Financial Engineering and Artificial Intelligence in Python

  • Welcome
  • Introduction and Outline
Introduction and Outline
06:39
Scope of the course
03:48
How to Practice
05:39
Warmup (Optional)
04:46
Where to get the code
02:06
Temporary 403 Errors
02:58
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
Optimal Portfolio Closed-Form Solution
20:11
Optimal Portfolio Closed-Form Solution in Code
05:38
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
Predicting A Recession (Demo Walkthrough)
13:05
Predicting A Recession Discussion
02:40
Let's Beat the Naive Forecast
13:25
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
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, 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
How to use Github & Extra Coding Tips (Optional)
11:12
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
Where to get discount coupons and FREE deep learning material
05:49
Introduction and Outline
06:39
Scope of the course
03:48
How to Practice
05:39
Warmup (Optional)
04:46
Where to get the code
02:06
Temporary 403 Errors
02:58
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
Optimal Portfolio Closed-Form Solution
20:11
Optimal Portfolio Closed-Form Solution in Code
05:38
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
Predicting A Recession (Demo Walkthrough)
13:05
Predicting A Recession Discussion
02:40
Let's Beat the Naive Forecast
13:25
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
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, 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
How to use Github & Extra Coding Tips (Optional)
11:12
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
Where to get discount coupons and FREE deep learning material
05:49

Not getting something? Want to ask a question? Visit the course Q&A

Github link Back to main course page and lecture list
Start learning today

Join 30 day bootcamp for free

4.7/5 from — 600k+ learners