Trading Systems

Financial markets generate petabytes of data daily: price ticks, order book updates, news feeds, earnings reports, social media sentiment, and macroeconomic indicators. Traditional quantitative finance relies on human-designed models—moving averages, mean reversion strategies, factor models—that capture known patterns but struggle to adapt to regime changes and novel market dynamics. Modern AI agents combine machine learning with systematic trading infrastructure to process multimodal signals, estimate future price movements, and execute trades at scale. ...

In high-frequency trading (HFT), microseconds determine profitability. When an arbitrage opportunity appears—say, a 0.01% price discrepancy between two exchanges—it vanishes within 100-500 microseconds as competing algorithms exploit it. The firm that detects and acts fastest captures the profit; everyone else loses. At this timescale, traditional software engineering practices (dynamic allocation, garbage collection, high-level abstractions) become liabilities. Systems must operate at the edge of hardware capability: cache-line optimization, lock-free algorithms, kernel bypass networking. ...