Part 4: Building AI for investment strategies
In the preceding parts, we forged shields—AI systems designed to protect capital against credit defaults and fraud. Now, we shift from defense to offense, building an end-to-end, AI-powered investment system designed to actively grow capital in the world’s most competitive arena: the financial markets.
Chapter 10 constructs the quantitative engine. You’ll build ETF-based data pipelines, engineer cross-asset features from financial time series, implement time-series validation with embargo periods, and train machine learning models for directional market forecasting—learning firsthand why even a modest predictive edge matters in efficient markets. Chapter 11 then builds the qualitative engine: a Retrieval-Augmented Generation (RAG) pipeline that uses Large Language Models to read, interpret, and quantify the flood of daily financial news, translating narratives, policy tone shifts, and emerging risks into structured, actionable signals. In chapter 12, these two engines converge. You’ll elevate the LLM from a signal extractor to a decision advisor that receives structured inputs, reasons within explicit constraints, and produces auditable portfolio allocation recommendations for human review—building not a trading system, but a repeatable decision framework that makes human judgment scalable.