LLMs and GANs in Quantitative Finance

Reactive Publishing

Large Language Models and Generative Adversarial Networks are reshaping how quantitative researchers create, stress-test, and deploy trading strategies. LLMs and GANs in Quantitative Finance: Building Synthetic Data for Alpha shows practitioners how to combine these two generative technologies to produce high-quality synthetic market data that can improve model robustness, expand limited historical datasets, and support the search for genuine alpha.

The book moves from foundational concepts to practical implementation. You will learn how GANs can generate realistic price paths, order-flow sequences, and volatility surfaces, while LLMs assist in feature engineering, scenario generation, and the interpretation of complex market regimes. Clear explanations are paired with Python-based examples that demonstrate data preparation, model training, evaluation metrics, and integration into existing quantitative workflows.

Topics include:

• Designing and training GANs for financial time series
• Using LLMs to augment and condition synthetic data generation
• Evaluating synthetic data quality against real market statistics
• Applying synthetic datasets to backtesting, risk modeling, and strategy research
• Practical considerations for avoiding common pitfalls such as mode collapse, leakage, and unrealistic correlation structures

Written for quantitative analysts, portfolio managers, and researchers who already work with market data, this book focuses on methods that can be implemented and validated in production research environments. It assumes familiarity with Python, basic machine learning, and core quantitative finance concepts, then builds directly on that foundation.

Whether you are extending limited datasets, testing strategies under rare market conditions, or exploring new sources of predictive signal, the techniques presented here provide a rigorous path for incorporating modern generative models into the quantitative research process.