Financial Engineering and Artificial Intelligence in Python

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!

4.83 (1790 reviews)
Udemy
platform
English
language
Data Science
category
8,776
students
21.5 hours
content
Mar 2024
last update
$74.99
regular price

What you will learn

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

Description

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

Content

Welcome

Introduction and Outline
Where to get the code
Scope of the course
How to Practice
Warmup (Optional)

Financial Basics

Financial Basics Section Introduction
Getting Financial Data
Getting Financial Data (Code)
Understanding Financial Data
Understanding Financial Data (Code)
Dealing with Missing Data
Dealing with Missing Data (Code)
Returns
Adjusted Close, Stock Splits, and Dividends
Adjusted Close (Code)
Back to Returns (Code)
QQ-Plots
QQ-Plots (Code)
The t-Distribution
The t-Distribution (Code)
Skewness and Kurtosis
Confidence Intervals
Confidence Intervals (Code)
Statistical Testing
Statistical Testing (Code)
Covariance and Correlation
Covariance and Correlation (Code)
Alpha and Beta
Alpha and Beta (Code)
Mixture of Gaussians
Mixture of Gaussians (Code)
Volatility Clustering
Price Simulation
Price Simulation (Code)
Financial Basics Section Summary

Time Series Analysis

Time Series Analysis Section Introduction
Efficient Market Hypothesis
Random Walk Hypothesis
The Naive Forecast
Simple Moving Average (Theory)
Simple Moving Average (Code)
Exponentially-Weighted Moving Average (Theory)
Exponentially-Weighted Moving Average (Code)
Simple Exponential Smoothing for Forecasting (Theory)
Simple Exponential Smoothing for Forecasting (Code)
Holt's Linear Trend Model (Theory)
Holt's Linear Trend Model (Code)
Holt-Winters (Theory)
Holt-Winters (Code)
Autoregressive Models - AR(p)
Moving Average Models - MA(q)
ARIMA
ARIMA in Code (pt 1)
Stationarity
Stationarity Code
ACF (Autocorrelation Function)
PACF (Partial Autocorrelation Funtion)
ACF and PACF in Code (pt 1)
ACF and PACF in Code (pt 2)
Auto ARIMA and SARIMAX
Model Selection, AIC and BIC
ARIMA in Code (pt 2)
ARIMA in Code (pt 3)
ACF and PACF for Stock Returns
Forecasting
Time Series Analysis Section Conclusion

Portfolio Optimization and CAPM

Portfolio Optimization Section Introduction
The S&P500
What is Risk?
Why Diversify?
Describing a Portfolio (pt 1)
Describing a Portfolio (pt 2)
Visualizing Random Portfolios (pt 1)
Visualizing Random Portfolios (pt 2)
Maximum and Minimum Portfolio Return
Maximum and Minimum Portfolio Return in Code
Mean-Variance Optimization
The Efficient Frontier
Mean-Variance Optimization And The Efficient Frontier in Code
Global Minimum Variance (GMV) Portfolio
Global Minimum Variance (GMV) Portfolio in Code
Sharpe Ratio
Maximum Sharpe Ratio in Code
Portfolio with a Risk-Free Asset and Tangency Portfolio
Risk-Free Asset and Tangency Portfolio in Code
Capital Asset Pricing Model (CAPM)
Problems with Markowitz Portfolio Theory and Robust Estimation
Portfolio Optimization Section Conclusion

VIP: Algorithmic Trading

Algorithmic Trading Section Introduction
Trend-Following Strategy
Trend-Following Strategy in Code (pt 1)
Trend-Following Strategy in Code (pt 2)
Machine Learning-Based Trading Strategy
Machine Learning-Based Trading Strategy in Code
Classification-Based Trading Strategy in Code
Using a Random Forest Classifier for Machine Learning-Based Trading
Algorithmic Trading Section Summary

VIP: The Basics of Reinforcement Learning

Reinforcement Learning Section Introduction
Elements of a Reinforcement Learning Problem
States, Actions, Rewards, Policies
Markov Decision Processes (MDPs)
The Return
Value Functions and the Bellman Equation
What does it mean to “learn”?
Solving the Bellman Equation with Reinforcement Learning (pt 1)
Solving the Bellman Equation with Reinforcement Learning (pt 2)
Epsilon-Greedy
Q-Learning
How to Learn Reinforcement Learning

VIP: Reinforcement Learning for Algorithmic Trading

Trend-Following Strategy with Reinforcement Learning API
Trend-Following Strategy Revisited (Code)
Q-Learning in an Algorithmic Trading Context
Representing States
Q-Learning for Algorithmic Trading in Code

Extras

Colab Notebooks

Setting Up Your Environment FAQ

Windows-Focused Environment Setup 2018
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners FAQ

How to Code by Yourself (part 1)
How to Code by Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it

Effective Learning Strategies for Machine Learning FAQ

How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Machine Learning and AI Prerequisite Roadmap (pt 1)
Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

What is the Appendix?
BONUS: Where to get discount coupons and FREE deep learning material

Screenshots

Financial Engineering and Artificial Intelligence in Python - Screenshot_01Financial Engineering and Artificial Intelligence in Python - Screenshot_02Financial Engineering and Artificial Intelligence in Python - Screenshot_03Financial Engineering and Artificial Intelligence in Python - Screenshot_04

Reviews

Shing
October 9, 2023
This is an amazing course. It covers the fundamental concepts in Financial Engineering, which opens a path for learning more advanced topics. The course also explains the mathematical concepts behind the algorithm well. Thank you for your teaching!
Maru
September 8, 2023
Excellent course and I would recommend it to anyone who would like to see how finance, statistics and AI work together.
Rohit
August 10, 2023
Very well made. You get the impression that he actually cares about the students he is teaching and wants them to succeed.
Fred
July 27, 2023
In 21 hours of instruction you'll gain 210 hours of knowledge. tl;dr: IMHO this course is a comprehensive high-level study of the three types of machine learning taught through concept and practice. The instructor is masterfully straightforward and logical. He builds on each lesson without waste and in a manner that keeps you engaged. If you have a question, it is answered in hours, not days. Note: you will not learn how to predict the future Again, in my opinion, if you want to get the most from this course, you should have a very good understanding of statistics and algebra and to a lessor degree the stock market. The stock market serves more as a medium for learning, than for a way to predict the future of stock prices. You should have some programming experience, although the instructor goes through the code line by line, being able to code will save you a ton of time and help you to understand how the math is applied. After finishing this course I was able to immediately apply what I learned to problems I face at work. It gave me tools to uncover hidden gems of information that is buried amount a massive amount of time series data we collect.
Rubel
July 16, 2023
This course is way much better then I expected. Before I buy this course I thought this might be my wrong decision, it is expensive, maybe I will save my money for party. But after I purchase this course I fall in love with finance.
Jagat
July 10, 2023
Very good lectures for a beginner if you just follow the lectures its a sure road to becoming proficient in finance and AI, good practices to sharpen your newly learned skills.
Daniel
June 5, 2023
Overall great course - the instructor's knowledge is superb, and you can tell he really knows his stuff. I would have liked to have gotten more out of the course overall. While the breadth and detail of each subject were phenomenal, I feel I am still missing how they tie together in an effort to make a trading bot or something of the like. I'm not looking to "get rich quick" but more to understand what it takes to make something of that scale and how it can apply to the average person to optimize better than elsewhere. In fairness to the above - I am definitely NOT in the math-heavy camp and will be going through this course 2-3 more times before I understand the concepts completely; maybe it will click better. If you have any sections/suggestions of what to re-review to understand better why to use certain techniques over others or what certain techniques accomplish to a more layperson, I would be most appreciative.
Bms
June 1, 2023
There is a lot of useful information provided, along with resources to help you learn. However, the teaching style could use some improvement to make it more engaging. Additionally, if your goal is to create automatic models to execute long and short positions, this course may not be suitable as shorting is not covered in the financial engineering curriculum for some reason.
Nimesh
April 25, 2023
For now, everything is going well. I have done over half of the course. The way the instructor teaches is also extremely good. Can't wait to continue.
Josh
April 1, 2023
A really well designed and informative course. The instructor explains the concepts well and with good examples to help clarify harder concepts. It covers a huge breadth of financial topics and investment strategies with lots of applicable data science and AI skills. I'm really enjoying the course so far!
Sudhin
March 16, 2023
The course is not designed for working professionals. There is a lot of treasure hunt for getting the code or html version of it which is highly un professional. The instructor is just going over superficial for example time series he didnt explain properly the math of ACF and PACF why we do it. The HMM implementation of Viterbi algorithm is not shown properly. Optimisation of the portfolio is not done properly, there are other courses in udemy which is far superior to this not even premium. The instructor is old school need to improve a lot, he doesn't take feedback properly and shoot the messenger. I will never ever take a course from this instructor. Not worth for the premium.
Angelo
February 4, 2023
The instructor was is clear and concise of what is being taught. What he expects from his students. What you can expect from him.
Hkbu-Davidlo
January 3, 2023
Huge efforts are devoted in this course. It would be better if there are lectures that talk about derivatives, which are also a corner stone of financial engineering.
Felix
October 13, 2022
Altho the expectations of predicting the stock market were clarified at the beginning of the course, I learned what realistically ML could do in the financial field. I do not have a financial background so I found exciting fintech concepts.
Frederick
September 11, 2022
My rating and review only apply to the core portion of this course and not to the supplemental VIP sections, which include the subject of algorithmic trading. I cannot properly judge the practical efficacy of the lessons on building a trading bot until I actually build my own and test it in a live environment. Overall, this course did a lot to elevate my understanding of finances, which was my ultimate goal coming into it. Learning how to build a machine learning trading bot was just icing on the cake. It is math heavy, and the course summary has a lecture dedicated to explaining why that needs to be the case; thus, if you do not like math, this course is not for you. You're not going to become a financial expert after completion. There's no realistic way to cram a 4-year finance degree into a 20-hour course. However, you will have a much better footing when it comes to portfolio optimization -- knowing how to discern what makes a good or bad investment from the data. Some quick insights: - Don't waste your time trying to predict stock prices. - Don't ever short. - If you're not interested in the intricacies of a fine-tuned investment strategy, then just stick with buy-and-hold, and you'll likely still outperform the majority of active traders.

Coupons

DateDiscountStatus
9/8/202078% OFF
expired
10/18/202077% OFF
expired
11/9/202075% OFF
expired
1/13/202175% OFF
expired
2/12/202175% OFF
expired
4/8/202175% OFF
expired
4/15/202175% OFF
expired
5/17/202175% OFF
expired
7/19/202175% OFF
expired
11/14/202175% OFF
expired
12/29/202175% OFF
expired
1/22/202275% OFF
expired

Charts

Price

Financial Engineering and Artificial Intelligence in Python - Price chart

Rating

Financial Engineering and Artificial Intelligence in Python - Ratings chart

Enrollment distribution

Financial Engineering and Artificial Intelligence in Python - Distribution chart
3279504
udemy ID
6/29/2020
course created date
9/8/2020
course indexed date
Bot
course submited by