Financial Engineering and Artificial Intelligence in Python

Udemy Financial Engineering and Artificial Intelligence in Python

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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 (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!)
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
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TUTProfessor
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