Financial markets respond not only to fundamentals, but also to investor emotions, expectations, news, narratives, and market behavior. In Financial Sentiment Analysis and AI Trading, you’ll learn how to analyze financial sentiment and evaluate its relevance across equities, sectors, global markets, credit, currencies, commodities, and high-frequency trading.

You’ll progress from foundational concepts such as sentiment levels, direction, momentum, source credibility, and contrarian signals to multi-asset sentiment intelligence, alternative credit data, cloud-based processing, commodity and oil-market sentiment, and multimodal financial signals. You’ll also explore how sentiment shocks influence order books, liquidity, bid–ask spreads, order flow, and price discovery. The course then connects sentiment with high-frequency trading through synchronized market data, feature engineering, machine learning, event-driven strategies, arbitrage, latency, slippage, transaction costs, execution risk, and model drift. By the end, you’ll be able to evaluate financial sentiment signals alongside market data, identify potential risks and opportunities, and assess whether sentiment-driven strategies remain reliable and executable under real-world trading constraints.