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Financial Engineering and Artificial Intelligence in Python - AD-TEAM - 07-07-2025
![[Image: 4f64aa509c09094ee14a77dac100676b.jpg]](https://i125.fastpic.org/big/2025/0707/6b/4f64aa509c09094ee14a77dac100676b.jpg)
Financial Engineering and Artificial Intelligence in Python
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 20h 3m | 6.09 GB
Created by Lazy Programmer Team
Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
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
Statistical Factor Models
Regime Detection with Hidden Markov Models
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
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!)
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
More Info
![[Image: 58QF06XI_o.jpg]](https://images2.imgbox.com/23/c0/58QF06XI_o.jpg)
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