Financial Engineering and Artificial Intelligence in Python
What you'll 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
Requirements
- Decent Python coding skills
- Numpy, Matplotlib, Pandas, and Scipy (I teach this for free! My gift to the community)
- Matrix arithmetic
- Probability
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)
Who this course is for:
- Anyone who loves or wants to learn about financial engineering
- Students and professionals who want to advance their career in finance or artificial intelligence and machine learning
Instructors
- 4.6 Instructor Rating
- 50,641 Reviews
- 197,858 Students
- 16 Courses
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.
- 4.6 Instructor Rating
- 130,768 Reviews
- 491,479 Students
- 30 Courses
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.