Financial Engineering and Artificial Intelligence in Python

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.

Suggested Prerequisites:

• Matrix arithmetic
• Probability
• Decent Python coding skills
• Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!)