Module 9: Machine Learning for Algorithmic Trading

• Introduction to Machine Learning Concepts
  • Supervised vs Unsupervised Learning
  • Feature Engineering for Trading
• Machine Learning Algorithms for Trading
  • Linear Regression, Decision Trees, Random Forest
  • Support Vector Machines (SVM), K-Nearest Neighbors (KNN)
• Predictive Modeling with Market Data
• Using Reinforcement Learning for Trading Strategies

Module 10: Advanced Strategy Design

• Statistical Arbitrage Strategies
• Option Pricing and Strategies (delta-neutral, straddles)
• Portfolio Optimization using Algorithms
• Risk Parity and Factor-Based Strategies
• Algorithmic Hedging Techniques

Module 11: Deployment and Automation

• Setting Up a Live Trading Environment
  • Connecting to Brokers via APIs
  • Handling Real-Time Data Feeds
• Algorithm Execution in Live Markets
  • Paper Trading vs Live Trading
  • Monitoring and Error Handling in Real-Time Systems
• Building a Trading Dashboard (Real-time P&L tracking, portfolio analytics)

Module 12: Regulatory, Ethical, and Risk Management Concerns

• Algorithmic Trading Regulations (SEC, MiFID II, etc.)
• Compliance and Audit Trails in Algo Trading
• Risk Monitoring and Limits
• Ethical Considerations in Automated Trading
• Data Privacy and Security in Algo Trading