Program trading and high frequency trading (HFT) have become important to the financial industry that it generated over sixty percent of trading volume at Nasdaq and NYSE. There are wide range of activities that AI and big data can support in such trading process, which include opportunities identification, cost/friction estimation, market impact estimation, trading strategies selection, trade scheduling, capital and liquidity management, as well as risk management. In this course, we will discuss both traditional financial engineering models and modern AI, especially the machine learning and deep learning, that can be used in supporting Algo Trading. Modern topics like RoboAdvisor, AlphaGo Zero, and social media based market sentiment analysis will also be discussed.

CILO-1: Apply the process and key steps of algorithmic trading and high frequency trading. CILO-2: Apply machine learning and deep learning algorithms to support different steps in the algo trading process. CILO-3: Use big data and AI to process non-structured data, like news articles and contents of social media, to generate market sentiment. CILO-4: Build algo trading or even high frequency trading system/platform, such as, latency, information aggregation, scalability, system reliability, and decision making. CILO-5: Build a trading system with selected trading strategies.