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30-Day Money-Back Guarantee

This course includes:

  • 20 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Financial Analysis

Financial Engineering and Artificial Intelligence in Python

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
Bestseller
Rating: 4.7 out of 54.7 (250 ratings)
1,911 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 12/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Forecasting stock prices and stock returns
  • Time series analysis
  • Holt-Winters exponential smoothing model
  • ARIMA
  • Efficient Market Hypothesis
  • Random Walk Hypothesis
  • Exploratory data analysis
  • Alpha and Beta
  • Distributions and correlations of stock returns
  • Modern portfolio theory
  • Mean-Variance Optimization
  • Efficient frontier, Sharpe ratio, Tangency portfolio
  • CAPM (Capital Asset Pricing Model)
  • Q-Learning for Algorithmic Trading

Course content

14 sections • 137 lectures • 20h 3m total length

  • Preview06:53
  • Where to get the code
    08:23
  • Preview03:29
  • How to Practice
    03:45
  • Warmup (Optional)
    04:09

  • 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
    03:03

  • 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)
    12:01
  • 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

  • Colab Notebooks
    00:00
  • Preview02:03

Requirements

  • Decent Python coding skills
  • Numpy, Matplotlib, Pandas, and Scipy (I teach this for free! My gift to the community)
  • Matrix arithmetic
  • Probability

Description

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)

Who this course is for:

  • Anyone who loves or wants to learn about financial engineering
  • Students and professionals who want to advance their career in finance or artificial intelligence and machine learning

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 39,208 Reviews
  • 144,572 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 105,600 Reviews
  • 417,831 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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