Synthetic Market Data Generation Using GANs: Overcoming Data Scarcity for Stress Testing Trading Algorithms in Extreme Market Conditions

Financial markets occasionally experience extreme conditions that can severely impact trading algorithms, yet historical data capturing these rare events is inherently scarce. This research addresses the critical challenge of data scarcity for stress testing trading algorithms by developing a novel Generative Adversarial Network (GAN) framework specifically designed to synthesize realistic financial market data representing extreme conditions. The proposed Conditional Market-GAN architecture incorporates temporal dependencies, multidimensional asset relationships, and regime-switching capabilities to generate high-fidelity synthetic data that exhibits the statistical properties and anomalous behaviors of actual market crises. Experimental results demonstrate that trading algorithms tested against our synthetic extreme scenarios identified vulnerabilities not detected in conventional backtesting. Performance evaluations show that our approach outperforms traditional simulation methods in preserving complex market dynamics while generating diverse stress scenarios. This research contributes a practical solution for financial institutions to strengthen algorithmic trading systems against rare but catastrophic market events, potentially reducing systemic risk in automated trading environments.