Financial Engineering and Artificial Intelligence in Python from Udemy

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

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
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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
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Show less

Syllabus

Welcome

Introduction and Outline

Scope of the course

How to Practice

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

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

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Reinforce the theoretical understanding of Simple Moving Averages by practicing and computing them for different time series datasets.

Browse courses on Time Series

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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

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Strengthen the understanding of Exponential Weighted Moving Average by implementing it and analyzing its impact on time series datasets.

Browse courses on Time Series

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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

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Gain hands-on experience in applying Holt-Winters Exponential Smoothing to forecast time series data, enhancing the ability to make predictions.

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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

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ARIMA Modeling

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Develop proficiency in applying ARIMA models to time series data, enabling advanced forecasting and predictive analytics.

Browse courses on ARIMA

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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

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Reinforce obtained knowledge of statistical distribution modeling by completing a series of practice questions and exercises.

Browse courses on Statistical Modeling

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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

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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

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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

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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

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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

Career center

Learners who complete Financial Engineering and Artificial Intelligence in Python will develop knowledge and skills that may be useful to these careers:

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Financial Engineering and Artificial Intelligence in Python

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