Bayesian Robust Financial Trading with Adversarial Synthetic Market Data

Paper: arXiv 2601.17008

Authors: Haochong Xia, Simin Li, Ruixiao Xu, Zhixia Zhang, Hongxiang Wang, Zhiqian Liu, Teng Yao Long, Molei Qin, Chuqiao Zong, Bo An

Abstract

Algorithmic trading relies on machine learning models to make trading decisions. Despite strong in-sample performance, these models often degrade when confronted with evolving real-world market regimes, which can shift dramatically due to macroeconomic changes-e.g., monetary policy updates or unanticipated fluctuations in participant behavior. We identify two challenges that perpetuate this mismatch: (1) insufficient robustness in existing policy against uncertainties in high-level market fluctuations, and (2) the absence of a realistic and diverse simulation environment for training, leading to policy overfitting. To address these issues, we propose a Bayesian Robust Framework that systematically integrates a macro-conditioned generative model with robust policy learning. On the data side, to generate realistic and diverse data, we propose a macro-conditioned GAN-based generator that leverages macroeconomic indicators as primary control variables, synthesizing data with faithful temporal, cross-instrument, and macro correlations. On the policy side, to learn robust policy against market fluctuations, we cast the trading process as a two-player zero-sum Bayesian Markov game, wherein an adversarial agent simulates shifting regimes by perturbing macroeconomic indicators in the macro-conditioned generator, while the trading agent-guided by a quantile belief network-maintains and updates its belief over hidden market states. The trading agent seeks a Robust Perfect Bayesian Equilibrium via Bayesian neural fictitious self-play, stabilizing learning under adversarial market perturbations. Extensive experiments on 9 financial instruments demonstrate that our framework outperforms 9 state-of-the-art baselines. In extreme events like the COVID, our method shows improved profitability and risk management, offering a reliable solution for trading under uncertain and shifting market dynamics.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an advanced theoretical framework using Bayesian Markov games and GAN-based simulation, paired with rigorous empirical evaluation across 9 financial instruments and out-of-sample stress scenarios like COVID-19. The availability of open-source code further reinforces its technical completeness and reproducibility.

Research Flowchart

  flowchart TD
    A[Research Goal: Robust Trading Policy Under Shifting Market Regimes] --> B[Data Input: Historical Market Data & Macroeconomic Indicators]
    
    subgraph Framework [Bayesian Robust Framework]
        B --> C[Macro-Conditioned GAN]
        C -- Generates Synthetic Market Data --> D[Two-Player Zero-Sum Bayesian Game]
        
        D --> E1[Adversarial Agent: Perturbs Macro Controls]
        D --> E2[Trading Agent: Updates Beliefs via Quantile Belief Network]
        
        E1 & E2 --> F[Bayesian Neural Fictitious Self-Play]
    end
    
    F --> G[Outcome: Robust Perfect Bayesian Equilibrium & Superior Multi-Asset Performance]

Cited References (161)

1. Simulation Techniques in Financial Risk Management (2015)
2. Simulation in financial engineering (2002)
3. Agent-based models of financial markets (2007)
4. Limit Order Books (2013) — link
5. Time-series generative adversarial networks (2019)
6. Conditional sig-wasserstein gans for time series generation (2020) — link
7. Robust control of Markov decision processes with uncertain transition matrices (2005)
8. Robust multi-agent Q-learning in cooperative games with adversaries (2021)

2 of 161 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.