About This Webinar
Discover how to leverage machine learning algorithms to predict stock price movements and optimize your trading strategies. This advanced webinar covers neural networks, ensemble methods, and real-world ML deployment for financial markets.
What You'll Learn
- ✓Understand different ML approaches for financial prediction
- ✓Build and train neural networks for price forecasting
- ✓Implement ensemble methods (Random Forests, XGBoost)
- ✓Feature engineering for financial time series
- ✓Avoid common pitfalls like overfitting and look-ahead bias
- ✓Deploy ML models in production trading systems
- ✓Evaluate model performance with proper backtesting
Agenda
ML in Finance Overview
Introduction to machine learning applications in trading, common algorithms, and success stories.
Data Preparation
Feature engineering, normalization, handling missing data, and creating training datasets.
Building ML Models
Hands-on implementation of neural networks and tree-based models using scikit-learn and TensorFlow.
Model Validation
Cross-validation techniques, avoiding overfitting, and realistic performance evaluation.
Production Deployment
Integrating ML predictions into trading systems, monitoring model drift, and retraining strategies.
Q&A & Case Studies
Real-world examples and answers to your questions.
Topics Covered
Speakers
Alex Chen
Lead ML Engineer at Quantum Capital
Former Google AI researcher with 8 years of experience applying ML to financial markets. Published author in top ML conferences.
Dr. Maria Rodriguez
Quantitative Researcher & Data Scientist
PhD in Statistics from MIT. Specialized in time series analysis and predictive modeling for hedge funds.
Who Should Attend
- •Data scientists with basic ML knowledge
- •Quantitative analysts exploring advanced techniques
- •Algorithmic traders looking to incorporate AI
- •Financial professionals with Python/ML experience
- •Graduate students in finance, CS, or statistics
Prerequisites
- •Solid Python programming skills
- •Basic understanding of machine learning concepts
- •Familiarity with pandas, numpy, and scikit-learn
- •Understanding of financial markets and trading
- •Recommended: Previous experience with trading algorithms