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:

In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

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:

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

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

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!)

- Introduction and Outline (06:39) (FREE preview available)
- Scope of the course (03:47)
- How to Practice (03:45)
- Warmup (Optional) (04:46)

- Where to get the code and data - instant access (01:32)
- How to use Github & Extra Coding Tips (Optional) (11:12)

- Financial Basics Section Introduction (05:32)
- Getting Financial Data (07:20)
- Getting Financial Data (Code) (07:16)
- Understanding Financial Data (05:05)
- Understanding Financial Data (Code) (12:08)
- Dealing with Missing Data (05:58)
- Dealing with Missing Data (Code) (07:00)
- Returns (09:15)
- Adjusted Close, Stock Splits, and Dividends (11:30)
- Adjusted Close (Code) (03:49)
- Back to Returns (Code) (07:21)
- QQ-Plots (05:29)
- QQ-Plots (Code) (07:19)
- The t-Distribution (03:55)
- The t-Distribution (Code) (08:07)
- Skewness and Kurtosis (07:34)
- Confidence Intervals (10:28)
- Confidence Intervals (Code) (02:16)
- Statistical Testing (14:18)
- Statistical Testing (Code) (07:07)
- Covariance and Correlation (08:16)
- Covariance and Correlation (Code) (05:56)
- Alpha and Beta (06:55)
- Alpha and Beta (Code) (08:09)
- Mixture of Gaussians (06:41)
- Mixture of Gaussians (Code) (06:13)
- Volatility Clustering (03:03)
- Price Simulation (03:04)
- Price Simulation (Code) (02:34)
- Financial Basics Section Summary (02:20)
- Suggestion Box (03:10)

- Time Series Analysis Section Introduction (06:52)
- Efficient Market Hypothesis (11:17)
- Random Walk Hypothesis (14:25)
- The Naive Forecast (06:45)
- Simple Moving Average (Theory) (04:17)
- Simple Moving Average (Code) (08:41)
- Exponentially-Weighted Moving Average (Theory) (11:07)
- Exponentially-Weighted Moving Average (Code) (11:05)
- Simple Exponential Smoothing for Forecasting (Theory) (10:13)
- Simple Exponential Smoothing for Forecasting (Code) (10:24)
- Holt's Linear Trend Model (Theory) (07:55)
- Holt's Linear Trend Model (Code) (03:11)
- Holt-Winters (Theory) (11:20)
- Holt-Winters (Code) (08:00)
- Autoregressive Models - AR(p) (12:51)
- Moving Average Models - MA(q) (03:31)
- ARIMA (10:45)
- ARIMA in Code (pt 1) (20:25)
- Stationarity (12:20)
- Stationarity Code (09:50)
- ACF (Autocorrelation Function) (10:10)
- PACF (Partial Autocorrelation Funtion) (06:55)
- ACF and PACF in Code (pt 1) (08:26)
- ACF and PACF in Code (pt 2) (07:03)
- Auto ARIMA and SARIMAX (09:41)
- Model Selection, AIC and BIC (09:52)
- ARIMA in Code (pt 2) (14:39)
- ARIMA in Code (pt 3) (16:21)
- ACF and PACF for Stock Returns (07:35)
- Forecasting (09:14)
- Time Series Analysis Section Conclusion (04:12)

- Portfolio Optimization Section Introduction (03:35)
- The S&P500 (02:46)
- What is Risk? (07:03)
- Why Diversify? (08:28)
- Describing a Portfolio (pt 1) (09:51)
- Describing a Portfolio (pt 2) (06:30)
- Visualizing Random Portfolios and Monte Carlo Simulation (pt 1) (13:07)
- Visualizing Random Portfolios and Monte Carlo Simulation (pt 2) (15:07)
- Maximum and Minimum Portfolio Return (09:35)
- Maximum and Minimum Portfolio Return in Code (04:59)
- Mean-Variance Optimization (07:26)
- The Efficient Frontier (07:23)
- Mean-Variance Optimization And The Efficient Frontier in Code (09:13)
- Global Minimum Variance (GMV) Portfolio (01:56)
- Global Minimum Variance (GMV) Portfolio in Code (02:14)
- Sharpe Ratio (08:01)
- Maximum Sharpe Ratio in Code (06:35)
- Portfolio with a Risk-Free Asset and Tangency Portfolio (09:52)
- Risk-Free Asset and Tangency Portfolio in Code (02:16)
- Capital Asset Pricing Model (CAPM) (12:26)
- Problems with Markowitz Portfolio Theory and Robust Estimation (09:13)
- Portfolio Optimization Section Conclusion (02:25)

- Algorithmic Trading Section Introduction (02:55)
- Trend-Following Strategy (13:14)
- Trend-Following Strategy in Code (pt 1) (08:27)
- Trend-Following Strategy in Code (pt 2) (09:38)
- Machine Learning-Based Trading Strategy (07:53)
- Machine Learning-Based Trading Strategy in Code (09:25)
- Classification-Based Trading Strategy in Code (03:40)
- Using a Random Forest Classifier for Machine Learning-Based Trading (05:00)
- Algorithmic Trading Section Summary (05:56)

- Reinforcement Learning Section Introduction (06:34)
- Elements of a Reinforcement Learning Problem (20:18)
- States, Actions, Rewards, Policies (09:24)
- Markov Decision Processes (MDPs) (10:07)
- The Return (04:56)
- Value Functions and the Bellman Equation (09:53)
- What does it mean to “learn”? (07:18)
- Solving the Bellman Equation with Reinforcement Learning (pt 1) (09:49)
- Solving the Bellman Equation with Reinforcement Learning (pt 2) (12:04)
- Epsilon-Greedy (06:09)
- Q-Learning (14:15)
- How to Learn Reinforcement Learning (05:56)

- Trend-Following Strategy with Reinforcement Learning API (12:33)
- Trend-Following Strategy Revisited (Code) (09:14)
- Q-Learning in an Algorithmic Trading Context (07:39)
- Representing States (07:27)
- Q-Learning for Algorithmic Trading in Code (15:33)

- Statistical Factor Models (Beginner) (15:41)
- Statistical Factor Models (Intermediate) (10:09)
- Statistical Factor Models (Advanced) (19:50)
- Statistical Factor Models (Code) (16:13)

- Why Sequence Models? (pt 1) (14:06)
- Why Sequence Models? (pt 2) (12:14)
- HMM Parameters (09:26)
- HMM Tasks and the Viterbi Algorithm (15:15)
- HMM for Modeling Volatility Clustering in Code (18:38)

- Final Thoughts and Course Summary (06:10)
- Creating Your Personalized Trading Strategy (14:01)
- Applying This Course (08:30)
- Trading APIs and Deploying Your Strategy in the Real World (05:53)
- High Frequency Trading (HFT) (03:54)
- The Importance of Data (09:14)
- Why do I have to learn statistics to learn finance? (10:37)
- Get a Plug-and-Play Trading Bot Without Math (05:11)
- Slippage and Bid-Ask Spread (03:16)
- Eliminating the Naive Mindset (pt 1) (16:01)
- Eliminating the Naive Mindset (pt 2) (15:13)
- VIP: Finance Enthusiasts, Beware of Marketers! (02:03)

- Windows-Focused Environment Setup 2018 (20:21)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

- Beginner's Coding Tips (13:22)
- How to Code Yourself (part 1) (15:55)
- How to Code Yourself (part 2) (09:24)
- Proof that using Jupyter Notebook is the same as not using it (12:29)
- Python 2 vs Python 3 (04:38)
- Is Theano Dead? (10:04)

- How to Succeed in this Course (Long Version) (10:25)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
- What order should I take your courses in? (part 1) (11:19)
- What order should I take your courses in? (part 2) (16:07)

- What is the Appendix? (02:48)
- Where to get discount coupons and FREE deep learning material (05:31)