Ai Finance

Financial Basics Section Introduction Preview 05:32

Getting Financial Data Preview 07:21

Getting Financial Data (Code) Preview 07:16

Understanding Financial Data Preview 05:05

Understanding Financial Data (Code) Preview 12:08

Dealing with Missing Data Preview 05:58

Dealing with Missing Data (Code) Preview 07:01

Returns Preview 09:15

Adjusted Close, Stock Splits, and Dividends Preview 11:30

Adjusted Close (Code) Preview 03:49

Back to Returns (Code) Preview 07:21

QQ-Plots Preview 05:29

QQ-Plots (Code) Preview 07:19

The t-Distribution Preview 03:55

The t-Distribution (Code) Preview 08:07

Skewness and Kurtosis Preview 07:34

Confidence Intervals Preview 10:28

Confidence Intervals (Code) Preview 02:16

Statistical Testing Preview 14:18

Statistical Testing (Code) Preview 07:08

Covariance and Correlation Preview 08:16

Covariance and Correlation (Code) Preview 05:56

Alpha and Beta Preview 06:55

Alpha and Beta (Code) Preview 08:09

Mixture of Gaussians Preview 06:41

Mixture of Gaussians (Code) Preview 06:13

Volatility Clustering Preview 03:03

Price Simulation Preview 03:04

Price Simulation (Code) Preview 02:34

Financial Basics Section Summary Preview 02:21

Suggestion Box Preview 03:03

Time Series Analysis Section Introduction Preview 06:52

Efficient Market Hypothesis Preview 11:17

Random Walk Hypothesis Preview 14:25

The Naive Forecast Preview 06:45

Simple Moving Average (Theory) Preview 04:17

Simple Moving Average (Code) Preview 08:41

Exponentially-Weighted Moving Average (Theory) Preview 11:07

Exponentially-Weighted Moving Average (Code) Preview 11:05

Simple Exponential Smoothing for Forecasting (Theory) Preview 10:13

Simple Exponential Smoothing for Forecasting (Code) Preview 10:24

Holt's Linear Trend Model (Theory) Preview 07:55

Holt's Linear Trend Model (Code) Preview 03:11

Holt-Winters (Theory) Preview 11:20

Holt-Winters (Code) Preview 08:00

Autoregressive Models - AR(p) Preview 12:51

Moving Average Models - MA(q) Preview 03:31

ARIMA Preview 10:45

ARIMA in Code (pt 1) Preview 20:25

Stationarity Preview 12:20

Stationarity Code Preview 09:50

ACF (Autocorrelation Function) Preview 10:10

PACF (Partial Autocorrelation Funtion) Preview 06:55

ACF and PACF in Code (pt 1) Preview 08:26

ACF and PACF in Code (pt 2) Preview 07:03

Auto ARIMA and SARIMAX Preview 09:41

Model Selection, AIC and BIC Preview 09:52

ARIMA in Code (pt 2) Preview 14:39

ARIMA in Code (pt 3) Preview 16:21

ACF and PACF for Stock Returns Preview 07:35

Forecasting Preview 09:14

Time Series Analysis Section Conclusion Preview 04:12

Portfolio Optimization Section Introduction Preview 03:35

The S&P500 Preview 02:46

What is Risk? Preview 07:03

Why Diversify? Preview 08:28

Describing a Portfolio (pt 1) Preview 09:51

Describing a Portfolio (pt 2) Preview 06:30

Visualizing Random Portfolios and Monte Carlo Simulation (pt 1) Preview 13:07

Visualizing Random Portfolios and Monte Carlo Simulation (pt 2) Preview 15:07

Maximum and Minimum Portfolio Return Preview 09:35

Maximum and Minimum Portfolio Return in Code Preview 04:59

Mean-Variance Optimization Preview 07:26

The Efficient Frontier Preview 07:23

Mean-Variance Optimization And The Efficient Frontier in Code Preview 09:13

Global Minimum Variance (GMV) Portfolio Preview 01:56

Global Minimum Variance (GMV) Portfolio in Code Preview 02:14

Sharpe Ratio Preview 08:01

Maximum Sharpe Ratio in Code Preview 06:35

Portfolio with a Risk-Free Asset and Tangency Portfolio Preview 09:52

Risk-Free Asset and Tangency Portfolio in Code Preview 02:16

Capital Asset Pricing Model (CAPM) Preview 12:26

Problems with Markowitz Portfolio Theory and Robust Estimation Preview 09:13

Portfolio Optimization Section Conclusion Preview 02:25

Algorithmic Trading Section Introduction Preview 02:55

Trend-Following Strategy Preview 13:14

Trend-Following Strategy in Code (pt 1) Preview 08:27

Trend-Following Strategy in Code (pt 2) Preview 09:38

Machine Learning-Based Trading Strategy Preview 07:53

Machine Learning-Based Trading Strategy in Code Preview 09:25

Classification-Based Trading Strategy in Code Preview 03:40

Using a Random Forest Classifier for Machine Learning-Based Trading Preview 05:00

Algorithmic Trading Section Summary Preview 05:56

Reinforcement Learning Section Introduction Preview 06:34

Elements of a Reinforcement Learning Problem Preview 20:18

States, Actions, Rewards, Policies Preview 09:24

Markov Decision Processes (MDPs) Preview 10:07

The Return Preview 04:56

Value Functions and the Bellman Equation Preview 09:53

What does it mean to “learn”? Preview 07:18

Solving the Bellman Equation with Reinforcement Learning (pt 1) Preview 09:49

Solving the Bellman Equation with Reinforcement Learning (pt 2) Preview 12:01

Epsilon-Greedy Preview 06:09

Q-Learning Preview 14:15

How to Learn Reinforcement Learning Preview 05:56

Trend-Following Strategy with Reinforcement Learning API Preview 12:33

Trend-Following Strategy Revisited (Code) Preview 09:14

Q-Learning in an Algorithmic Trading Context Preview 07:39

Representing States Preview 07:27

Q-Learning for Algorithmic Trading in Code Preview 15:33

Statistical Factor Models (Beginner) Preview 15:41

Statistical Factor Models (Intermediate) Preview 10:09

Statistical Factor Models (Advanced) Preview 19:50

Statistical Factor Models (Code) Preview 16:13

Why Sequence Models? (pt 1) Preview 14:06

Why Sequence Models? (pt 2) Preview 12:14

HMM Parameters Preview 09:26

HMM Tasks and the Viterbi Algorithm Preview 15:15

HMM for Modeling Volatility Clustering in Code Preview 18:38

Trading APIs and Deploying Your Strategy in the Real World Preview 05:53

High Frequency Trading (HFT) Preview 03:54

Colab Notebooks Preview 00:00

VIP: Finance Enthusiasts, Beware of Marketers! Preview 02:03

Anaconda Environment Setup Preview 20:20

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow Preview 17:30

How to Code by Yourself (part 1) Preview 15:54

How to Code by Yourself (part 2) Preview 09:23

Proof that using Jupyter Notebook is the same as not using it Preview 12:29

How to Succeed in this Course (Long Version) Preview 10:24

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? Preview 22:04

Machine Learning and AI Prerequisite Roadmap (pt 1) Preview 11:18

Machine Learning and AI Prerequisite Roadmap (pt 2) Preview 16:07

What is the Appendix? Preview 02:48

BONUS Lecture Preview 05:31