Interaction strategies for reward in competitive environments are significantly influenced by the nature and extent of available information. In financial markets, particularly foreign exchange (forex), traders operate independently with limited information, often yielding highly unpredictable outcomes. This study introduces a game-theoretic framework modeling the market as a strategically active participant, rather than a neutral entity, within a stochastic, imperfect information setting. In this model, the market alternates sequentially with new traders, each trader having limited visibility of the market's moves, while the market observes and counteracts each trader strategy. Through a series of simulations, we show that this information asymmetry enables the market to consistently outperform traders on aggregate. This outcome suggests that real-world forex environments may inherently favor market structures with greater informational advantage, challenging the perception of a level playing field. The model provides a basis for simulating skewed information environments, highlighting how strategic imbalances contribute to trader losses. Further optimization of the intelligent market scoring and refined simulations of trader-market interactions can enhance predictive analytics for forex, offering a robust tool for market behavior analysis.

翻译：竞争环境中为获取回报的交互策略在很大程度上受到可用信息的性质与范围的显著影响。在金融市场，尤其是外汇市场中，交易者在信息有限的情况下独立操作，往往产生高度不可预测的结果。本研究引入了一个博弈论框架，将市场建模为随机、不完全信息环境中的一个具有策略主动性的参与者，而非中性实体。在该模型中，市场与新交易者依次交替行动，每位交易者对市场行为的可见性有限，而市场则观察并反制每位交易者的策略。通过一系列仿真实验，我们证明这种信息不对称使得市场在总体上能够持续优于交易者。这一结果表明，现实世界的外汇环境可能本质上更有利于具有更大信息优势的市场结构，从而对公平竞争环境的认知提出了挑战。该模型为模拟倾斜信息环境提供了基础，揭示了策略性失衡如何导致交易者亏损。进一步优化智能市场评分机制及精细化模拟交易者与市场间的互动，可增强外汇市场的预测分析能力，为市场行为分析提供一种强有力的工具。