Implementation & Performance
Algocracked Implementation Labs - Master the application of Reinforcement Learning to financial markets, from environment design to production-grade agents.
Foundations of RL in Finance - Map trading problems to the Markov Decision Process (MDP) framework Environment Design & Reward Engineering - Build high-performance custom environments using the Gymnasium (formerly Gym) API Policy Gradient Methods (PPO & A3C) - Implement Proximal Policy Optimization (PPO) for stable agent training Deep Deterministic Policy Gradient (DDPG) & Soft Actor-Critic (SAC) - Master RL in continuous action spaces for precise position sizing Advanced Topics - Multi-Agent RL & HFT Applications - Understanding Multi-Agent Reinforcement Learning (MARL) for market making Agentic AI & LLM Integration (2026 Update) - Integrate LLMs (FinGPT, Llama-4) for sentiment-driven state representation
The 2026 RL Quant Fund.
