Grace Investment Machine Raises $20 Million in Series A to Expand AI-Powered Investment Platform

Grace Investment Machine Raises $20 Million in Series A to Expand AI-Powered Investment Platform

Grace Investment Machine has completed a $20 million Series A funding round, marking its third capital raise within its first year of operations as the company accelerates the development of artificial intelligence for quantitative investing.

The financing was co-led by a leading U.S. venture capital firm and Hony Capital, with additional participation from IDG Capital and Monolith Capital.

Before the Series A round, the company had also secured more than RMB 100 million through its angel and angel+ financing rounds. The angel+ investment was led by SAIF Investment Fund, with participation from a family office associated with the chief executive of a major internet company.

Grace Investment Machine focuses on applying large-scale AI models to financial markets, developing proprietary systems designed to analyze time-series data and generate investment signals.

The company said it successfully validated the scaling laws of its proprietary financial time-series models with architectures reaching 8 billion parameters, representing a milestone in expanding AI capabilities for quantitative finance.

In addition, Grace Investment Machine introduced CogAlpha, a multi-agent signal mining framework developed to improve stock selection through collaborative AI models.

According to the company, CogAlpha achieved leading performance in CSI 300 stock selection benchmarks and was accepted for presentation at the ACL 2026 Main Conference, one of the leading international conferences dedicated to natural language processing and artificial intelligence.

The latest funding will support continued research and development, the expansion of the company’s AI infrastructure, and the commercialization of its quantitative investment technologies.

The announcement reflects continued momentum in AI-driven financial technology as companies expand the use of machine learning and large-scale models for investment research, portfolio management, and market analysis.

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