Financial Engineering and Artificial Intelligence in PythonPython
Name / Engineering and Artificial
Created by / Lazy Programmer Inc.
Hours / 20
- Description Financial Engineering and Artificial Intelligence in Python - Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
Have you ever thought about what would happen if you combined the power of machine learning, artificial intelligence, and financial engineering?
Today you can stop fantasizing and get down to business.
This course will teach you the basics of financial engineering, with a touch of machine learning.
We will cover fundamental 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)
Additionally, we will look at various unconventional technologies from the field of machine learning and artificial intelligence only, 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'll see the biggest mistake of the past decade by marketers pretending to be "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their approach is fundamentally flawed and why their results are downright ridiculous. It is a lesson about how AI is not being applied in finance.
As the author of nearly 30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn't help but immerse myself in the vast and complex world. Financial engineering.
This course is for those who love finance or AI, and especially if you like both!
Whether you are a student, professional, or someone looking to advance their career, this course is for you.
Thanks for reading, I'll see you in class!
- Matrix calculation
- Probably that
- 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)