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Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

By Decrypt
Apr 15, 2026
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Hong Kong, HongKong, April 15th, 2026

Evolving the Retail Trading Paradigm

With the proliferation of perp DEXs, RWAs, and prediction markets, financialization of instruments and choice of trading venues have reached an all-time high. As markets are becoming increasingly efficient, and adversarial players are becoming more diverse as well as institutional, retail investors' trading edge is being increasingly eroded. AlphaNet has built a platform that aims to level the playing field by providing users with end-to-end automated trading from a collection of institutional-grade alpha sources at their disposal.

End-to-End Platform for Systematic Alpha Generation

AlphaNet focuses on providing users with “systematic” alpha - sources of superior risk-adjusted return for users that can be measured, tracked, and assessed quantitatively over time. The main offering is a SOTA end-to-end platform that includes an institutional-grade quantitative strategy marketplace, one-click strategy deployment, seamless execution via proprietary trading algorithms, and tools to manage, track, and tweak the users’ own strategy-mix in real time.  

Incubated by Asia prop trading firm Tensor Investment, AlphaNet is the only platform that combines deep learning-based strategies, low-latency execution algos, AI compute for training and inference, and liquidity via a DEX-based protocol in one seamless turnkey solution. The platform does the heavy lifting and the users get to access proprietary alpha that was previously only available to institutional investors.

Open Platform and Agentic Future

AlphaNet currently has over 30 high-Sharpe strategies and a user retention rate of over 98%. All of the strategies are currently provided by Tensor, but towards the end of Q2 the Open Platform is expected to launch, enabling other prop trading teams to integrate their quant strategies and alphas. All strategies on the platform require undergoing AlphaNet’s proprietary robustness testing and alpha decay mitigation framework before being allowed to be live on the platform, and will leverage AlphaNet’s shared trading execution and inference infrastructure.

By the end of Q2, 100+ strategies are expected to be on AlphaNet, and a novel agentic system for personalized strategy selection and deployment is currently being developed to help users navigate through the myriad of alphas and automate deployment and monitoring.

Experience, Resources, and Alignment via Capital

Through this funding round, AlphaNet benefits from the operational expertise of Joffre Capital, whose multibillion dollar portfolio include private and public companies with the likes of Investing.com, Grindr, and Coins.ph (Southeast Asia’s biggest crypto exchange). Huashan Capital, early investor of companies such as Airwallex, AI Rudder, and Moonshot.ai (Kimi) helps bring expertise and resources in scaling of tech platforms and fundraising. 

And finally, the quantitative trading consortium brings proprietary strategies, compute resources, and algorithms to AlphaNet’s offerings to help it grow as the first institutional-grade end-to-end open alpha platform.

About AlphaNet

AlphaNet is a SOTA quantitative AI trading platform that provides users institutional-grade alpha, proprietary algo execution, and automated trading in one seamless end-to-end solution.

Peter Yudee

Disclaimer: The information on this page may have been obtained from third parties and does not necessarily reflect the views or opinions of BitKan. This content is provided for general informational purposes only, without any representation or warranty of any kind, nor shall it be construed as financial or investment advice. BitKan shall not be liable for any errors or omissions, or for any outcomes resulting from the use of this information. Investments in digital assets can be risky. Please carefully evaluate the risks of a product and your risk tolerance based on your own financial circumstances. Products mentioned in this article may not be available in your region.

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