Skills you'll learn:
Automated plan optimizationBacktesting
AI Trading Strategies
Nanodegree Program
Start mastering AI-powered trading with this Nanodegree. Learn to build, backtest, and optimize sophisticated AI-driven trading models, gaining practical skills to succeed in dynamic financial markets.
Start mastering AI-powered trading with this Nanodegree. Learn to build, backtest, and optimize sophisticated AI-driven trading models, gaining practical skills to succeed in dynamic financial markets.
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Advanced96 hoursLast Updated August 15, 2025
Prerequisites:
Advanced
96 hours
Last Updated August 15, 2025
Skills you'll learn:
Automated plan optimization • Backtesting • Feature engineering • Financial analysis with AI
Prerequisites:
Algorithmic trading • Basic calculus • Basic SQL
Skills that demand high salaries
Quantitative Analyst
Demand for financial quantitative analysts is expected to increase, with an expected 54,350 new jobs filled by 2029.*
Salary Ranges
- Low
- $100,000
- Average
- $127,695
- High
- $187,457
Courses In This Program
Course 1 • 45 minutes
Welcome to the Nanodegree Program!
Welcome to Udacity! We're excited to share more about your Nanodegree program and start this journey with you!
Lesson 1
Welcome!
Welcome to Udacity. Takes 5 minutes to get familiar with Udacity courses and gain some tips to succeed in courses.
Lesson 2
Getting Help
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
Course 2 • 13 hours
Building a Workflow for AI
Refine your skills in AI-based trading by mastering key machine learning techniques such as reinforcement learning, supervised and unsupervised learning. Develop and backtest trading models using real financial data.
Lesson 1
Introduction to AI Workflows in Trading
Learn how to prepare price data for AI models, backtest trading algorithms, and build a simple RSI algorithm.
Lesson 2
Unsupervised Learning
Explore investment data, summarize key stats, use K-Means and PCA for clustering, adapt trading algorithms, and identify risk factors to enhance model insights on outperformance
Lesson 3
Supervised Learning: Regression
Build regression models using past returns, explore regularization to avoid over/underfitting, and differentiate between training and test data while identifying signs of overfitting and underfitting.
Lesson 4
Supervised Learning: Classification
Predict categorical variables using logistic regression and decision trees. Improve model performance with cross-validation for strong out-of-sample results.
Lesson 5
Reinforcement Learning
Explore reinforcement learning (RL) and its components, Q-learning, the DQN algorithm, and how to build and backtest an RL model.
Course 3 • 17 hours
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.
Lesson 1
An Overview of Machine Learning Pipelines
We'll define the steps of the machine learning pipeline, from data ingestion to production. We'll emphasize preprocessing and feature engineering, essential steps to well-performing trading models.
Lesson 2
Data Acquisition and Preprocessing
How do you get data from the Internet to your model? We'll talk about ingestion, transformation,and data wrangling using Pandas, the industry-standard time-series package for data manipulation.
Lesson 3
Feature Engineering for Trading Models
Feature engineering significantly improves model performance, and in this lesson, we'll go over strategies you can use. Basic and more involved techniques will be presented, with use cases noted.
Lesson 4
Exploratory Data Analysis
What's your data trying to tell you? Use EDA, exploratory data analysis, to find out! We'll use Python's two most popular plotting packages, matplotlib and Plotly, to find out your data's secrets.
Lesson 5 • Project
Project: Data Transformation for Trading Models
Learners will use historical stock prices for two large companies to practice data manipulation and exploratory data analysis, or EDA.
Course 4 • 13 hours
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.
Lesson 1
Measuring Returns
Understand the foundations for backtesting. Learners will examine formulas and develop tools for calculating and plotting returns.
Lesson 2
Measuring Risks
Understand volatility, skewness, kurtosis, and the impacts that these concepts have on developing a trading strategy.
Lesson 3
Measuring Risk-Adjusted Returns
Explore drawdowns, how to calculate them, and which ratios should be employed when developing a backtest strategy. Calculations include the Sharpe, Sortino, and Calmar Ratio.
Lesson 4
Backtesting a Risk Parity Portfolio
Through Python, learn how to implement Walk-Forward Validation and combine core calculations to develop a robust backtesting strategy.
Lesson 5 • Project
Project: Evaluating and Backtesting a Dynamic Investment Strategy
Assess and manage investment risk through key calculations such as Volatility, Sharpe Ratio, Sortino Ratio, Calmar Ratio. Learners develop and backtesting a strategy using Walk-Forward Validation.
Course 5 • 23 hours
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.
Lesson 1
Reinforcement Learning in Trading
Introduction to reinforcement learning, Q-learning, and core concepts including how reinforcement learning fits in the trading world.
Lesson 2
Representing the Financal Market: State and Action Spaces
Explore the concept of Financial State and Action Spaces. Learn how to define states and extract popular market indicators and conditions with Python and YFinance.
Lesson 3
Constructing a Reinforcement Trading Model
Construct a RL trading model using Python including define and running a training loop. Learn key tips for implementation and run test data through the newly created model.
Lesson 4
Backtesting and Optimization Techniques
Examine key backtesting concepts, gather important backtesting information on an RL model, and learn how to interpret those results to optimize performance.
Lesson 5 • Project
Project: Building a Reinforcement Learning Trading Model
The Project for this course will involve the students building and training RL Q-learning agent from scratch in a jupyter notebook.
Course 6 • 17 hours
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.
Lesson 1
Introduction to AI Model Optimization
We review how AI models work in principle and important terminology used in AI model training and optimization. We talk about where AI model optimization applies in using AI models for trading.
Lesson 2
Regularization Techniques to Prevent Overfitting
Overfitting is a common issue when training AI models for trading. We’ll explore bias, variance, and the role of hyperparameters in the context of various AI model types.
Lesson 3
Hyperparameter Tuning Methods
Get hands-on with AI model hyperparameters and discuss the various methods available to us for tuning them in a systematic or ad-hoc way, as well as the advantages and disadvantages of each method.
Lesson 4
Evaluating and Optimizing AI Strategies
We discuss some practical methods and important considerations related to model optimization and evaluation in the context of AI models for trading.
Lesson 5
Deployment and Real-World Considerations
We analyze important practical considerations for using AI models for trading. We discuss things we need to keep in mind as we maintain or iterate on our deployed models.
Lesson 6 • Project
Project: Building and Optimizing a Classification Model for Trading
Optimize a stock price prediction model using data preprocessing, hyperparameter tuning, over/underfitting detection, model evaluation, and feature selection.
Course 7 • 14 hours
Momentum-Based Trading
In this course, learners will explore how to design, backtest, and optimize a working momentum-based ML trading strategy.
Lesson 1
What is Momentum-Based Trading
Normal distribution and geometric Brownian motion are key to momentum trading. Shapiro-Wilk’s and Student’s t-tests are useful statistical tools for developing momentum-based trading strategies.
Lesson 2
Identifying and Extracting Momentum Features
Explore geometric Brownian motion for stock price modeling, calibration, forecasting, and confidence intervals, followed by deriving and coding the Black-Scholes formula for European option pricing.
Lesson 3
Constructing a Momentum Trading Model
Building a momentum-based trading program using MySQL/SQLite, Python, and geometric Brownian motion for price forecasting, confidence intervals, and Monte-Carlo simulation for scenario analysis.
Lesson 4
Backtesting and Optimization Techniques
The lesson covers back-testing momentum strategies, evaluating with Sharpe ratio and maximum drawdown, and quantitative risk management using Value-at-Risk (VaR) and Expected Shortfall (ES) in Python.
Lesson 5 • Project
Project: Build a Momentum-Based Algorithmic Trading Program
Build a momentum-based strategy to trade the S&P 500 index. You can later expand and customize you project to suit your needs. You will use the Python packages numpy, scipy and sqlite3, among others.
Course 8 • 10 minutes
Congratulations!
Congratulations on finishing your program!
Lesson 1
Congratulations!
Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.
Taught By The Best
Metin Akyol
Quantitative Analyst
Metin is a Data Scientist and Quantitative Analyst with nearly 10 years of experience in the investment industry, specializing in data-driven investment strategies and AI decision-making. He holds a PhD in Economics from Darmstadt University of Technology and has taught Machine Learning for 5 years.
Lara Kattan
Data Scientist & Clinical Assistant Professor at University of Chicago’s Booth School of Business
Lara Kattan is a data scientist and statistician. She's a Clinical Assistant Professor at the University of Chicago's Booth School of Business, where she teaches Python, R, and SQL. She creates curriculum for and teaches courses in machine learning, finance, and programming.
Alexandre Landi
Expert Data Scientist and Lecturer in Quantitative Finance at Skema Business School
Alex has worked as an Expert Data Scientist at IBM, has led teams of data engineers and data scientists at Airbus, and has provided business intelligence training for Viva Aerobus in Mexico. He also teaches programming and quantitative finance at Skema Business School.
Xiaolei Xie
Senior Quant Modeler at London Stock Exchange Group
Xiaolei Xie is a senior quant modeler at London Stock Exchange Group. He has worked on various areas of financial mathematics, statistics and algorithmic trading, such as stochastic differential equations, time series models, quantitative risk management, etc. He has years of experience in Python and C++ programming.
Lizzie Hnatiuk
Freelance AI Consultant
Lizzie Hnatiuk is an AI Engineer, with experience consulting for established small and mid-sized companies, working with startups in the AI sector, and working as a venture analyst. Her passion is AI, from development all the way through to business application. She has a B.Sc in AI and a Masters of Business Management.
Farid Taba
Data Engineer at Panalyt
Farid Taba is a machine learning engineer and a former quantitative analyst with 8 years of experience in the Technology and Financial sectors. He has a bachelor's degree in Computer Engineering from the University of Waterloo and a master's degree in Mathematical Finance from the University of Toronto.
Metin Akyol
Quantitative Analyst
Metin is a Data Scientist and Quantitative Analyst with nearly 10 years of experience in the investment industry, specializing in data-driven investment strategies and AI decision-making. He holds a PhD in Economics from Darmstadt University of Technology and has taught Machine Learning for 5 years.
Lara Kattan
Data Scientist & Clinical Assistant Professor at University of Chicago’s Booth School of Business
Lara Kattan is a data scientist and statistician. She's a Clinical Assistant Professor at the University of Chicago's Booth School of Business, where she teaches Python, R, and SQL. She creates curriculum for and teaches courses in machine learning, finance, and programming.
Alexandre Landi
Expert Data Scientist and Lecturer in Quantitative Finance at Skema Business School
Alex has worked as an Expert Data Scientist at IBM, has led teams of data engineers and data scientists at Airbus, and has provided business intelligence training for Viva Aerobus in Mexico. He also teaches programming and quantitative finance at Skema Business School.
Xiaolei Xie
Senior Quant Modeler at London Stock Exchange Group
Xiaolei Xie is a senior quant modeler at London Stock Exchange Group. He has worked on various areas of financial mathematics, statistics and algorithmic trading, such as stochastic differential equations, time series models, quantitative risk management, etc. He has years of experience in Python and C++ programming.
Lizzie Hnatiuk
Freelance AI Consultant
Lizzie Hnatiuk is an AI Engineer, with experience consulting for established small and mid-sized companies, working with startups in the AI sector, and working as a venture analyst. Her passion is AI, from development all the way through to business application. She has a B.Sc in AI and a Masters of Business Management.
Farid Taba
Data Engineer at Panalyt
Farid Taba is a machine learning engineer and a former quantitative analyst with 8 years of experience in the Technology and Financial sectors. He has a bachelor's degree in Computer Engineering from the University of Waterloo and a master's degree in Mathematical Finance from the University of Toronto.
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About AI Trading Strategies
The AI Trading Strategies Nanodegree equips learners with the skills to build and optimize AI-based trading models. The program covers key areas like ideation, data preprocessing, model development, backtesting, and optimization. Graduates will differentiate AI trading models, select the right model for specific applications, ingest and prepare data, and backtest models using industry best practices. Additionally, learners will master model optimization and detect model drift to ensure ongoing performance. This hands-on program provides the essential knowledge to create effective AI-driven strategies for capital markets.
Subscription · Monthly
- Cancel Anytime
- Unlimited access to hundreds of top-rated courses
- Hands-on projects with expert feedback
- Personalized career coaching and interview prep
- Program Certificates