Machine Learning for Market Prediction

Learn to eliminate silent backtest inflation with confidence using measured, verified engineering, guided by a pragmatic quant researcher's playbook for point in time data, leakage-free features, and honest validation.

Key Features

• Master core reliability disciplines used across real reviewed backtests, from data sourcing to walk-forward validation
• Learn to eliminate silent leakage, including lookahead bias, survivorship bias, label leakage, and search-driven overfitting
• Build provable validity without assuming correctness, using deflated Sharpe ratios, permutation testing, and SHAP-based audits

Book Description

Most traders ship a backtest once it produces a strong Sharpe ratio, unaware the pipeline never checked whether that number is real. This book closes that gap, providing the practical knowledge of point in time data sourcing, leakage-free feature engineering, and honest validation needed to build a model with measured correctness, not assumed correctness.

You will begin by sourcing and cleaning market data at the standard real backtests require, joining fundamentals on release date rather than report date, and reconstructing point in time universe membership that includes securities later delisted. You'll gain a clear framework for engineering out silent leakage at its source, understanding where centered rolling windows, full sample normalization, and untested labeling introduce risk, and how each cost compounds, or gets eliminated, under a properly embargoed pipeline.

The book walks through the full arc of a production research process: feature construction from price, volume, and sentiment data that never touches the future, triple barrier labeling scaled to genuine volatility, walk-forward validation with an embargo sized to the labeling horizon, and a full backtest rebuilt with realistic execution and cost. It addresses evaluation rigor, including deflated Sharpe ratios and regime-conditional stress testing, and closes with a full case study from raw data to a validated model.

Whether you're building your first trading model or auditing your fourth, this book provides actionable techniques for securing correctness that holds under live capital.

What You Will Learn

• Define lookahead bias, survivorship bias, and overfitting precisely and distinguish them from ordinary noise
• Build real point in time data pipelines without relying on assumed-safe vendor joins
• Deconstruct backtest performance into signal, cost, and search-inflated components
• Evaluate fixed horizon and triple barrier labeling before committing to a target
• Verify correctness against deflated Sharpe ratios and permutation testing before trusting a result
• Recognize and eliminate silent leakage sources without guessing at root cause
• Know when a model's edge is proven versus merely untested
• Design validation for a noisy, regime-shifting live market environment

Who This Book Is For

This book is for Python developers, quantitative researchers, and self-taught traders building or hardening a production trading model. It's useful for engineers moving into research-driven roles where validation guarantees grow more complex. No prior quantitative finance experience is assumed.

Table of Contents

1. Why Most Trading Models Fail Before Deployment
2. Sourcing and Cleaning Market Data
3. Feature Engineering from Price Data
4. Feature Engineering from Alternative Data
5. Labeling the Prediction Target
6. Choosing and Training the Model
7. Backtesting Without Fooling Yourself
8. Evaluating Model Performance Honestly
9. Feature Importance and Model Interpretability
10. Case Study: From Raw Data to a Validated Model; Appendices