Quantitative research and trading are at the core of modern finance. From hedge funds to algorithmic trading desks, professionals rely on data, models, and automation to make informed investment decisions. With the rise of artificial intelligence, this process has become faster, smarter, and more scalable. In this course, AI Basics for Quant Research and Trading Automation, you will learn how to combine Python, quantitative finance concepts, and AI tools like ChatGPT to build a complete research workflow.
We begin with a strong foundation by understanding the quant research pipeline—how raw data is transformed into trading decisions through features, models, backtesting, portfolio construction, and execution. You will then learn how to build data pipelines, import financial datasets, and prepare them for analysis.
The course moves into data cleaning and feature engineering, where you will create financial features such as returns, volatility, and moving averages. You will also explore sentiment analysis using AI, where ChatGPT helps convert textual data into actionable signals. Next, you will build vectorized backtesting systems to evaluate trading strategies and compare them with benchmarks. You will learn how to calculate key performance metrics like CAGR, volatility, Sharpe ratio, and maximum drawdown, and understand what they mean in real-world trading.
Finally, the course integrates ChatGPT directly into the workflow, allowing you to automate analysis, generate explanations, and improve strategies like a professional quant analyst. By the end of this course, you will understand how modern quant systems are designed, automated, and enhanced using AI.
Enroll now and start building AI-powered quant research and trading systems.
