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Lazy Programmer Team and Lazy Programmer Inc.

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.

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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

Enroll now

What's inside

Learning objectives

  • 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
  • Show more
  • Show less

Syllabus

Welcome
Introduction and Outline
Scope of the course
How to Practice
Read more
Warmup (Optional)
Getting Set Up
Where to get the code, notebooks, and data
How to Succeed in This Course
Temporary 403 Errors
Financial Basics
Financial Basics Section Introduction
Getting Financial Data
Getting Financial Data (Code)
Understanding Financial Data
Understanding Financial Data (Code)
Dealing with Missing Data
Dealing with Missing Data (Code)
Returns
Adjusted Close, Stock Splits, and Dividends
Adjusted Close (Code)
Back to Returns (Code)
QQ-Plots
QQ-Plots (Code)
The t-Distribution
The t-Distribution (Code)
Skewness and Kurtosis
Confidence Intervals
Confidence Intervals (Code)
Statistical Testing
Statistical Testing (Code)
Covariance and Correlation
Covariance and Correlation (Code)
Alpha and Beta
Alpha and Beta (Code)
Mixture of Gaussians
Mixture of Gaussians (Code)
Volatility Clustering
Price Simulation
Price Simulation (Code)
Financial Basics Section Summary
Suggestion Box
Time Series Analysis
Time Series Analysis Section Introduction
Efficient Market Hypothesis
Random Walk Hypothesis
The Naive Forecast
Simple Moving Average (Theory)
Simple Moving Average (Code)
Exponentially-Weighted Moving Average (Theory)
Exponentially-Weighted Moving Average (Code)
Simple Exponential Smoothing for Forecasting (Theory)
Simple Exponential Smoothing for Forecasting (Code)
Holt's Linear Trend Model (Theory)
Holt's Linear Trend Model (Code)
Holt-Winters (Theory)
Holt-Winters (Code)
Autoregressive Models - AR(p)
Moving Average Models - MA(q)
ARIMA
ARIMA in Code (pt 1)
Stationarity
Stationarity Code
ACF (Autocorrelation Function)
PACF (Partial Autocorrelation Function)
ACF and PACF in Code (pt 1)
ACF and PACF in Code (pt 2)
Auto ARIMA and SARIMAX
Model Selection, AIC and BIC
ARIMA in Code (pt 2)
ARIMA in Code (pt 3)
ACF and PACF for Stock Returns
Forecasting
Time Series Analysis Section Conclusion
Portfolio Optimization and CAPM
Portfolio Optimization Section Introduction
The S&P500
What is Risk?
Why Diversify?
Describing a Portfolio (pt 1)
Describing a Portfolio (pt 2)
Visualizing Random Portfolios and Monte Carlo Simulation (pt 1)
Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)
Maximum and Minimum Portfolio Return
Maximum and Minimum Portfolio Return in Code
Mean-Variance Optimization
The Efficient Frontier
Mean-Variance Optimization And The Efficient Frontier in Code
Global Minimum Variance (GMV) Portfolio
Global Minimum Variance (GMV) Portfolio in Code
Sharpe Ratio
Maximum Sharpe Ratio in Code
Portfolio with a Risk-Free Asset and Tangency Portfolio
Risk-Free Asset and Tangency Portfolio in Code
Capital Asset Pricing Model (CAPM)
Problems with Markowitz Portfolio Theory and Robust Estimation
Portfolio Optimization Section Conclusion
VIP: Algorithmic Trading
Algorithmic Trading Section Introduction
Trend-Following Strategy
Trend-Following Strategy in Code (pt 1)

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches financial engineering with a machine learning twist, targeting individuals with an interest in both disciplines
Covers advanced topics like algorithmic trading, statistical factor models, and regime detection
Provides a solid foundation in financial engineering, with topics ranging from data analysis to portfolio optimization
Emphasizes the practical applications of machine learning in finance, including stock price prediction, risk management, and algorithmic trading
Instructors have a proven track record in machine learning and financial engineering, ensuring high-quality content
Requires prior knowledge in matrix arithmetic, probability, and Python coding, which may limit accessibility for beginners

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Financial Engineering and Artificial Intelligence in Python with these activities:
Simple Moving Averages
Reinforce the theoretical understanding of Simple Moving Averages by practicing and computing them for different time series datasets.
Browse courses on Time Series
Show steps
  • Select a time series dataset
  • Calculate the Simple Moving Average for different window sizes
  • Plot the time series and the corresponding Simple Moving Averages
Exponential Weighted Moving Average
Strengthen the understanding of Exponential Weighted Moving Average by implementing it and analyzing its impact on time series datasets.
Browse courses on Time Series
Show steps
  • Select a time series dataset
  • Calculate the Exponential Weighted Moving Average for different smoothing factors
  • Plot the time series and the corresponding Exponential Weighted Moving Averages
Holt-Winters Exponential Smoothing
Gain hands-on experience in applying Holt-Winters Exponential Smoothing to forecast time series data, enhancing the ability to make predictions.
Show steps
  • Select a time series dataset with trend and seasonality
  • Perform Holt-Winters Exponential Smoothing with different parameters
  • Evaluate the accuracy of the forecasts using appropriate metrics
Four other activities
Expand to see all activities and additional details
Show all seven activities
ARIMA Modeling
Develop proficiency in applying ARIMA models to time series data, enabling advanced forecasting and predictive analytics.
Browse courses on ARIMA
Show steps
  • Identify and understand the ARIMA model components
  • Build ARIMA models for different time series datasets
  • Evaluate the performance of ARIMA models using statistical metrics
Practice Statistical Modeling
Reinforce obtained knowledge of statistical distribution modeling by completing a series of practice questions and exercises.
Browse courses on Statistical Modeling
Show steps
  • Solve 10 practice questions on normal distribution.
  • Solve 10 practice questions on lognormal distribution.
  • Solve 5 practice questions on student's t-distribution.
  • Solve 5 practice questions on chi-square distribution.
Stock Portfolio Optimization Project
Deepen understanding of portfolio optimization by building a project that implements Modern Portfolio Theory and demonstrates the construction of efficient portfolios.
Browse courses on Portfolio Optimization
Show steps
  • Gather historical stock data
  • Calculate expected returns and covariance matrix
  • Implement mean-variance optimization to find efficient portfolios
  • Evaluate portfolio performance using risk and return metrics
  • Write a report summarizing the project and findings
Financial Engineering Whitepaper
Sharpen analytical and communication skills by authoring a whitepaper that explores the applications of financial engineering and machine learning in the finance industry.
Browse courses on Financial Engineering
Show steps
  • Research and identify a specific topic within financial engineering
  • Develop a thesis statement and outline
  • Write the whitepaper, including introduction, literature review, methodology, results, and conclusion
  • Proofread and edit the whitepaper

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