AI-Powered Alpha Generation

Amundi embarked on a strategic initiative to enhance its investment process through cutting-edge AI.  They chose dakAI as their partner, attracted by their expertise in developing bespoke AI solutions and their commitment to a collaborative, co-creation approach.  Together, they embarked on a project to develop and implement advanced AI algorithms for trading and portfolio optimization, tailored specifically to Amundi unique investment strategies and risk management framework.

Phase 1 - Deep dive into data and strategy

dakAI's team of data scientists and financial engineers worked closely with Amundi traders and portfolio managers, gaining a deep understanding of their investment philosophies, trading strategies, and risk parameters. This collaborative effort focused on identifying the most valuable data sources, including market data, proprietary research, and alternative datasets, and integrating them into a unified, high-performance data infrastructure.

Phase 2 - Co-creating custom AI algorithms

Leveraging cutting-edge techniques in machine learning, including deep reinforcement learning and advanced neural networks, the joint team developed sophisticated trading algorithms. These algorithms were meticulously designed to analyze vast quantities of data in real-time, identify subtle patterns indicative of market opportunities, and execute trades automatically with optimal speed and precision.  Furthermore, dakAI helped develop and integrate generative AI models, specifically Large Language Models, to simulate diverse market scenarios, stress-test trading strategies, and enhance the robustness of the algorithms under various market conditions, allowing for more informed adjustments and risk mitigation.
‍

Phase 3 - Optimized portfolios and enhanced risk management

The collaboration extended beyond trading algorithms to encompass portfolio optimization. dakAI developed AI models that dynamically adjusted portfolio allocations based on client objectives, risk tolerance, and a comprehensive analysis of market factors, including macroeconomic trends and ESG considerations. These models continuously learned and adapted to evolving market conditions, ensuring portfolios remained optimally positioned for superior risk-adjusted returns.