Practical Financial Machine Learning: A Hands-On Guide from Data to Deployment

by Ali AZARY

Overview

Unlock the power of machine learning in quantitative finance. This comprehensive guide takes you from foundational data structuring and feature engineering to advanced model building, rigorous backtesting, and modern portfolio management.

Learn to implement cutting-edge techniques with practical, code-first Python examples, navigating the unique challenges of financial data to develop and deploy robust trading strategies. This book covers the end-to-end workflow, from raw data processing to strategy execution, emphasizing a practical, hands-on approach.

What You'll Master:

• Data Structuring: Implement information-driven bars (Tick, Volume, Dollar, and Imbalance Bars).
• Labeling Strategies: Apply the Triple-Barrier Method and Meta-Labeling for robust forecasting.
• Feature Engineering: Utilize Fractional Differentiation (FFD) for stationarity while preserving memory.
• Non-IID Data Handling: Manage sample uniqueness with weights and the Sequential Bootstrap.
• Ensemble Methods: Leverage Bagging and Random Forests for improved model stability in finance.
• Robust Cross-Validation: Implement Purged K-Fold CV to prevent data leakage.
• Feature Importance: Demystify model drivers with MDI, MDA, and orthogonal features.
• Hyper-Parameter Tuning: Optimize models effectively using finance-specific scoring metrics.
• Bet Sizing: Translate model predictions into actionable position sizes.
• Rigorous Backtesting: Evaluate strategy performance with advanced metrics (Probabilistic Sharpe Ratio, Drawdown Analysis).
• Modern Asset Allocation: Explore ML-driven techniques like Hierarchical Risk Parity (HRP).

Who Is This Book For?

• Quantitative Analysts and Researchers seeking practical ML applications.
• Data Scientists transitioning into the financial domain.
• Python Developers building algorithmic trading systems.
• Portfolio Managers looking to incorporate ML into asset allocation.
• Finance Students & Academics wanting a hands-on guide to financial ML.
• Traders aiming to develop, test, and deploy data-driven strategies.