Think Different

                                                               generated by meta ai Yes. In fact, causal  modelling  is one of the most advanced topics in quantitative finance and is becoming increasingly important because traditional ML models (LSTM, XGBoost, Transformers) often learn  correlations , whereas causal models aim  to discover why prices move. Since I am  pursuing an  MSc in Financial Engineering  at  WQU , I'll teach it at that level, covering  theory, mathematics, and Python implementations. Learning Roadmap We'll build from scratch. Correlation vs Causation Structural Causal Models (SCM) Directed Acyclic Graphs (DAGs) Causal Discovery Algorithms Do-Calculus (Pearl) Counterfactual Prediction Causal Forecasting Applying causal models to stock prediction Building an end-t...

This is a comprehensive topic, so I'll guide you through the key concepts and provide a simplified example. Keep in mind that a full, production-ready implementation involves more complexity, but this will give you a solid foundation. Understanding the Core Components Before diving into the example, let's understand why each tool is essential: MLflow : An open-source platform for managing the ML lifecycle. MLflow Tracking : Records parameters, metrics, and artifacts (models, datasets) of your experiments. Crucial for reproducibility and comparison. MLflow Models : Provides a standard format for packaging ML models, making them deployable across various platforms. MLflow Model Registry : A centralized hub for managing the lifecycle of ML models, including versioning, stage transitions (Staging, Production), and annotations. MLflow Projects : A format for packaging ML code in a reusable and reproducible way. Docker : A platform for developing, shipping, and running app...