine that tested it, using the same rules and logic rather than being rebuilt in a separate system. We also plan to provide an auditable record of live orders, so users can understand how their strategy is performing rather than trust a black box.
BDD: You are also building HYDRA. How does it expand CoinQuant’s capabilities?
The first version of CoinQuant was built around a simple idea: one sentence, one strategy, one answer.
HYDRA expands on that. A user will be able to set a broader objective, such as building a portfolio spanning crypto and US equities that meets specific risk and trading frequency requirements.
That requires a research process, not one backtest. HYDRA uses several AI agents to research possibilities, build strategies, challenge assumptions, and apply risk limits before presenting options that meet the user’s criteria.
The aim is to make this kind of research accessible to people with varying levels of market experience and access to financial tools and resources.
BDD: You have said that some future CoinQuant users may be autonomous AI agents. Why is that important?
Autonomous AI agents are beginning to research, test, and refine strategies independently. They will need reliable data, transparent results, and clear limits before they can responsibly operate in financial markets.
No one should give an AI system a trading budget based solely on a prompt. That is why we are building controls such as spending allowances, position limits, and vetoes that remain in place regardless of what an AI model recommends.
As automated trading grows, trust becomes more important, not less. Verifiable results will matter more than impressive claims. Humans and AI agents should be held to the same standard of proof.
BDD: CoinQuant is headquartered in Dubai, but its operating mindset was shaped in Beirut. How did BDD influence the company?
Building in Beirut teaches you to be resourceful, resilient, and honest about what it takes to build and run a product. You learn to move fast, focus on what matters, and solve real problems under real constraints.
BDD gave that mindset a community. It connected CoinQuant with founders and operators who share a practical approach to building. CoinQuant is headquartered in Dubai, but its operating system is Beirut.
BDD: What comes next for CoinQuant?
We are raising a $3 million seed round to expand into more markets, exchanges, and languages, while continuing to develop our live automation and AI research capabilities.
The goal is to make rigorous market research more accessible. When someone has an idea about a market, whether they are an experienced trader or just beginning to explore a strategy, testing it should be the obvious first step.
For traders, that means a clearer path from instinct to evidence, with the tools to understand a strategy before putting real money behind it.
