Finance Trading Algorithms in High-Frequency Markets: Predictive Modeling, Reinforcement Learning, and Real Time Anomaly Detection

Abstract

High-frequency trading (HFT) requires algorithms capable of finding short-lived alpha, trading with less than microsecond-latency, and being robust against regime changes and market microstructure misbehaviors. This paper introduces a unified decision stack that integrates (i) direction and intensity of return predictive modeling by short-horizon predictive modeling, (ii) inventory-sensitive quoting and execution by reinforcement learning (RL), and (iii) stress-time anomaly signal by real-time anomaly detection to ensure that directions and policies are not gated or down-risked. We elaborate on data engineering of full depth limit order books, leakage-safe labeling and latency-sensitive model calibration. A roll-walk forward protocol is used to compare gradient-boosted and sequence models to make predictions, CVaR-constrained RL policies to baseline execution strategies and streaming detectors (autoencoders, isolation-based methods and extreme-value tails) to outlier control. Performance is measured in terms of risk-adjusted profitability (PnL, Sharpe, CVaR), quality of execution (fill ratio, slippage, cancel-to-trade) and end-to-end 99 th -percentile latency. Empirical evidence shows that calibrated predictors and safe-RL can be used to reduce risk-adjusted returns, whereas the anomaly gate can be used to reduce drawdowns and tail exposure during volatility spikes without significantly impacting latency budgets. We end with deployment advice such as blue-green rollouts, monitoring and governance to make the stack operational in production HFT settings.