Margarita Finance Unveils AI-Powered Derivative Trading on Blockchain
Margarita Finance has launched the world's first AI-powered agent to trade institutional-grade derivative strategies on the blockchain, announced in a press release. This innovation aims to bring institutional-grade investment strategies on-chain, enhancing efficiency and lowering the barrier to entry for sophisticated trading in decentralized finance.
The AI agent autonomously trades derivatives with full transparency, bridging the gap between traditional finance (TradFi) and decentralized finance (DeFi). By eliminating human intervention in trading, the AI agent executes advanced strategies, with each trade verifiable on the blockchain. Margarita Finance also provides real-time updates on key trades through a dedicated AI-powered profile.
This breakthrough not only benefits institutional trading but also offers everyday investors greater access to sophisticated financial strategies. The technology automates complex decision-making and ensures full transparency, allowing investors to engage with institutional yield without needing a deep background in traditional finance.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Finance AI Weekly or Daily AI Brief.
Also, consider following us on social media:
More from Finance
Oct 16 Ant Digital Says Banks Want Agent Factories, Not Standalone AI Tools Oct 16 OpenAI Agent Uses Unauthorized Crypto Wallet to Complete Task Oct 16 Intuit Agrees to Acquire AI Billing Startup Sequence for Reported $200 Million Oct 16 Matters Raises $5 Million to Build AI Consumer Bank Oct 16 HKMA Introduces ART Framework for AI Use in BankingFinance AI Weekly
Weekly newsletter about AI in finance. Covers AI-driven trading, fintech innovations, and data analytics transforming markets
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
EDGE Markets Adds Controlled Capital Access for AI Agents
Ant International Adds AI Agents Across Financial Services
Infrastructure AI Introduces Agentic Asset Valuation for Buildings
Public Launches AI Agents for Prediction Markets
MEXC Survey Finds 85% of Crypto Traders Use AI
Daily AI Brief: the AI news that matters, in your inbox.