Moonberg Drops QuantFi Trading Platform at Davos Summit

Moonberg rolled out QuantFi last week during the World Economic Forum in Davos, bringing a new quantitative trading platform that opens up the company’s massive crypto data setup to regular traders and AI systems. The launch marks another big push from Moonberg to get more quantified intelligence into crypto markets.

Since day one, Moonberg focused on grabbing and crunching crypto market data from tons of different sources across the space. The firm built its own algorithms like MoonScope, which delivered some pretty solid numbers so far: 79.1% win rate for trades that gained over 25%, plus a 64.5% win rate for those exceeding 50%. These results came from roughly 110 trading signals each week that the system generated.

QuantFi basically democratizes access. No more coding required.

The platform wants to make all that data infrastructure available through a no-code interface, cutting out the technical barriers that kept quantitative trading locked up in big institutions with deep pockets. A managed cloud platform will follow soon after the initial rollout begins.

Joah Santos, Co-Founder at Moonberg, said: “We’ve always been about data first and foremost. We validated our infrastructure using our own algorithms and now we’re enabling builders and traders to create quantitative strategies without needing any coding skills whatsoever.” Santos didn’t specify exact timeline details for the full platform launch during the Davos announcement.

AI agents need better data access these days. Moonberg’s integration with the Model Context Protocol gives AI systems consolidated access to various data points including on-chain metrics, social sentiment readings, technical indicators, DeFi protocol health checks, and wallet activity patterns. The protocol connection seems pretty crucial for the platform’s success.

Users can build quantitative formulas through a visual interface and deploy algorithms without writing a single line of code. Both human traders and AI agents can use the infrastructure to execute trading decisions on their own. The setup supports autonomous trading operations across different market conditions.

Moonberg’s team includes former executives from major players like Binance, Kraken, UBS, and Airbnb. Their combined experience covers traditional finance, cryptocurrency exchanges, and technology platforms. The leadership brings serious credentials to the project.

QuantFi will roll out in phases, with initial access going to existing Moonberg community members first. The platform’s main goal centers on democratizing access to high-level crypto data infrastructure as markets shift toward AI-driven trading systems. Community feedback will shape the platform’s development during early testing phases.

But the real test comes later. Market conditions change fast in crypto, and even the best algorithms can struggle when volatility spikes or unexpected events hit. Moonberg’s 79.1% win rate looks good on paper, though past performance doesn’t guarantee future results in this space.

The Davos launch timing wasn’t random either. Santos and the team picked the World Economic Forum because it gathers global financial leaders and influencers who might become early adopters or partners. The venue gave QuantFi immediate credibility and exposure to decision-makers across traditional finance and crypto sectors.

Early access starts with Moonberg’s existing community, which gives the company a built-in testing group for feedback and improvements. Santos emphasized that user input will drive platform refinements: “Community involvement remains integral to how we refine features and functionality going forward.” He expects the feedback loop to accelerate development cycles.

The platform aggregates over 700 different data points for users to work with. That’s a massive amount of information compared to what most individual traders can access on their own. Social sentiment, technical indicators, wallet movements, DeFi health metrics – all of it gets fed into the system for analysis and strategy building.

Moonberg’s no-code approach could attract traders who understand markets but lack programming skills. Traditional quantitative trading required serious technical knowledge that kept many potential users out. The visual interface removes that barrier completely.

The Model Context Protocol integration sets QuantFi apart from competitors in the space. AI systems can pull from multiple data sources simultaneously instead of connecting to each one separately. For AI agents executing trades, that consolidated access means faster decision-making and better strategy execution.

Santos didn’t reveal specific pricing details during the Davos announcement. The phased rollout approach suggests Moonberg wants to test demand and refine the platform before setting final pricing structures. Community members will probably get preferential rates during early access periods.

Market timing looks favorable for the launch. More traders want AI-powered tools, and institutional interest in crypto quantitative strategies keeps growing. Moonberg positioned itself right in the middle of both trends with QuantFi.

The company continues providing comprehensive crypto market data and analytics beyond just the QuantFi platform. Their broader data infrastructure supports the new trading tool but also serves other clients across the industry. Contact details include Joah Santos at [email protected] for more information about early access registration.

The crypto quantitative trading market reached $1.7 billion in assets under management by late 2023, according to PwC research. Major hedge funds like Pantera Capital and Galaxy Digital already deploy sophisticated algorithmic strategies, but smaller players faced technical barriers until now.

Moonberg competes directly with established platforms like TradingView and 3Commas, though neither offers the same depth of crypto-specific data integration. The company’s 700+ data points exceed most competitors by significant margins, potentially giving QuantFi users substantial analytical advantages in volatile market conditions.