10 Building the quantitative engine: Market direction prediction with machine learning
This chapter covers
- Building ETF data pipelines
- Engineering cross-asset features
- Validating time series with embargoes
- Training models for market direction
- Evaluating trading performance metrics
In the preceding chapters, we forged shields. We architected sophisticated AI systems to defend financial institutions against the threats of credit defaults and fraud. We learned to protect capital. Now, we shift our perspective from defense to offense. Our goal is no longer just to preserve money, but to actively grow it in the world’s most competitive arena: the financial markets.
This chapter marks the beginning of our most ambitious project yet: building an end-to-end, AI-powered investment system. This is where all the concepts we’ve learned—from data pipelines and machine learning (ML) models to the strategic “model-to-money” mindset—converge into a tangible system designed to generate alpha, or market-beating returns. We’ll construct a hybrid engine that mirrors the latest approaches of modern quant funds, blending the cold logic of numbers with the nuanced understanding of human language.
Our journey begins by constructing its foundational pillar: the quantitative engine.