10 Building the quantitative engine: market direction prediction with ML
This chapter covers
- Building ETF-based data pipelines for market prediction
- Engineering cross-asset features from financial time series
- Implementing time-series validation with embargo periods
- Training ML models for directional market forecasting
- Evaluating trading-focused 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 ML models to the strategic 'model-to-money' mindset—converge into a tangible system designed to generate 'alpha', or market-beating returns. We will construct a hybrid engine that mirrors the cutting-edge 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.