The goal of the AI Trading Strategies Nanodegree program is to provide learners with the knowledge to build an AI based trading model. This includes ideation, preprocessing, model development, backtesting, and optimization.
Overview
Syllabus
- Welcome to the Nanodegree Program!
- Welcome to Udacity! We're excited to share more about your Nanodegree program and start this journey with you!
- Building a Workflow for AI
- This course introduces learners to the concepts of AI-based trading models. Learners will discover the basic building blocks used to create an AI-based trading model and key Machine Learning techniques they need to be aware of.
- Preparing for Data Analysis
- Preprocessing is a critical concept in any successful ML model. In this course, you will learn the basics of data engineering, data selection, and exploratory data analysis.
- Evaluating Returns and Backtesting
- This course will advance learners' abilities to construct and backtest strategies. The curriculum emphasizes a deep understanding of key performance metrics—such as annualized returns, volatility, and various risk-adjusted ratios—to critically evaluate the effectiveness of trading strategies. Additionally, learners will enhance their skills in visualizing strategy performance through advanced graphical representations. By mastering the implementation and rigorous evaluation of trading models, students will be well-equipped to optimize strategies and ensure robust performance in the world of capital markets.
- Reinforcement Learning
- In this course, learners will explore how to design, backtest, and optimize a working reinforcement-based ML trading strategy. This course will introduce popular techniques and indicators used in reinforcement learning-based trading, such as Q-learning, PCA, use of market indicators, assessment of market context, and assessment of the strategy outcomes. This course is designed for hobby traders with a background in data science. By the end of this course, you will be able to build, train, backtest, and optimize a reinforcement learning trading strategy with Python.
- Optimizing AI Strategies
- This course covers various aspects of improving AI models. Topics include introduction to model optimization, hyperparameter tuning, regularization techniques, evaluating and optimizing strategies, and deployment considerations. Students will learn how to monitor, evaluate and enhance model performance, prevent overfitting, and apply techniques for real-world scenarios.
- Momentum-Based Trading
- In this course, learners will explore how to design, backtest, and optimize a working momentum-based ML trading strategy.
- Congratulations!
- Congratulations on finishing your program!
- Career Services
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Taught by
Metin Akyol , Lara Kattan, Alexandre Landi, Xiaolei Xie, Lizzie Hnatiuk and Farid Taba
