4.8 out of 5
4.8
570 reviews on Udemy

Financial Engineering and Artificial Intelligence in Python

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
Instructor:
Lazy Programmer Team
3,244 students enrolled
English [Auto]
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

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!)

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)

Welcome

1
Introduction and Outline
2
Where to get the code
3
Scope of the course
4
How to Practice
5
Warmup (Optional)

Financial Basics

1
Financial Basics Section Introduction
2
Getting Financial Data
3
Getting Financial Data (Code)
4
Understanding Financial Data
5
Understanding Financial Data (Code)
6
Dealing with Missing Data
7
Dealing with Missing Data (Code)
8
Returns
9
Adjusted Close, Stock Splits, and Dividends
10
Adjusted Close (Code)
11
Back to Returns (Code)
12
QQ-Plots
13
QQ-Plots (Code)
14
The t-Distribution
15
The t-Distribution (Code)
16
Skewness and Kurtosis
17
Confidence Intervals
18
Confidence Intervals (Code)
19
Statistical Testing
20
Statistical Testing (Code)
21
Covariance and Correlation
22
Covariance and Correlation (Code)
23
Alpha and Beta
24
Alpha and Beta (Code)
25
Mixture of Gaussians
26
Mixture of Gaussians (Code)
27
Volatility Clustering
28
Price Simulation
29
Price Simulation (Code)
30
Financial Basics Section Summary
31
Suggestion Box

Time Series Analysis

1
Time Series Analysis Section Introduction
2
Efficient Market Hypothesis
3
Random Walk Hypothesis
4
The Naive Forecast
5
Simple Moving Average (Theory)
6
Simple Moving Average (Code)
7
Exponentially-Weighted Moving Average (Theory)
8
Exponentially-Weighted Moving Average (Code)
9
Simple Exponential Smoothing for Forecasting (Theory)
10
Simple Exponential Smoothing for Forecasting (Code)
11
Holt's Linear Trend Model (Theory)
12
Holt's Linear Trend Model (Code)
13
Holt-Winters (Theory)
14
Holt-Winters (Code)
15
Autoregressive Models - AR(p)
16
Moving Average Models - MA(q)
17
ARIMA
18
ARIMA in Code (pt 1)
19
Stationarity
20
Stationarity Code
21
ACF (Autocorrelation Function)
22
PACF (Partial Autocorrelation Funtion)
23
ACF and PACF in Code (pt 1)
24
ACF and PACF in Code (pt 2)
25
Auto ARIMA and SARIMAX
26
Model Selection, AIC and BIC
27
ARIMA in Code (pt 2)
28
ARIMA in Code (pt 3)
29
ACF and PACF for Stock Returns
30
Forecasting
31
Time Series Analysis Section Conclusion

Portfolio Optimization and CAPM

1
Portfolio Optimization Section Introduction
2
The S&P500
3
What is Risk?
4
Why Diversify?
5
Describing a Portfolio (pt 1)
6
Describing a Portfolio (pt 2)
7
Visualizing Random Portfolios and Monte Carlo Simulation (pt 1)
8
Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)
9
Maximum and Minimum Portfolio Return
10
Maximum and Minimum Portfolio Return in Code
11
Mean-Variance Optimization
12
The Efficient Frontier
13
Mean-Variance Optimization And The Efficient Frontier in Code
14
Global Minimum Variance (GMV) Portfolio
15
Global Minimum Variance (GMV) Portfolio in Code
16
Sharpe Ratio
17
Maximum Sharpe Ratio in Code
18
Portfolio with a Risk-Free Asset and Tangency Portfolio
19
Risk-Free Asset and Tangency Portfolio in Code
20
Capital Asset Pricing Model (CAPM)
21
Problems with Markowitz Portfolio Theory and Robust Estimation
22
Portfolio Optimization Section Conclusion

VIP: Algorithmic Trading

1
Algorithmic Trading Section Introduction
2
Trend-Following Strategy
3
Trend-Following Strategy in Code (pt 1)
4
Trend-Following Strategy in Code (pt 2)
5
Machine Learning-Based Trading Strategy
6
Machine Learning-Based Trading Strategy in Code
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20 hours on-demand video
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Certificate of Completion
Financial Engineering and Artificial Intelligence in Python
Price:
$218.98

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