幻方量化去年收益率56.6% 为DeepSeek提供超级弹药

Core Insights - The article highlights the impressive returns of Fantom Quantitative, which achieved an average return of 56.55% in 2025, ranking second among quantitative private equity firms in China, only behind Lingjun Investment with a return of 73.51% [1] - Fantom Quantitative's average return over the past three years is 85.15%, and 114.35% over the past five years, providing substantial funding support for DeepSeek's large model research [2] - Founded in 2015 by Liang Wenfeng, Fantom Quantitative focuses on AI quantitative trading and has a current management scale exceeding 70 billion yuan, maintaining a leading position in the domestic private quantitative investment sector [2][3] Company Overview - Fantom Quantitative has a team composed of award-winning mathematicians, physicists, and experts in AI, employing interdisciplinary collaboration to tackle challenges in deep learning, big data modeling, and quantitative analysis [2] - The company has been utilizing machine learning for fully automated quantitative trading since 2008 and has expanded rapidly since its inception [2] - Significant investments were made in AI training platforms, with "Firefly No. 1" established in 2019 and "Firefly No. 2" in 2021, leading to the establishment of DeepSeek in July 2023 [3] Financial Performance - Liang Wenfeng holds a majority stake in Fantom Quantitative and has ceased to introduce external funding for the fund, indicating a strong accumulation of capital for supporting large model research [4] - The strong performance of Fantom Quantitative is estimated to have generated over 700 million USD in revenue last year, assuming a 1% management fee and 20% performance fee [4] DeepSeek Developments - DeepSeek's V3 model has a total training cost budget of 5.57 million USD, while competitors like Zhizhu and MiniMax have reported significant R&D expenditures [5] - DeepSeek plans to release its next-generation AI model, DeepSeek V4, around the Lunar New Year, which is expected to surpass current leading models in programming capabilities [5]