Modernizing futures and options trading platforms for real-time margin and intraday risk computation

Modernizing futures and options trading platforms has become a strategic and technical priority because real-time margin and intraday risk computation must now operate under higher message rates, larger cross-product portfolios, and more demanding intraday control requirements. Traditional batch-based risk engines were designed for periodic recalculation, but current market conditions require exposures, sensitivities, and collateral requirements to be updated continuously in response to rapidly changing volatility and liquidity. Recent literature provides valuable building blocks for this transition, particularly in fast option pricing, surrogate modeling, deep-learning-based volatility estimation, reinforcement-learning-based hedging, and high-dimensional numerical methods. At the same time, the peer-reviewed evidence base remains fragmented, with far more attention to computational kernels than to full production-platform redesign for intraday margining. This literature review appraises recent journal articles in the context of platform modernization, such as computational latency, model fidelity, explainability and operational control. Major themes include event-based risk architecture, staged recomputation, rapid pricing of Greeks and exposure aggregation, and governance requirements for margin decisions that must remain defensible under stressed market conditions. The review identifies a persistent gap between advances in algorithms and deployable intraday margin systems, and argues that future progress will require integrated work across quantitative finance, distributed-systems engineering, and model-risk governance.