Trading stocks at 9, authorized by banks at 15! 22-year-old Canadian youth builds a billion-dollar quant empire

22-year-old Canadian Denis Dariotis founded the quantitative trading software company GoQuant, processing over $1 billion in trades daily through quantitative strategies and data analytics. He began trading stocks at age 9, and at 15, licensed his quantitative strategies to a major Canadian bank, landing his first major client. GoQuant has now raised $7 million and employs around 80 people.

Refusing Teacher’s Screen Monitoring in 9-Year-Old’s Stock Trading Class

At only 22, Denis Dariotis has already walked a path of success many envy. He started trading stocks at age 9, caught the attention of a bank at 15, and is now expanding into the crypto world with his quantitative trading software company, GoQuant. In an interview with CoinDesk, Dariotis recalled his early interest in stock trading at 9 and once told his teacher he needed to take 10 minutes during market open and close to check his investment portfolio.

When the teacher tried to check what was on his computer screen, he shut his laptop and said it was private, refusing the teacher’s request. This detail vividly demonstrates Dariotis’s independence and keen awareness of financial privacy from a young age. While most 9-year-olds were playing games, he was already tracking stock trends and managing his own portfolio. This precociousness was no accident, but a product of his family environment.

Inspired by CNBC financial news playing at home and Warren Buffett’s investment philosophy, Dariotis began learning programming around age 11 or 12, moving from web development to Python and C++. This marked his transition from stock trading enthusiast to quantitative trader. He used his computer skills to automate the processing of massive trading data, allowing him to focus on researching trading strategies. This integration of technology and finance is at the heart of modern quantitative trading.

Dariotis’s Early Growth Trajectory

Age 9: Began trading stocks, insisted on checking his portfolio during class

Ages 11-12: Learned programming, mastered Python and C++

Age 13: Started automating data processing and backtesting strategies

Age 15: Licensed his quantitative strategies to a major Canadian bank

At 13, he realized he was spending too much time manually scanning large datasets and began thinking about automating the process through his computing knowledge. Although he didn’t yet know the term “quantitative trading,” he was already conducting backtests and deeply researching portfolio construction, optimization, and risk management, delving into every detail of how quantitative markets operate.

First Major Client: Licensing to a Bank at 15

At 15, he achieved a remarkable breakthrough by licensing his quantitative strategies to a major Canadian bank—his first big client—and subsequently consulted for several other investment management companies. This accomplishment is almost unheard of in the fintech world. A minor convincing a tightly regulated bank to adopt his trading strategies is a testament to both the strategies’ effectiveness and Dariotis’s persuasiveness.

An interesting memory: at a data science conference in New York, a large hedge fund tried to hire Dariotis on the spot, but was shocked to learn he was only 15. This anecdote shows his professional level was high enough to make top Wall Street firms consider making exceptions. Hedge funds usually only hire professionals with elite degrees and years of experience, so the fact that a 15-year-old caught their interest speaks volumes for his talent.

From age 15 to 22, Dariotis never stopped exploring. After several years in traditional investment markets, he turned his focus to crypto. This pivot was not just following a trend, but based on deep insights into market structure flaws. He noticed the crypto market was primarily retail-driven and lacked true institutional-grade infrastructure, with liquidity fragmented across centralized exchanges, decentralized exchanges, and OTC desks—leading to severe market fragmentation.

This fragmentation causes multiple issues. First, significant price differences exist for the same asset across platforms, and while arbitrage opportunities exist, execution costs are high. Second, large trades are difficult to complete on a single platform without impacting price, requiring execution across multiple venues. Third, dispersed liquidity makes price discovery inefficient, increasing market volatility. These issues are major obstacles for institutional investors used to mature infrastructure in traditional finance.

After applying data tools in the crypto market, he noticed trade venues had delays in order book updates and determined that building a comprehensive infrastructure stack was the best solution. This idea led to the creation of the GoQuant crypto quantitative platform.

GoQuant’s Tech Stack Processes $1 Billion Daily

As of January 2025, GoQuant has raised $3 million in a pre-seed round and $4 million in a seed round led by crypto trading firm GSR—for a total of $7 million, a significant vote of confidence for a company led by a 22-year-old founder. GSR, one of the most professional market makers and trading firms in crypto, leading the round signals strong recognition of GoQuant’s technical strength and market positioning.

Currently, GoQuant processes over $1 billion in daily trading volume and has about 80 employees across the US, Europe, India, the Philippines, and Morocco. Processing $1 billion in daily trades is an astonishing feat, making GoQuant a key part of crypto market infrastructure. This level of volume requires extremely high reliability and execution efficiency, as any delay or error could lead to major losses.

GoQuant’s core value lies in solving liquidity fragmentation. The platform integrates liquidity from multiple centralized exchanges, decentralized exchanges, and OTC desks, providing institutional clients with a unified execution interface. When clients need to execute large trades, GoQuant’s algorithm automatically splits orders across platforms to minimize price impact and achieve optimal execution. This smart order routing technology is central to modern quantitative trading.

GoQuant recently launched an institutional dark pool product, “GoDark,” and a credit platform, “GoCredit,” which is negotiating about $500 million in crypto loans. Dark pools allow institutions to execute large trades without publicly revealing order information—a common feature in traditional finance, but still rare in crypto. GoDark fills this gap, offering a solution for large buyers who don’t want to expose their trading intentions.

GoCredit opens another commercialization path. Crypto lending is a huge and rapidly growing market, with institutional clients needing to borrow funds for leveraged trading or arbitrage. The $500 million in crypto loans GoCredit is negotiating demonstrates quickly growing trust among institutional clients. This credit business not only generates revenue but also deepens client relationships and increases client stickiness.

Building an Ecosystem to Avoid Product Silos

Discussing future vision, Dariotis emphasized that GoQuant aims to become the core of value movement. He says GoQuant is positioned mainly as a technology provider, not a financial intermediary. With the rise of prediction markets and asset tokenization, the future will be a world where “everything is tradable,” so the market will need a platform that efficiently connects all trading activities.

This vision reveals Dariotis’s deep understanding of industry trends. Tokenization is turning traditionally illiquid assets (such as real estate, art, and private equity) into digital assets freely tradable on blockchains. Prediction markets allow people to bet on and trade forecasts of future events. These innovations require robust trading infrastructure. GoQuant aims to be the pipeline layer of this emerging ecosystem, connecting all trading venues and asset types.

The talented young Dariotis also offers practical advice for aspiring entrepreneurs. He believes founders must stay flexible and be willing to pivot when needed. Using himself as an example, he notes that while just handling data could have been successful, he recommends avoiding the creation of “product silos.” Even if a single product could be worth $100 million, building a fully connected ecosystem could multiply potential value.

This advice is based on his real experience. GoQuant may have started as an order execution tool, but as the business grew, it expanded into dark pool trading, credit services, and data analytics. These products complement each other, offering clients a one-stop solution. Clients using GoQuant for trading naturally use its credit services for leverage or its dark pool product for large orders. This cross-selling and ecosystem synergy means the overall value far exceeds the sum of the parts.

For young entrepreneurs, this is highly inspiring advice. Many startups focus excessively on perfecting a single product, neglecting the importance of ecosystem building. In a fiercely competitive market, a single product is easily copied or replaced, but a complete ecosystem has much stronger moats and network effects.