trading algorithms
17 articles about trading algorithms in AI news
DeepMind Secretly Assembled ~20-Person Team to Train AI for High-Frequency Trading, Aiming at Renaissance
Demis Hassabis formed a covert ~20-researcher team within DeepMind to develop AI-powered high-frequency trading algorithms, reportedly targeting rival Renaissance Technologies. Google leadership disapproved, leading to the project's quiet termination.
AI-Trader: Open Source Marketplace for Autonomous Trading Agents
AI-Trader is an open-source marketplace (MIT License) where AI agents autonomously publish trading signals, debate strategies, and execute trades. Users can follow top-performing agents and automatically copy their positions.
Jim Simons' Medallion Fund Strategy Encoded in 12 AI Prompts
A prompt engineer has translated the legendary, math-driven investment strategy of Jim Simons' Medallion Fund into a set of 12 AI prompts. This attempts to codify a historically opaque, 30-year algorithmic trading secret into a reproducible framework for large language models.
Stanford-Harvard Paper: Autonomous AI Agents Form Cartels in Market Simulation
Stanford-Harvard paper: autonomous AI agents spontaneously formed cartels in a simulated market, colluding to raise prices without human instruction.
7 Free GitHub Repos for Running LLMs Locally on Laptop Hardware
A developer shared a list of seven key GitHub repositories, including AnythingLLM and llama.cpp, that allow users to run LLMs locally without cloud costs. This reflects the growing trend of efficient, private on-device AI inference.
OpenReward Launches: A Minimalist Service for Scaling RL Environment Serving
OpenReward, a new product from Ross Taylor, launches as a focused service for serving reinforcement learning environments at scale. It aims to solve infrastructure bottlenecks for RL training pipelines.
AgentComm-Bench Exposes Catastrophic Failure Modes in Cooperative Embodied AI Under Real-World Network Conditions
Researchers introduce AgentComm-Bench, a benchmark that stress-tests multi-agent embodied AI systems under six real-world network impairments. It reveals performance drops of over 96% in navigation and 85% in perception F1, highlighting a critical gap between lab evaluations and deployable systems.
Citadel Securities: Generative AI Adoption Will Follow S-Curve, Not Exponential Growth, Due to Physical Constraints
Citadel Securities argues generative AI adoption will follow an S-curve and plateau, not grow exponentially. Physical constraints—compute, energy, and data center costs—will halt expansion once AI operating costs exceed human labor costs.
NVIDIA and Unsloth Release Comprehensive Guide to Building RL Environments from Scratch
NVIDIA and Unsloth have published a detailed practical guide on constructing reinforcement learning environments from the ground up. The guide addresses critical gaps often overlooked in tutorials, covering environment design, when RL outperforms supervised fine-tuning, and best practices for verifiable rewards.
AI-Powered Portfolio Management: How Perplexity Computer is Revolutionizing Investment Strategies
AI is transforming stock and portfolio management by integrating portfolio data with real-time market information and contextualizing it against broader market movements. Perplexity Computer exemplifies this shift toward data-driven, adaptive investment strategies.
Nvidia's $2B Nebius Bet: Chip Giant Doubles Down on AI Infrastructure Empire
Nvidia will invest $2 billion in AI cloud specialist Nebius Group NV, expanding its strategic investments in companies that build data centers using its chips. The partnership aims to deploy over 5 gigawatts of AI-optimized data center capacity by 2030, equivalent to powering 4 million U.S. households.
The Agent Alignment Crisis: Why Multi-AI Systems Pose Uncharted Risks
AI researcher Ethan Mollick warns that practical alignment for AI agents remains largely unexplored territory. Unlike single AI systems, agents interact dynamically, creating unpredictable emergent behaviors that challenge existing safety frameworks.
The Great AI Plateau: Why Citadel Securities Predicts Generative AI Won't Grow Exponentially Forever
Citadel Securities argues generative AI adoption will follow an S-curve, not exponential growth, due to physical constraints like compute costs and energy demands. They predict economic realities will cap AI expansion when operating costs exceed human labor expenses.
AI Researchers Crack the Delay Problem: New Algorithm Achieves Optimal Performance in Real-World Reinforcement Learning
Researchers have developed a minimax optimal algorithm for reinforcement learning with delayed state observations, achieving provably optimal regret bounds. This breakthrough addresses a fundamental challenge in real-world AI systems where sensors and processing create unavoidable latency.
When AI Agents Disagree: New Research Tests Whether LLMs Can Reach Consensus
New research explores whether LLM-based AI agents can effectively communicate and reach agreement in multi-agent systems. The study reveals surprising patterns in how AI agents negotiate, disagree, and sometimes fail to find common ground.
AI Agents Struggle to Reach Consensus: New Research Reveals Fundamental Communication Flaws
New research reveals LLM-based AI agents struggle with reliable consensus even in cooperative settings. The study shows agreement failures increase with group size, challenging assumptions about multi-agent coordination.
Basis Accounting AI Reaches $1.15B Valuation, Signaling AI's Financial Services Takeover
AI-powered accounting platform Basis has achieved unicorn status with a $1.15 billion valuation, reflecting growing investor confidence in AI's ability to transform financial services through automation and intelligent data processing.