Section 1 : Welcome

Lecture 1 Introduction and Outline 6:43
Lecture 2 Where to get the code 8:14
Lecture 3 Scope of the course 3:19
Lecture 4 How to Practice 3:35
Lecture 5 Warmup (Optional) 3:59

Section 2 : Financial Basics

Lecture 6 Financial Basics Section Introduction 5:23
Lecture 7 Getting Financial Data 7:11
Lecture 8 Getting Financial Data (Code) 7:6
Lecture 9 Understanding Financial Data 4:55
Lecture 10 Understanding Financial Data (Code) 11:58
Lecture 11 Dealing with Missing Data 5:48
Lecture 12 Dealing with Missing Data (Code) 6:51
Lecture 13 Returns 9:6
Lecture 14 Adjusted Close, Stock Splits, and Dividends 11:20
Lecture 15 Adjusted Close (Code) 3:39
Lecture 16 Back to Returns (Code)
Lecture 17 QQ-Plots
Lecture 18 QQ-Plots (Code) 7:9
Lecture 19 The t-Distribution 3:45
Lecture 20 The t-Distribution (Code) 7:58
Lecture 21 Skewness and Kurtosis 7:25
Lecture 22 Confidence Intervals 10:19
Lecture 23 Confidence Intervals (Code) 2:7
Lecture 24 Statistical Testing 14:8
Lecture 25 Statistical Testing (Code) 6:57
Lecture 26 Covariance and Correlation 8:6
Lecture 27 Covariance and Correlation (Code) 5:46
Lecture 28 Alpha and Beta 6:45
Lecture 29 Alpha and Beta (Code) 7:59
Lecture 30 Mixture of Gaussians 6:31
Lecture 31 Mixture of Gaussians (Code) 6:3
Lecture 32 Volatility Clustering 2:54
Lecture 33 Price Simulation 2:54
Lecture 34 Price Simulation (Code) 2:24
Lecture 35 Financial Basics Section Summary 2:11
Lecture 36 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 3 : Time Series Analysis

Lecture 37 Time Series Analysis Section Introduction 6:42
Lecture 38 Efficient Market Hypothesis 11:8
Lecture 39 Random Walk Hypothesis 14:15
Lecture 40 The Naive Forecast 6:35
Lecture 41 Simple Moving Average (Theory) 4:8
Lecture 42 Simple Moving Average (Code) 8:31
Lecture 43 Exponentially-Weighted Moving Average (Theory) 10:58
Lecture 44 Exponentially-Weighted Moving Average (Code) 10:54
Lecture 45 Simple Exponential Smoothing for Forecasting (Theory) 10:3
Lecture 46 Simple Exponential Smoothing for Forecasting (Code) 10:14
Lecture 47 Holt's Linear Trend Model (Theory) 7:46
Lecture 48 Holt's Linear Trend Model (Code) 3:1
Lecture 49 Holt-Winters (Theory) 11:10
Lecture 50 Holt-Winters (Code) 7:50
Lecture 51 Autoregressive Models - AR(p) 12:41
Lecture 52 Moving Average Models - MA(q) 3:22
Lecture 53 ARIMA 10:35
Lecture 54 ARIMA in Code (pt 1) 20:15
Lecture 55 Stationarity 12:11
Lecture 56 Stationarity Code 9:40
Lecture 57 ACF (Autocorrelation Function) 10:1
Lecture 58 PACF (Partial Autocorrelation Funtion) 6:45
Lecture 59 ACF and PACF in Code (pt 1) 8:16
Lecture 60 ACF and PACF in Code (pt 2) 6:53
Lecture 61 Auto ARIMA and SARIMAX 9:32
Lecture 62 Model Selection, AIC and BIC 9:43
Lecture 63 ARIMA in Code (pt 2) 14:29
Lecture 64 ARIMA in Code (pt 3) 16:11
Lecture 65 ACF and PACF for Stock Returns 7:25
Lecture 66 Forecasting 9:4
Lecture 67 Time Series Analysis Section Conclusion 4:3

Section 4 : Portfolio Optimization and CAPM

Lecture 68 Portfolio Optimization Section Introduction 3:25
Lecture 69 The S&P500 2:37
Lecture 70 What is Risk 6:53
Lecture 71 Why Diversify
Lecture 72 Describing a Portfolio (pt 1) 9:42
Lecture 73 Describing a Portfolio (pt 2) 6:21
Lecture 74 Visualizing Random Portfolios and Monte Carlo Simulation (pt 1) 12:58
Lecture 75 Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)
Lecture 76 Maximum and Minimum Portfolio Return 9:25
Lecture 77 Maximum and Minimum Portfolio Return in Code 4:49
Lecture 78 Mean-Variance Optimization 7:17
Lecture 79 The Efficient Frontier 7:14
Lecture 80 Mean-Variance Optimization And The Efficient Frontier in Code 9:4
Lecture 81 Global Minimum Variance (GMV) Portfolio 1:47
Lecture 82 Global Minimum Variance (GMV) Portfolio in Code 2:5
Lecture 83 Sharpe Ratio 7:52
Lecture 84 Maximum Sharpe Ratio in Code 6:25
Lecture 85 Portfolio with a Risk-Free Asset and Tangency Portfolio 9:42
Lecture 86 Risk-Free Asset and Tangency Portfolio in Code 2:6
Lecture 87 Capital Asset Pricing Model (CAPM) 12:17
Lecture 88 Problems with Markowitz Portfolio Theory and Robust Estimation 9:3
Lecture 89 Portfolio Optimization Section Conclusion 2:15

Section 5 : VIP Algorithmic Trading

Lecture 90 Algorithmic Trading Section Introduction 2:45
Lecture 91 Trend-Following Strategy 13:5
Lecture 92 Trend-Following Strategy in Code (pt 1) 8:17
Lecture 93 Trend-Following Strategy in Code (pt 2) 9:28
Lecture 94 Machine Learning-Based Trading Strategy 7:44
Lecture 95 Machine Learning-Based Trading Strategy in Code 9:15
Lecture 96 Classification-Based Trading Strategy in Code 3:30
Lecture 97 Using a Random Forest Classifier for Machine Learning-Based Trading 4:51
Lecture 98 Algorithmic Trading Section Summary 5:46

Section 6 : VIP The Basics of Reinforcement Learning

Lecture 99 Reinforcement Learning Section Introduction 6:24
Lecture 100 Elements of a Reinforcement Learning Problem 20:8
Lecture 101 States, Actions, Rewards, Policies 9:14
Lecture 102 Markov Decision Processes (MDPs) 9:57
Lecture 103 The Return 4:46
Lecture 104 Value Functions and the Bellman Equation 9:43
Lecture 105 What does it mean to “learn” 7:8
Lecture 106 Solving the Bellman Equation with Reinforcement Learning (pt 1) 9:38
Lecture 107 Solving the Bellman Equation with Reinforcement Learning (pt 2) 11:51
Lecture 108 Epsilon-Greedy 5:59
Lecture 109 Q-Learning 14:6
Lecture 110 How to Learn Reinforcement Learning 5:46

Section 7 : VIP Reinforcement Learning for Algorithmic Trading

Lecture 111 Trend-Following Strategy with Reinforcement Learning API 12:23
Lecture 112 Trend-Following Strategy Revisited (Code) 9:4
Lecture 113 Q-Learning in an Algorithmic Trading Context
Lecture 114 Representing States 7:17
Lecture 115 Q-Learning for Algorithmic Trading in Code 15:23

Section 8 : VIP Statistical Factor Models and Unsupervised Machine Learning

Lecture 116 Statistical Factor Models (Beginner) 15:31
Lecture 117 Statistical Factor Models (Intermediate) 10:0
Lecture 118 Statistical Factor Models (Advanced) 19:40
Lecture 119 Statistical Factor Models (Code) 16:4

Section 9 : VIP Regime Detection and Sequence Modeling with Hidden Markov Models

Lecture 120 Why Sequence Models (pt 1) 13:57
Lecture 121 Why Sequence Models (pt 2) 12:4
Lecture 122 HMM Parameters 9:16
Lecture 123 HMM Tasks and the Viterbi Algorithm 15:6
Lecture 124 HMM for Modeling Volatility Clustering in Code 18:29

Section 10 : Extras

Lecture 125 About Proctor Testing Pdf
Lecture 126 About Certification Pdf

Section 11 : Setting Up Your Environment FAQ

Lecture 127 Windows-Focused Environment Setup 2018 20:13
Lecture 128 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:30

Section 12 : Extra Help With Python Coding for Beginners FAQ

Lecture 129 How to Code by Yourself (part 1) 15:49
Lecture 130 How to Code by Yourself (part 2) 9:23
Lecture 131 Proof that using Jupyter Notebook is the same as not using it 12:24

Section 13 : Effective Learning Strategies for Machine Learning FAQ

Lecture 132 How to Succeed in this Course (Long Version) 10:17
Lecture 133 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 21:57
Lecture 134 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:14
Lecture 135 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:7

Section 14 : Appendix FAQ Finale

Lecture 136 What is the Appendix 2:41