Zero-shot/Few-shot Investment Strategy Synthesis and Automated Backtesting using Generative AI: Building a Closed-Loop Pipeline for Untapped Alpha in Financial Markets

Finding alpha (excess returns) in financial markets is becoming increasingly challenging. This article presents a method for building a fully automated, closed-loop pipeline that leverages generative AI to automatically create investment strategies, which are then backtested for evaluation and improvement. This opens a new era of systematic alpha discovery, transcending human limitations and biases.

1. The Challenge / Context

For decades, quantitative traders have strived to capture market inefficiencies by analyzing vast amounts of data and building complex mathematical models. However, the explosive growth of data and increasing market efficiency have made it difficult to find new alpha through human intuition and manual strategy development alone. Discovering meaningful patterns amidst countless financial products, market indicators, and alternative data (news, social media, satellite imagery, etc.), converting them into profitable strategies, and validating numerous hypotheses consumes immense time and resources. Furthermore, inherent human cognitive biases can hinder objective strategy development. This is precisely where generative AI becomes an innovative tool for exploring uncharted territories in financial markets. Its ability to understand complex market conditions, integrate diverse data sources, propose novel strategy ideas that humans might not conceive, implement them in code, and immediately backtest them holds the potential to change the paradigm of quantitative trading.

2. Deep Dive: Principles of Generative AI and Financial Strategy Synthesis

Generative AI