Machine Learning for Trading: Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems
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Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAP
- Build point-in-time pipelines, integrate alternative data, and ensure data integrity
- Build and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signals
- Deploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generators






















