Financial Engineering and Artificial Intelli…

Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?

Today, you can stop imagining, and start doing.

This course will teach you the core fundamentals of financial engineering, with a machine learning twist.

We will cover must-know topics in financial engineering, such as:

Exploratory data analysis, significance testing, correlations, alpha and beta
Time series analysis, simple moving average, exponentially-weighted moving average
Holt-Winters exponential smoothing model
ARIMA and SARIMA
Efficient Market Hypothesis
Random Walk Hypothesis
Time series forecasting ("stock price prediction")
Modern portfolio theory
Efficient frontier / Markowitz bullet
Mean-variance optimization
Maximizing the Sharpe ratio
Convex optimization with Linear Programming and Quadratic Programming
Capital Asset Pricing Model (CAPM)
Algorithmic trading (VIP only)
Statistical Factor Models (VIP only)
Regime Detection with Hidden Markov Models (VIP only)

In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

Regression models
Classification models
Unsupervised learning
Reinforcement learning and Q-learning

VIP-only sections (get it while it lasts. )

Algorithmic trading (trend-following, machine learning, and Q-learning-based strategies)
Statistical factor models
Regime detection and modeling volatility clustering with HMMs

We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.

As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn't help but wander into the vast and complex world of financial engineering.

This course is for anyone who loves finance or artificial intelligence, and especially if you love both.

Whether you are a student, a professional, or someone who wants to advance their career - this course is for you.

Thanks for reading, I will see you in class.

Suggested Prerequisites:

Matrix arithmetic
Probability
Decent Python coding skills
Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses. )

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Forecasting stock prices and stock returns
Time series analysis
Holt-winters exponential smoothing model
Arima
Efficient market hypothesis
Random walk hypothesis
Exploratory data analysis
Alpha and beta

Distributions and correlations of stock returns
Modern portfolio theory
Mean-variance optimization
Efficient frontier, sharpe ratio, tangency portfolio
Capm (capital asset pricing model)
Q-learning for algorithmic trading
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Forecasting stock prices and stock returns
Time series analysis
Holt-winters exponential smoothing model
Arima
Efficient market hypothesis
Random walk hypothesis
Exploratory data analysis
Alpha and beta
Distributions and correlations of stock returns
Modern portfolio theory
Mean-variance optimization
Efficient frontier, sharpe ratio, tangency portfolio
Capm (capital asset pricing model)
Q-learning for algorithmic trading
Show more
Show less

Syllabus

Welcome

Introduction and Outline

Scope of the course

How to Practice

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches financial engineering with a machine learning twist, targeting individuals with an interest in both disciplines

Covers advanced topics like algorithmic trading, statistical factor models, and regime detection

Provides a solid foundation in financial engineering, with topics ranging from data analysis to portfolio optimization

Emphasizes the practical applications of machine learning in finance, including stock price prediction, risk management, and algorithmic trading

Instructors have a proven track record in machine learning and financial engineering, ensuring high-quality content

Requires prior knowledge in matrix arithmetic, probability, and Python coding, which may limit accessibility for beginners

Reviews summary

Rigorous financial ai with python

According to learners, this course offers a deep dive into financial engineering and AI with Python, providing a strong mathematical foundation alongside practical coding implementations. Many students highlight the instructor's unparalleled expertise and the course's unique approach to debunking common AI myths in finance, particularly regarding stock price prediction. While the lectures are thorough and the code examples are clear, prospective students should be aware of the demanding prerequisites and fast pace, as it often requires a stronger background in mathematics and statistics than initially suggested.

Covers broad topics; can be dense, requiring supplemental study.

"The content is dense and comprehensive, which is good, but I found the explanations could sometimes be clearer for those without a very strong math background."

"The course has good information, but it feels like it tries to cover too much, leading to some sections being less in-depth than I hoped."

"You definitely need to supplement with other resources if you want to master these areas. The math is also very heavy."

Provides critical insights by challenging common AI misconceptions.

"The debunking of LSTM stock prediction was particularly insightful and helped ground my understanding in reality. This isn't a 'get rich quick' scheme; it's proper education."

"The clarity in explaining why certain AI methods are misused in finance was refreshing. It’s an advanced course... worth it."

"The instructor's unique perspective on market inefficiencies and debunking 'fake AI' is a major plus."

Effectively integrates theory with clear, hands-on coding examples.

"The code examples are clear, and I appreciate the emphasis on understanding the math behind it all. Highly recommended for professionals!"

"Excellent course! The lectures are thorough, and the hands-on coding in Python for financial concepts is invaluable."

"The 'every line of code explained' feature is true and incredibly helpful. This is one of the best courses for applied financial ML."

Emphasizes deep theoretical and mathematical understanding.

"The instructor's depth of knowledge is unparalleled, and the way he breaks down complex topics... is superb. I appreciate the emphasis on understanding the math behind it all."

"This course cuts through the hype and provides a rigorous, practical, and mathematically sound approach to financial engineering with AI."

"Great course for those who want to understand the *why* behind the financial models, not just the *how*. The emphasis on mathematical rigor is a distinguishing feature."

Requires a stronger quantitative background than stated.

"I struggled with this course... felt the mathematical prerequisites were severely understated. It assumes a much deeper understanding of linear algebra and statistics."

"My only minor critique is that sometimes the pace felt a bit fast if you're not already comfortable with some of the advanced mathematical concepts."

"The content is dense and comprehensive... I found the explanations could sometimes be clearer for those without a very strong math background."

"The pace is unforgiving for anyone without a quant background. Ended up dropping it halfway through. Not for beginners to quant finance."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Financial Engineering and Artificial Intelligence in Python with these activities:

Simple Moving Averages

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Reinforce the theoretical understanding of Simple Moving Averages by practicing and computing them for different time series datasets.

Browse courses on Time Series

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Select a time series dataset
Calculate the Simple Moving Average for different window sizes
Plot the time series and the corresponding Simple Moving Averages

Exponential Weighted Moving Average

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Strengthen the understanding of Exponential Weighted Moving Average by implementing it and analyzing its impact on time series datasets.

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Select a time series dataset
Calculate the Exponential Weighted Moving Average for different smoothing factors
Plot the time series and the corresponding Exponential Weighted Moving Averages

Holt-Winters Exponential Smoothing

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Gain hands-on experience in applying Holt-Winters Exponential Smoothing to forecast time series data, enhancing the ability to make predictions.

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Select a time series dataset with trend and seasonality
Perform Holt-Winters Exponential Smoothing with different parameters
Evaluate the accuracy of the forecasts using appropriate metrics

Four other activities

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Show all seven activities

ARIMA Modeling

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Develop proficiency in applying ARIMA models to time series data, enabling advanced forecasting and predictive analytics.

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Identify and understand the ARIMA model components
Build ARIMA models for different time series datasets
Evaluate the performance of ARIMA models using statistical metrics

Practice Statistical Modeling

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Reinforce obtained knowledge of statistical distribution modeling by completing a series of practice questions and exercises.

Browse courses on Statistical Modeling

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Solve 10 practice questions on normal distribution.
Solve 10 practice questions on lognormal distribution.
Solve 5 practice questions on student's t-distribution.
Solve 5 practice questions on chi-square distribution.

Stock Portfolio Optimization Project

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Deepen understanding of portfolio optimization by building a project that implements Modern Portfolio Theory and demonstrates the construction of efficient portfolios.

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Gather historical stock data
Calculate expected returns and covariance matrix
Implement mean-variance optimization to find efficient portfolios
Evaluate portfolio performance using risk and return metrics
Write a report summarizing the project and findings

Financial Engineering Whitepaper

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Sharpen analytical and communication skills by authoring a whitepaper that explores the applications of financial engineering and machine learning in the finance industry.

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Research and identify a specific topic within financial engineering
Develop a thesis statement and outline
Write the whitepaper, including introduction, literature review, methodology, results, and conclusion
Proofread and edit the whitepaper

Career center

Learners who complete Financial Engineering and Artificial Intelligence in Python will develop knowledge and skills that may be useful to these careers: