Financial Engineering and Artificial Intelligence in Python

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.