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Financial Analysis
Financial Engineering and Artificial Intelligence in Python

Financial Engineering and Artificial Intelligence in Python

Loại khoá học: Finance

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!

image
50.000 VND
90 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

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)


UNIQUE FEATURES

  • Every line of code explained in detail - email me any time if you disagree

  • No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

  • Not afraid of university-level math - get important details about algorithms that other courses leave out

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Yêu cầu

Nội dung khoá học

16 sections

Welcome

4 lectures
Introduction and Outline
06:38
Scope of the course
03:48
How to Practice
05:39
Warmup (Optional)
04:46

Getting Set Up

3 lectures
Where to get the code, notebooks, and data
04:29
How to Succeed in This Course
03:04
Temporary 403 Errors
02:57

Financial Basics

31 lectures
Financial Basics Section Introduction
05:32
Getting Financial Data
07:21
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)
07:01
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)
07:08
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
02:21
Suggestion Box
03:10

Time Series Analysis

31 lectures
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 and CAPM

22 lectures
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

VIP: Algorithmic Trading

9 lectures
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

VIP: The Basics of Reinforcement Learning

12 lectures
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

VIP: Reinforcement Learning for Algorithmic Trading

5 lectures
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

VIP: Statistical Factor Models and Unsupervised Machine Learning

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

VIP: Regime Detection and Sequence Modeling with Hidden Markov Models

5 lectures
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

Course Summary and Common Questions

9 lectures
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

Extras

1 lectures
VIP: Finance Enthusiasts, Beware of Marketers!
02:03

Setting Up Your Environment FAQ

3 lectures
Pre-Installation Check
04:12
Anaconda Environment Setup
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:30

Extra Help With Python Coding for Beginners FAQ

5 lectures
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
Get Your Hands Dirty, Practical Coding Experience, Data Links
08:33
How to use Github & Extra Coding Tips (Optional)
11:12

Effective Learning Strategies for Machine Learning FAQ

4 lectures
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Machine Learning and AI Prerequisite Roadmap (pt 1)
11:18
Machine Learning and AI Prerequisite Roadmap (pt 2)
16:07

Appendix / FAQ Finale

2 lectures
What is the Appendix?
02:48
BONUS
05:48

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