Core Insights - The article highlights the significant financial success of Huanfang Quantitative, which is projected to earn 5 billion RMB in 2025, allowing for the training of 2,380 DeepSeek R1 models [1] - Huanfang Quantitative, led by Liang Wenfeng, ranks second among large quantitative funds in China with an average return rate of 56.6% and manages over 70 billion RMB [1] - The revenue generated from Huanfang's management fees and performance fees has provided DeepSeek with substantial funding for its AI research, enabling it to operate independently without external financing [2] Financial Performance - Huanfang Quantitative's earnings of approximately 5 billion RMB last year surpassed the pre-IPO fundraising of AI unicorn MiniMax [1] - The average management fee of 1% and performance fee of 20% contributed significantly to Huanfang's revenue [1] AI Development - DeepSeek's training costs are relatively low, with the R1 model costing only 294,000 USD and the V3 model costing 5.576 million USD, allowing for extensive model training with the funds available [2] - The financial model creates a symbiotic relationship where profits from quantitative trading support AI research, while AI technology enhances quantitative strategies [2]

Core Insights - David Itkin, an assistant professor at LSE, has developed a new method for assessing price impact in trade portfolios, which has been recognized with the Risk.net award for rising star in quant finance [1] - The research simplifies previous methods and demonstrates that a linear approach can effectively model nonlinear price impacts, providing a practical solution for portfolio managers [10][18] Research Development - The idea originated from Peter Schmidt, who was exploring portfolio optimization strategies during his master's at Imperial in 2022, focusing on the relationship between price impact and other factors [2] - Johannes Muhle-Karbe proposed extending the research into a full paper, leading to collaboration with Itkin, who contributed to the theoretical framework [3] Methodology - The paper titled "Tackling nonlinear price impact with linear strategies" was submitted in 2023 and published in the following year, addressing the common assumption of quadratic costs in portfolio optimization [3] - Itkin's approach involves selecting the right linear strategy through an optimization procedure, which simplifies the modeling of price impact [5][8] Findings - The research reveals that while naive linear strategies can lead to performance losses, optimizing the "effective" quadratic cost parameter can enhance performance [7] - The proposed method allows for analytical calculations without the need for computationally intensive simulations, making it accessible for practitioners [10] Performance Comparison - The linear policy developed in the research was benchmarked against a nonlinear optimizer, showing a 2% performance drop, which may be acceptable for many firms given the lower computational costs [17] - The findings provide reassurance to portfolio managers that approximations can yield nearly optimal results, addressing a significant concern in the industry [18] Future Directions - A follow-up paper is in progress, which will incorporate the effects of impact decay and contribute further to the understanding of price impact functions using neural network strategies [18]