AI Trading Agents with Python: Backtesting Strategies

Algorithmic trading is no longer restricted to institutional quantitative analysts. With modern Python tools and AI frameworks, developers can build systems to analyze markets, generate signals, and execute strategies based on data. This course guides you through the process of creating a functional, backtested AI trading agent from scratch. You will start with foundational financial terminology and Python data structures, then progress to designing intelligent algorithms that evaluate historical market data to make informed decisions. What you'll learn: - Understand fundamental market terminology, trading mechanics, and quantitative analysis basics. - Process financial time-series data using modern dataframe libraries and Python type hints. - Apply AI concepts, including basic sentiment analysis and predictive signals, to market data. - Build custom algorithmic trading strategies based on technical indicators and AI outputs. - Practice rigorous backtesting to evaluate strategy performance, risk, and historical drawdowns. - Structure your trading agent using modern Python packaging and environment management. The curriculum begins with essential financial concepts before moving into data manipulation and algorithmic logic. You will work through structured written exercises to build, test, and refine your own trading agent logic step-by-step. Designed for beginner developers and programming enthusiasts with no prior finance background who want to explore quantitative trading. Start building your automated trading strategies today.